Volume-5 Issue-1

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Volume-5 Issue-1, March 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Vasudeva G, Cyril Prasanna Raj P

Paper Title:

Study of 8 Bits Fast Multipliers for Low Power Applications

Abstract: High–speed multiplication has always been a fundamental requirement of high performance processors and systems. With MOS scaling and technological advances there is a need for design and development of high speed data path operators such as adders and multipliers to perform signal processing operations at very high speed supporting higher data rates. In Digital signal Processing applications, multiplication is one of the most utilized arithmetic operations as part of filters, convolves and transforms processors. It is found in the literature that improving multipliers design directly benefits the high performance embedded processors used in consumer and industrial electronic products. Also significant increase in the bit length increases the critical path affecting the frequency of operations. It is also found that the regular structure required for each processing elements also increases and hence consumes area and power. Hence there is a need for design and development of high-speed architectures for N-bit multipliers supporting high speed and power. In this paper we review the architecture reported in the literature for multipliers and critical issues degrading the speed and power. Based on the literature review suitable modifications are suggested in the design for high speed and low power multipliers. The multipliers Booth, Wallace tree and Dadda are implemented and the constraints Area, Power and Timing are optimized using software resources NC SIM and VC SIM.

Keywords:
DSP, microprocessor, NC SIM, VC SIM


References:

1. Keshab K.Parhi, “VLSI DIGITAL SIGNAL PROCESSING SYSTEMS”, Design and implementation John Wiley and sons (ASIA), 1999.
2. Neil H.E.Weste, David Harris, Ayan Banerjee, “CMOS VLSI DESIGN”, A circuits and systems perspective. Pearson Education, Third edition 2007, pp345-356.

3. Robert F.Shaw, “Arithmetic operations in a binary computer”, Review of scientific instruments; vol 21, pp 687-693, 1950.

4. O.L.Mac Sorley,”High-Speed Arithmetic in Binary Computers”, Proceedings of the IRE, Vol 49, pp.67-91, 1961.

5. Bruce Gilchrist, J.H. Pomerene and S.Y.Wong, “Fast Carry logic for Digital Computers”, IRE Transactions on Electronic Computers, Vol.EC-4, PP.133-136, 1955.

6. R. De Mori, “Suggestions for an TC Fast Parallel Multiplier”, Electronics letters, Vol.5, pp 50-51,1965

7. K’Adrea C.Bickerstaff,Michael J.Schulte,and Earl E. Swartz Lander, Jr., “Reduced Area Multipliers”, Proceedings of the 1993 International Conference on
Application Specific Array Processors, pp.478-489,1993.

8. Andrew D.Booth, “A Signed Binary Multiplication Technique”, Quarterly Journal of Mechanics and Applied Mathematics, Vol .4, pp.236-240, 1951.

9. Charles R. Baugh and Bruce .A.Wooley, “A Two’s Complement Parallel Array Multiplication Algorithm”, IEEE Transactions on Computers, Vol. C-22, pp.1045-47,
1973.

10. Behrooz Parhami, “Computer Arithmetic: Algorithms and Hardware Designs, Newyork: Oxford University press, 2000.

11. Thomas K.Callaway and Earl E. Swatzlander,JR., “Optimizing Multipliers for WSI”, Proceedings of the 1993 International Conference on Wafer Scale Integration, pp.85-94,1993.

12. C.S.Wallace,” A suggestion for a Fast Multiplier”, IEE Transactions on Electronic Computers, Vol.EC-13, pp.14-17, 1964.

13. Luigi Dadda,” Some Schemes for Parallel Multipliers”, Alta Frequenza, Vol.34, pp.349-356, August 1965.
14. Advanced Asic Chip Synthesis by Himanshu Bhatnagar Second Edition pp-183-256, 2002.
15. Wey C.L and Chang T.Y., “Design and analysis of VLSI-based parallel multipliers”, IEEE proceedings computers and Digital Techniques, vol.137, no.4,pp,328-336, July 1990.(Journal paper)

 

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2.

Authors:

Nandan Makarand Deval

Paper Title:

Secure Steganography Algorithm Based on Cellular Automata using Fibonacci Representation and Reverse Circle Cipher Application for Steganography

Abstract: Steganography is the act of hiding a message inside another message in such a way that can only be detected by its intended recipient .The process of hiding information inside another media is called steganography .In this technique the basic idea of steganography based on cellular automata using Fibonacci representation. The pixels color component is decompose into Fibonacci domain to extent more available bit-planes which can be used for data hiding for encryption we use reverse circle Cipher .This uses circular substitution and reversal transposition to exploit the benefits of both confusion and diffusion. With the help of these techniques we enhance the capacity of data hiding within image and security.

Keywords:
Steganography, cellular automata, Fibonacci Representation, encryption, Cipher


References:

1. . Fridrich, M. Goljan, and R. Du, “Detection of LSB steganography in color and grayscale images,” Magazine of IEEE Multimedia Special Issue on Security, pp. 22-28, October 2001.
2. S. Dumitrescu, X. Wu and Z. Wang, “Detection of LSB steganography via sample pair analysis”, IEEE Transactions on Signal Processing, vol. 51, no. 7, pp.
1995-2007, July 2003.

3. A secure steganographic algorithm based on Cellular Automata using Fibonacci representation Tuan Duc Nguyen Department of Computer Science Faculty of Science, Khon Kaen University Khon Kaen, Thailand

4. Somjit Arch-int Department of Computer ScienceFaculty of Science, Khon Kaen University Khon Kaen, Thailand june 2013

5. Bruce Schneier, “Applied Cryptography – Protocols, Algorithms, and Source Code in C”, John Wiley and Sons Inc. Second Edition. pp. 12-30.

6. Matt Bishop, “Computer Security: Art and Science”, Pearson Education, pp. 270-300, 2005. William Stallings, “Cryptography and Network Security: Principles and Practices” Fourth Edition, Pearson Education, pp. 30-150, April 2006.

7. Yee Wei Law, Jeroen Doumen, and Pieter Hartel. Survey and Benchmark of Block Ciphers for Wireless Sensor Networks. Transactions on Sensor Networks (TOSN). ACM February 2006

8. Reverse Circle Cipher for Personal and Network Security Ebenezer R.H.P. Isaac, Joseph H.R. Isaac and J. VisumathiJeppiaar Engineering College Chennai, Tamil Nadu, India

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3.

Authors:

Alyaa Hussein Ali, Shahad Imad Abdulsalam, Ihssan Subhi Nema

Paper Title:

Detection and Segmentation of Hemorrhage Stroke using Textural Analysis on Brain CT Images

Abstract: The detection of brain strokes from Computed Tomography CT images needs convenient processing techniques starting from image enhancement to qualify the brain image by isolation process, region growing and logical operators (OR and AND). These methods with the help of the simplest segmentation process, which is the thresholding process, are used to extract a stroke region from the CT image of the brain. The median filter is applied to remove the noise from the image. The statistical features calculated using first-order histogram were utilized in the detection of the stroke region.

Keywords:
Hemorrhage stroke; CT scan image; Brain segmentation; statistical features.


References:

1. M. M. Kyaw, ”Computer-Aided Detection system for Hemorrhage contained region”, International Journal of Computational Science and Information Technology, Vol. 1, February 2013, No. 1, PP. 11-16.
2. T. Gong, “Classification of CT Brain Images of Head Trauma”, Springer-Verlag Berlin Heidelberg, 2007, PP. 401-408,.

3. M. Chawla, “A method for automatic detection and classification of stroke from brain CT images”, 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2009.

4. V. Nagalkar and S. Agrawal, “Ischemic Stroke Detection Using Digital Image Processing by Fuzzy Methods”, International Journal of Latest Research in Science and Technology, Vol. 1, November-December 2012, PP. 345-347,

5. O.E. Ramos and B. Rezaei, “Scene Segmentation and Interpretation Image Segmentation using Region Growing”, MSc, Thesis, Computer Vision and Robotics, Universitat de Girona, 2010.

6. N. Aggarwal and R.K. Agrawal ,” First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images”, Journal of Signal and Information Processing, Vol. 3, 2012, PP. 146-153.

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4.

Authors:

Chathuri Gunathunga, K. P. Hewagmage

Paper Title:

Implementation of Integrated Virtual Learning Environment Model for Schools with Limited Resources for Online Learning

Abstract: With the advancement of Information Communication Technology in Sri Lanka teachers should take advantage there to upgrade their teaching technique. Students should be allowed to learn any time anywhere and at their own place. Learning Management System (LMS) plays an important role in ICT enabled learning environment in academic institutes. In K12 education, schoolnet is used to connect all secondary educational institutes and learning communities in a country since they follow the national curriculum. However, a single LMS hosted in the schoolnet network cannot integrate all similar learning communities identified with respect to each school, according to our evaluation of schoolnet LMS in Sri Lanka. After gathering and analyzing different requirements of stakeholders, we propose a suitable hierarchical model to integrate school level LMSs to create a loosely couple distributed learning environment. This model facilitates learners to explore the learning space starting from the classroom based interaction and it promotes the collaborative learning of other teachers and students irrespective of their physical location. In a prototype development, we have implemented suitable software architecture for the proposed model using Moodle LMS. It was designed considering the real world K12 educational administration in Sri Lanka. We also present a methodology to extend a LMS to a Virtual Learning Environment (VLE) which contains learning resources and activities using the model which implemented.

Keywords:
Learning Management System (LMS), e-Learning, K12 education, blended learning, ICT enabled learning, Moodle, Virtual Learning Environment


References:

1. McCluskey, A 2004 “Schooling: a Sustainable Learning Organisation?,” ERNIST project available at http://www.esode.com/downloadable%20files/ERNIST%20Project/schooling.pdf [last accessed 15th May 2011]
2. SEIR*TEC 2007 “Factors that Affect the Effective Use of Technology for Teaching and Learning Lessons Learned from the SEIR*TEC Intensive Site Schools,” SouthEast Initiatives Regional Technology in Education Consortium, 2007. http://www.seirtec.org/publications/lessons.pdf [last accessed 15th May 2011]

3. Johnston, J and Barker, LT 2002 “Assessing the Impact of Technology in Teaching and Learning, A Sourcebook for Evaluators,” Institute for Social Research, University of Michigan, [Online]. Available: http://www.rcgd.isr.umich.edu/tlt/TechSbk.pdf [Accessed May 15th 2011]

4. Priyashantha,W.C.P, and Ajith Pasqual , Impact on Learning and Teaching. e-Asia, Digital Learning Conference, December 2009, Colombo, Sri Lanka

5. K. P. Hewagamage, S. C. Premaratne and K. H. R. A. Peiris. “Design and Development ofBlended Learning through LMS”, 6th International Conference in Web-based Learning (ICWL 2007), August 2007, Edinburgh, United Kingdom

6. C. Paul Newhouse, “A Framework to Articulate the Impact of ICT on Learning in Schools” in The Impact of ICT on Learning and Teaching, Western Australian Department of Education, 2002, http://www.det.wa.edu.au/education/cmis/eval/downloads/pd/impactframe.pdf [Accessed May 15th 2011]

 

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5.

Authors:

Satveer Kaur, Jagpal Singh Ubhi

Paper Title:

A Simplified Approach to Analyze Routing Protocols in Dynamic MANET Environment

Abstract: The fundamental characteristic which differentiates MANETs from other wireless or wired network is mobility and node density. Mobile Wireless Ad Hoc Networks (MANET) is a network without infrastructure, where every node functions as transmitter, router and data sink. Therefore, MANET routing protocols are designed to adaptively cater for dynamic changes in topology while maximizing throughput and packet delivery ratio, and minimizing delay, aggregate good put, average jitter and minimum packet loss. In this paper, the MANET is implemented by using Ad Hoc Demand Vector (AODV), Dynamic Source Routing (DSR), and Dynamic MANET on Demand (DYMO), Optimized Link State Routing (OLSR) and Zone Routing Protocol (ZRP) and simulated on QualNet5.0 simulator. The effect of mobility and density of nodes changing in MANET is investigated and compared a number of reactive, hybrid and proactive routing protocols including AODV, DSR, DYMO, OLSR and ZRP. The simulative study on MANET routing protocols aims to determine the performance of current MANET routing protocols with respect to mobility and node density factors. Results vary when we change the node density. The results of this network are tabulated along with a comprehensive analysis which compares throughput, packet delivery ratio, end to end delay, aggregate good put, average jitter value and packet dropping with node density.

Keywords:
MANET, QualNet5.0, AODV, DSR, DYMO, OLSR and ZRP.


References:

1. Fahim Mann and Nauman Mazhar. MANET Routing Protocols vs Mobility Models: A Performance Evaluation. Third International Conference on ICUFN, Pages: 179-184, 2011.
2. N. Adam, M.Y. Ismail and J. Abdullah. Effect of Node Density on Performances of Three MANET Routing Protocols. International Conference on Electronic Devices, Systems and Applications (ICEDSA2010), Pages: 321-325, 2010.

3. Liu Tie-yuan, CHANG Liang and Gu Tian-long. Analyzing the Impact of Entity Mobility Models on the Performance of Routing Protocols in the MANET. Third International Conference on Genetic and Evolutionary Computing, Pages: 56-59, 2009.

4. N. Aschenbruck, E. G. Padilla, M. Gerharz, M. Frank and P. Matrini. Modelling mobility in disaster area scenarios. In Proceedings of MSWiM07, October 2007.

5. Ashish Shrestha and Firat Tekiner. Investigation of MANET Routing Protocols for Mobility and Scalability. International Conference on Parallel and Distributed Computing , Applications and Technologies, Pages:451-456, 2010.

6. Mamoun Hussein Mamon. Important Characteristic of Differences between DSR and AODV Routing Protocol. MCN 2007 Conference.

7. Bu Sung Lee, Mai Ngoc San, Teck Meng Lim, Chai Kiat Yeo and Boon Chong Seet. Processing Delay as a New Metric for On –Demand Mobile Ad Hoc. International Conference on WiCOM, Pages: 1-4, 2007.

8. David B. Johson, David A. Maltz and Yih-Chun Hu. The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks. Internet-Draft, draft-ietf-manet-dsr-10.txt.

9. Samir R. Das, Charles E. Perkins and Elizabeth M. Royer. Performance Comparison of Two On-Demand Routing Protocols for Ad Hoc Networks. IEEE Journal, USA, 2008.

10. R.E. Thorup. Implementing and evaluating the DYMO routing protocol. Master Thesis at Department of Computer Science, University of Aarhus, Denmark, 2007.

11. Josh Broach, David A. Maltz, David B. Johson, Yih-Chun Hu & Jorjeta Jetcheva. A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols. Proc. of Mobicom. Texas, 1998.

12. T. Clausen and P. Jacquet. Optimized Link State Routing Protocol (OLSR), RFC 3626, October, 2003.

13. G. Lin, G. Noubir and R. Rajaraman. Mobility Models for ad hoc network simulation. International Journal of Computer Science and Network Security, 7(6):160-164, 2007.

14. N. Aschenbruck, E. G. Padilla and P. Matrini. A survey on mobility models for performance analysis in tactical mobile networks. Journal of Telecommunications and Information Technology, 2: Pages:54-61, 2008.

15. I. Awan and K. Al-Begain. Performance evaluation of wireless networks. International Journal of Wireless Information Networks, 13(2): Pages: 95-97, 2006.

 

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6.

Authors:

B. Sankara Babu, K. Rajasekhar Rao, P. Satheesh

Paper Title:

An Advanced Precision based Approach to String Transformation

Abstract: Distinct obstacles occur in Natural language processing, Knowledge Engineering, Information Retrieval, Genetics Informatics, Computational molecular biology and Data Mining concerned to String Transformation. Consider an input string, the system automatically produces top k output strings referring to input string. Generally people perform various kinds of spelling errors such as misspell words accidentally while surfing the web. To circumvent such errors, this Paper propounds an advanced Precision based approach to string transformation which is very accurate. The proposed system comprises unique precision value allocated to each alphabet and these are aggregated to give the Total Precision of the particular word. Data sets are trained with the precision based approach by validating them to dictionary called the database. Misspell word precision is compared with the data sets precision and retrieves the top k nearest neighbour output strings relevant to input string. This is one of the best accurate Misspell word and sentence correction approach and experimentally proven on large data sets.

Keywords:
String Transformation, Precision based Approach, Misspell words, Total Precision.


References:

1. Wagner, Robert A., and Roy Lowrance. “An extension of the string-to-string correction problem.” Journal of the ACM (JACM) 22.2 (1975): 177-183.
2. Masek, William J., and Michael S. Paterson. “A faster algorithm computing string edit distances.” Journal of Computer and System sciences 20.1 (1980): 18-31.

3. Marzal, Andres, and Enrique Vidal. “Computation of normalized edit distance and applications.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 15.9 (1993): 926-932.

4. Elmi, Mohammad Ali, and Martha Evens. “Spelling correction using context.”Proceedings of the 17th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 1998.

5. Brill, Eric, and Robert C. Moore. “An improved error model for noisy channel spelling correction.” Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2000.

6. Cormode, Graham, and S. Muthukrishnan. “The string edit distance matching problem with moves.” ACM Transactions on Algorithms (TALG) 3.1 (2007): 2.

7. Arasu, Arvind, Surajit Chaudhuri, and Raghav Kaushik. “Transformation-based framework for record matching.” Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on. IEEE, 2008.

8. Okazaki, Naoaki, et al. “A discriminative candidate generator for string transformations.” Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2008.

9. Greenhill, Simon J. “Levenshtein distances fail to identify language relationships accurately.” Computational Linguistics 37.4 (2011): 689-698.

10. Kumar, Akshay. “A Log Linear Probabilistic Model for String Transformation Using Non Dictionary Approach.” International Journal of Innovative Research and Development 3.5 (2014).

11. Jeyalakshmi.S et al. “Improving Efficiency and Accuracy in String Transformation on Large Data Sets.”International Journal of Computer Science and Mobile Applications Vol.2 Issue.3, March-2014, pg.55-65 ISSN:2321-8363

12. Malarvizhi, Mrs P., and Mrs S. Mohana. “A Survey on Various Candidate Generator Methods for Efficient String Transformation.”

13. Yi, Xing, Henry Feild, and James Allan. “Spelling Correction Based on User Search Contextual Analysis and Domain Knowledge.”

14. Angel Wills and D.F.Jenolin Flora. “Approach For Query Reformulation Using Log Linear Model.”International Journal of Research in Engineering and Bioscience

 

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7.

Authors:

D. Y. Patil

Paper Title:

Infrastructure Development in the Border and Non-Border Districts of Punjab

Abstract: Infrastructure Development is regarded as a prerequisite for rapid transformation of an economy. Some regions on account of their location disadvantages face some inherent problems regarding development. The present study is an effort to compare the infrastructure development in the border and non-border districts of Punjab. The study compares the infrastructural development in terms of health, education, economic, physical and social sector parameters. The study covers time period from 2002 to 2012. The study revealed that the with the passage of time, the gulf between border and non-border districts with respect to infrastructural development instead of narrowing down, appears to have widened further.

Keywords:
Infrastructure Development, rapid transformation, border and non-border.


References:

1. Hirschman, Albert (1958): The Strategy of Economic Development, New Hanes: Yale University Press, p.83.
2. Nurkse, Ragnar (1980): Problems of Capital Formation in Underdeveloped Countries, (Delhi: Oxford University Press), p.152.

3. Planning Commission, (2004): Punjab Development Report, Govt of India, New-Delhi, p.596.

 

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8.

Authors:

Simon Nderitu Watuthu, Michael Kimwele, George Okeyo

Paper Title:

The Key Issues Surrounding Electronic Commerce Information Security Management

Abstract: The purpose of this study was to identify the key issues surrounding electronic commerce information security management. A descriptive survey research design was conducted to gather primary data. Information about the current status of ecommerce information security practices and the impediments of these approaches was also collected. A structured questionnaire was used to collect secondary data. Once all the instruments were collected, they were validated edited and then coded. In the validation process, the collected instruments were checked to determine whether an acceptable sample was obtained in terms of proportion of the issued instrument. Descriptive statistics such as frequency distribution, percentages, means and standard deviations were calculated. This was facilitated by use of the statistical package for social science (S.P.S.S). The observations made from this study are that In Kenya, ecommerce faces numerous information security challenges. Confidentiality and Privacy issues was the top security issue of concern to the respondent’s with 60.7% of the respondents admitting to it. Respondents further considered viruses and malicious software at 46.4%, human errors at 28.6% and also system or software errors at 17.9% as the top three main causes of confidential threat their organizations. Further the study revealed 85.7% of the respondents admitted that their organisation did not use any framework in managing information security.

Keywords:
electronic commerce, information security ecommerce security


References:

1. Arabjafari, M. (2012). Security in E-commerce. Available at:
2. http://www.slideshare.net/mohsinq1/security-for-e-commerce? related=1

3. Ciampo, M. (2012). Security & Guide to Network Security Fundamental, International Edition.Mexico: Cengage Learning

4. Curtis, G. & Cobham.D. (2005). Business Information System: Analysis, design and practice Fifth edition. England: Pearson Education Limited Edinburgh hate Harlow.

5. Hoodrick, M.T. &Temassia, R. (2011).Introduction to Computer Security. Delhi : Pearson Education

6. Horak, R. (2002). Communications Systems and Networks, 3rd Edition. New Delhi: Wiley Dreamtech India (P) Ltd

7. Norman, A. A.&Yasin, N.M. (2011).An Analysis of Information Systems Security Management (ISSM): The Hierarchical Organizations vs. Emergent Organization. International Journal of Digital Society (IJDS), (2010). 1(3)

8. Patil, J. (2008). Information Security Framework: Case Study of a Manufacturing Organization.Seleanu, D. (2013).Cyber security in Canada: Finance industry, government seek ways toshare Data. Available at: http://blogs.reuters.com/financial-regulatory-forum/2013/07/18/cybersecurity-in-canada-finance-industry-government-seek-ways-to-share-data/

9. Trepper, C. (2000). E-commerce Strategies.Asoke K Ghosh, Prentice Hall of India Private Limited, Connaught Circus, New Delhi.

10. Ward T. (2010). Strategies for Reducing the Risk of eCommerce Fraud.First Data

11. Whitman M. E. &Mettord H. J. (2012).Principles of Information Security, 4th Edition.Course technology, Cengage learning

 

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9.

Authors:

José M. Reyes Álamo, Aparicio Carranza

Paper Title:

A Cloud Robotics Architecture with Applications to Smart Homes

Abstract: Cloud computing is a computational model in which interconnected computers over the Internet work together toward offering greater processing power and storage capabilities than stand-alone solutions. The use of the cloud has found application in a diversity of fields including robotics and mobile computing. This has resulted in the emergence of areas like cloud robotics, a paradigm where robots rely on the cloud to perform their heavy computations and for their storage needs while focusing on simpler computation tasks. The mix of mobile devices and the cloud has created the field of mobile cloud computing (MCC) where mobile devices like smartphones and tablets focus on data gathering and simple processing tasks while using the cloud for complex computations and greater storage. In this paper we review several mobile cloud robotics architectures that combine these concepts. We provide a background of the different technologies used to develop these solutions. We present a prototype implementation of one of the architectural models and also show some practical applications of it using a Smart Home environment as an example

Keywords: Cloud computing, cloud robotics, mobile cloud, smart home.

References:

1. Y. Chen, Z. Du, and M. García-Acosta, “Robot as a Service in Cloud Computing,” in 2010 Fifth IEEE International Symposium on Service Oriented System Engineering (SOSE), 2010, pp. 151–158.
2. G. Hu, W.-P. Tay, and Y. Wen, “Cloud robotics: architecture, challenges and applications,” IEEE Netw., vol. 26, no. 3, pp. 21–28, 2012.

3. H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches,” Wirel. Commun. Mob. Comput., p. n/a–n/a, 2011.

4. M. W. Lew, T. B. Horton, and M. S. Sherriff, “Using LEGO MINDSTORMS NXT and LEJOS in an Advanced Software Engineering Course,” in 2010 23rd IEEE Conference on Software Engineering Education and Training (CSEE T), 2010, pp. 121–128.

5. R. U. Pedersen, J. Nørbjerg, and M. P. Scholz, “Embedded programming education with Lego Mindstorms NXT using Java (leJOS), Eclipse (XPairtise), and Python (PyMite),” in Proceedings of the 2009 Workshop on Embedded Systems Education, New York, NY, USA, 2009, pp. 50–55.

6. “LeJOS, Java for Lego Mindstorms.” [Online]. Available: http://www.lejos.org/. [Accessed: 22-Oct-2013].

7. “The Jython Project,” Nov-2013. [Online]. Available: http://www.jython.org/. [Accessed: 11-Nov-2013].

8. “PyMite: python-on-a-chip,” 2013. [Online]. Available: http://code.google.com/p/python-on-a-chip/. [Accessed: 11-Nov-2013].

9. “nxt-python – A pure-python driver/interface/wrapper for the Lego Mindstorms NXT robot. – Google Project Hosting.” [Online]. Available: http://code.google.com/p/nxt-python/. [Accessed: 11-Nov-2013].

10. J. D. Brock and R. F. Bruce, “Sensing the World with a Raspberry Pi,” J Comput Sci Coll, vol. 30, no. 2, pp. 174–175, Dec. 2014.

11. S. Greenberg and C. Fitchett, “Phidgets: Easy Development of Physical Interfaces Through Physical Widgets,” in Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, New York, NY, USA, 2001, pp. 209–218.

12. J. Cappos, I. Beschastnikh, A. Krishnamurthy, and T. Anderson, “Seattle: a platform for educational cloud computing,” in Proceedings of the 40th ACM technical symposium on Computer science education, New York, NY, USA, 2009, pp. 111–115.

13. “Seattle.” [Online]. Available: https://seattle.poly.edu/html/. [Accessed: 23-Oct-2013].

14. J. M. Reyes Álamo, M. Benito, and A. Carranza, “Towards An Architecture for Mobile Cloud Robotics,” in IHART, Las Vegas, NV, 2013, vol. 31, pp. 391–398.

15. N. Noury, G. Virone, P. Barralon, J. Ye, V. Rialle, and J. Demongeot, “New trends in health smart homes,” in Enterprise Networking and Computing in Healthcare Industry, 2003. Healthcom 2003. Proceedings. 5th International Workshop on, 2003, pp. 118–127.

 

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10.

Authors:

Vikal R. Ingle, V. T. Ingole

Paper Title:

Performance Test of Power Transformer Prior to Maintenance Using DGA and Grey Relational Analysis

Abstract: The insulation of power transformer i.e. oil and paper decomposition recognized by means of dissolved gas-in-oil analysis (DGA). To detect incipient faults in a transformer, standard key gas method of DGA is employed on the basis of quantity of gases released from the oil. This primary information also reflects the overall condition of a transformer. In this paper, condition assessment of power transformer using relative scaling is discussed. Grey relational analysis is identified as best option for relative scaling, wherein the data of fleet connected transformers is compared and accordingly scales them on the strength of score. Grey relational analysis on key gas sample determines the Target Heart Degrees (THD) of a specific transformer. However, THD represent the average estimation of bull’s eye coefficients, calculated by means of attributes with equal weight condition. Subsections linearity relations are utilized to decide seven intervals for ranking purpose. Linear regression demonstrated on subsection linearity relations for different sets of key gas samples. Result shows the dominance of proposed model in deciding the maintenance priorities.

Keywords:
DGA, Key gas method, Grey Relational Analysis, Target Heart Degree, Rank Approaching Degree, subsections linearity relation.


References:

1. Heywood R.J., Emsley A.M., and Ali M. “Degradation of cellulosic insulation in power transformers”. Part I: Factors affecting the measurement of the average visco-metric degree of polymerization of new and aged electrical papers‟, IEE Proc., Sci., Meas. Technol., 2000, 147, (2), pp.86-90
2. Emsley A.M., Xiao, X., Heywood R.J., and Ali, M.: Degradation of cellulosic insulation in power transformers. Part 2: Formation of furan products in insulating oil‟, IEE Proc. Sci., Meas. Technol., 2000, 147, (3), pp. 110-114

3. Emsley, A.M., Xiao X., Heywood, R.J., and Ali, M. “Degradation of cellulosic insulation in power transformers. Part 3: Effects of oxygen and water on ageing in oil”, IEE Proc., Sci., Meas. Technol., 2000, 147, (3), pp. 115-120

4. Emsley A.M., Xiao, X., Heywood R.J., and Ali, M.: and Xiao, X., “Degradation of cellulosic insulation in power transformers. Part 4: Effects of ageing on the tensile strength of paper”, IEE Proc., Sci., Meas. Technol., 2000, 147, (6), pp. 285-290

5. Hossam A. Nabwy, E. A. Rady, A. M. Kozae, A. N. Ebady, “Fault Diagnosis of Power Transformer Based on Fuzzy Logic, Rough Set theory and Inclusion Degree Theory”, The Online Journal on Power and Energy Engineering(OJPEE) Vol.(1)-No.(2),Reference Number: W09-0011

6. Arjan van Schijndel, “Power Transformer Reliability Modelling” PhD Thesis, 2010 ,A catalogue record is available from the Eindhoven University of Technology Library ISBN: 978-90-386-2282-8

7. Technical Manual, Department of the Army TM 5-686“Power Transformer Maintenance and Acceptance Testing”, 16 November 1998

8. Eilert Bjerkan, “High Frequency Modeling Of Power Transformers Stresses And Diagnostics” PhD Thesis, NTNU Norwegian University of Science and Technology

9. Wang, M., A.J. Vandermaar, and K.D. Srivastava, “Review of Condition Assessment of Power Transformers In Service”, in IEEE Electrical Insulation Magazine. 2002. p. 12-25.

10. Dr S R Kannan FIET, “Condition Assessment of Power Transformers Status & Challenges”, Third International Conference on Power systems IIT Kharagpur December 27-29, 2009

11. Jianpo Li, Xiaojuan Chen, Chunming Wu; Information Engineering College Northeast Dianli University Jilin, China “Power Transformer State Assessment Based on Grey Target Theory” 2009 International Conference on Measuring Technology and Mechatronics Automation, IEEE Computer Society, ICMTMA-2009, pp. 664-667.

12. IEC 60599, “Mineral Oil-Impregnated Electrical Equipment in service-Guide to the Interpretation of Dissolved and free Gases Analysis”, Edition 2, 1999

13. “IEEE Guide for the interpretation of gases generated in oil immersed transformers”, IEEE Engg.soc., ANSI/IEEE std.C57.104-1991

14. A.N.Jahromi, R. Peircy, S. Cress,J.R.R.Service and W.Fan, “An Approach to power Transformer Asset Management using Health Index,”IEEE Electrical Insulation Magazine,Vol.25,No.2, 2009,pp.20-34

15. Naderian, S. Cress and R. Peircy, “An Approach to determine the Health Index of Power Transformers”, Proc. IEEE Int. sump. Electrical Insulation, Vancouver, Cananday, June2008, pp.192-96

16. Deng, J.L. “The Primary Methods of Grey System Theory,” Huazhong University of Science and Technology Press, Wuhan (2005).

17. Lu, M.;Wevers, K., “Application of grey relational analysis for evaluating road traffic safety measures: advanced driver assistance systems against infrastructure redesign”, Intelligent Transport Systems, IET Volume 1,Issue: 1,2007,pp. 3-14

18. Y.Kuo,T.Yang,and G.W.Huang, “The use of grey relational analysis in solving multiple attributes decision-making problems”, Computer & Industrial Engineering, Vol.55, 2008, pp80-93

19. Sikun Yang, School of Electrical Engineering, Beijing Jiaotong University, “Application of Grey Target Theory for Handling Multi-criteria Vague Decision Making Problems”,2008 ISECS International Colloquium on Computing, Communication, Control and Management, 3-4 Aug. 2008, pp 467-471.

20. Jiang Wei, Liang Jiarong, and Jiang Jianbing, “Multi-Objective Vague decision making based on gray connection analysis”, Computer Engineering and Applications, Vol.43,No.18, 2007, pp. 171-173.

21. S .F. Liu, Y. Lin, Grey Information Theory and Practical Applications, Springer-Verlag, London, 2006

 

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11.

Authors:

Vipin Kumar, Anupama Sharma, C.B.Gupta

Paper Title:

A Deterministic Inventory Model For Weibull Deteriorating Items with Selling Price Dependent Demand And Parabolic Time Varying Holding Cost

Abstract: This paper with development of an inventory model when deterioration rate follows Weibull two way parameter distributions. It is assumed that demand rate is function of selling price and holding cost is parabolic in terms of time. In this models both the cases with shortage and without shortage are taken into consideration. Whenever shortage allowed is completely backlogged. To illustrate the result numerical examples are given .the sensitive analysis for the model has been performed to study the effect of changes the value of parameters associated with the model. Mathematics Subject Classification: – 90B05

Keywords:
EOQ model, deteriorating items, Weibull distribution, shortage, price dependent demand, parabolic holding cost.


References:

1. Harris FW (1915) Operations and cost. A. W, Shaw Company, Chicago
2. Wilson RH (1934) A scientific routine for stock control. Harv Bus Rev 13:116–128

3. Whitin TM (1957) The theory of inventory management, 2nd edition. Princeton University Press, Princeton

4. Ghare PM, Schrader GF (1963) A model for an exponentially decaying inventory.

5. Dave U, Patel LK (1981) (T, Si) policy inventory model for deteriorating items with time proportional demand. Journal of Operational Research Society 32:137–142

6. Chung KJ, Ting PS (1993) A heuristic for replenishment for deteriorating items with a linear trend in demand. Journal of Operational Research Society 44:1235–1241

7. Wee HM (1995) A deterministic lot-size inventory model for deteriorating items with shortages and a declining market. Computational Operation research
22:345–356

8. Abad PL (1996) Optimal pricing and lot-sizing under conditions of perishability and partial backordering. Manage Sci 42:1093–1104 Abad PL (2001) optimal price and order-size for a reseller under partial backlogging. Computational Operation research 28:53–65

9. Chang HJ, Dye CY (1999) An EOQ model for deteriorating items with time varying demand and partial backlogging Journal of Operational Research Society 50:1176–1182

10. Goyal SK, Giri BC (2001) Recent trends in modeling of deteriorating inventory. European Journal of Operation Research 134:1–16

11. Ouyang W, Cheng X (2005) An inventory model for deteriorating items with exponential declining demand and partial backlogging. Yugoslav Journal of Operation Research15 (2):277–288

12. Alamri AA, Balkhi ZT (2007) The effects of learning and forgetting on the optimal production lot size for deteriorating items with time varying demand and deterioration rates. International Journal of Production Economics 107:125–138

13. Dye CY, Ouyang LY, Hsieh TP (2007) Deterministic inventory model for deteriorating items with capacity constraint and time-proportional backlogging rate. European Journal of Operation Research 178(3):789–807

14. Roy A (2008) An inventory model for deteriorating items with price dependent demand and time varying holding cost. Adv Modeling Opt 10:25–37

15. Liao JJ (2008) An EOQ model with non-instantaneous receipt and exponentialdeteriorating item under two-level trade credit International Journal of Production Economics 113:852–861

16. Skouri K, Konstantaras I, Papachristos S, Ganas I (2009) Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate.European Journal of Operation Research 192:79–92

17. Mandal B (2010) An EOQ inventory model for Weibull distributed deteriorating items under ramp type demand and shortages. Opsearch 47(2):158–165

18. Mishra VK, Singh LS (2010) Deteriorating inventory model with time dependent demand and partial backlogging. Applied Mathematical Sciences 4(72):3611–3619

19. Tripathy CK, Mishra U[2010] An inventory model for Weibull deteriorating items with price dependent demand and time varying holding cost.

20. Hung K-C (2011) An inventory model with generalized type demand, deterioration and backorder rates. European Journal of Operation Research 208(3):239–242

21. Mishra VK, Singh LS (2011a) Inventory model for ramp type demand, timedependent deteriorating items with salvage value and shortages. International Journal of mathematics and statistics 23(D11):84–91

 

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12.

Authors:

D. J. Evanjaline, P. Rajakumar, N. Kalpana

Paper Title:

Two Tier Security Enhancement for Wireless Protocol WEP (Wired Equivalent Privacy)

Abstract: A Wireless Local Area Network (WLAN) is a flexible data communication system implemented as an extension to or as an alternative for a wired Local Area Network (LAN). However, anyone can eavesdrop on information so that WLAN has the hidden security trouble such as leaking of electromagnetic wave or eavesdropping of data because WLAN adopts common electromagnetic wave as media to transmit data. Therefore, the security of WLAN is very important and outstanding. In IEEE 802.11, there are three security technologies used to ensure the data security in WLAN—SSID (Service Set Identifier), MAC (Media Access Control), WEP (Wired Equivalent Privacy). The proposed work falls on the third technology namely the WEP protocol. WEP suffered threats of attacks from hackers owing to certain security shortcomings in the WEP protocol. The proposed schemes implemented in two different layers of WLAN network architecture to strengthen the security of WLAN against the key stream reuse attacks and weak IV attacks.

Keywords:
WLAN, WEP, Initialization Vector.


References:

1. Mohamed Juwaini, Raed Alsaqour, Maha Abdelhaq , “A Review On WEP Wireless Security Protocol,” Journal of Theoretical and Applied Information Technology, ISSN: 1992-8645, E-ISSN: 1817-3195 Vol. 40 No.1, 15 June 2012.
2. Dr.R.N. Rajotiya, Pridhi Arora, “Enhancing Security of WI-FI Network”, International Journal of Computer Applications, ISSN: 2250 – 1797, Issue 2, Vol 3, June 2012.

3. Arash Habibi Lashkari,Farnaz Towhidi,Raheleh Sadat Hosseini.Wired Equivalent Privacy (WEP). International Conference on Future Computer and Communication, IEEE, 2009.

4. Lashkari, A.H.; Mansoor, M.; Danesh, A.S.; Wired Equivalent Privacy (WEP) versus Wi-Fi Protected Access (WPA). International Conference on Signal
Processing System. IEEE, 2009

5. Lashkari, A.H., A survey on wireless security Protocols (WEP, WPA and WPA2/802.11i), Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on 8-11 Aug 2009, E-ISBN: 978-1- 4244-3878-5

6. Sandirigama, M, Security weaknesses of WEP Protocol IEEE 802.11b and enhancing the Security with dynamic keys, Science and Technology for Humanity (TIC-STH), 2009, IEEE Toronto International Conference

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13.

Authors:

Chandan Tamrakar, Chinmay Chandrakar, Monisha Sharma

Paper Title:

Detection of Rpeak Index and Characterization of QRS Complex of the ECG Signal using Virtual Instruments of Lab VIEW

Abstract: In the ECG signals P, QRS and T waves play an essential role. Various features of these waves provide significant information to diagnose most of the cardiac diseases after preprocessing of the ECG signal. In various features, RR-interval, QRS Duration, and QRS sample Characteristic are the feature, which reveals significant information about the physiological conditions of the patient. In the previous work to find the RR-interval Discrete Wavelet Transform (DWT) technique and by applying a thresholding to peak detection method has been used. The proposed work is totally digital system based to for detection of consecutive Rpeaks in time domain and in the form of sample index finally the RR-interval has been calculated with the help of Waveform Min Max VI and Search Waveform VI of LabVIEW. In the previous work to detect QRS characteristics LabVIEW mathscript tool and simple moving average filter etc. method has been used. This paper deals with a resourceful composite system which has been proposed for detection of Rpeak Index and QRS Duration. In the proposed work QRS characteristics has been extracted from Extract Portion of the Signal VI of LabVIEW for the standard MIT-BIH arrhythmia database. LabVIEW 2013 version provided by National Instruments has been used here to design the feature extractor

Keywords: Biomedical Signal, Detrending, Denoising, ECG, Feature extraction, LabVIEW, MIT-BIH arrhythmia database, RR-interval, Wavelet Analysis.

References:

1. Jiapu Pan and willis j. Tompkins, “A Real-Time QRS Detection Algorithm” IEEE transactions on biomedical engineering, vol. BME-32, no. 3, pp. 230-236, march 1985.
2. Cuiwei Li, Chongxun Zheng, and Changfeng Tai “Detection of ECG Characteristic Points Using Wavelet Transforms” IEEE Transactions on Biomedical
Engineering, Vol. 42, No. 1, January 1995 pp.21-28

3. S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348,2005

4. Szi-Wen Chena, Hsiao-Chen Chena, Hsiao-Lung Chanb “A real-time QRS detection method based on moving averaging incorporating with wavelet denoising” computer methods and programs in biomedicine 82 (2006) 187–195.

5. Natalia M. Arzeno, Zhi-De Deng, and Chi-Sang Poon, Analysis of First-Derivative Based QRS Detection Algorithms IEEE Transactions on Biomedical Engineering, Vol. 55, No. 2, February 2008, pp.478-484

6. Channappa Bhyri, Kalpana.V, S.T.Hamde, and L.M.Waghmare “Estimation of ECG features using LabVIEW”, TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 2, NO. 1, pp. 320-324, July 2009

7. Faezipour, Student, Adnan Saeed, Suma Chandrika Bulusu,Mehrdad Nourani, Hlaing Minn, and Lakshman Tamil, “A Patient-Adaptive Profiling Schemefor ECG Beat Classification”, transactions on information technology in biomedicine, vol. 14, no. 5, pp. 1153-1165, September 2010.

8. M. K. Islam, A. N. M. M. Haque, G. Tangim, T. Ahammad, and M. R. H. Khondokar, (2012). “Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools”, International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, pp. 404-408 June.

9. Noack, R. Poll, W.-J. Fischer, S. Zaunseder, “QRS Pattern Recognition Using a Simple Clustering Approach for Continuous Data” 2013 IEEE XXXIII International Scientific Conference Electronics and Nanotechnology (ELNANO), pp. 228-232

10. Swati Banerjee and M. Mitra, (2013). “ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier”, J Med Eng. Technol, 37(4).pp.264–27

11. http://www.physionet.org/physiotools/wag/wag.pdf

12. http://www.physionet.org/cgi-bin/atm/ATM?database=mitdb&tool =plot_waveforms

 

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14.

Authors:

Kushal Dinkar Badgujar, Woldesemayat Muluneh Lemma

Paper Title:

Adaptive Neuro-Fuzzy Controller for High Temperature Gas Cooled Reactor

Abstract: A Neuro-Fuzzy controller is applied to control the power of a high temperature pebble bed reactor (HTPBR). A simplified model of the reactor and lumped model of heat transfer is developed and used. Xenon feedback with Xenon and Iodine balance equations and feedback with power coefficient of reactivity are included. The inputs to controller are represented using seven fuzzy sets. The output is obtained as linear combinations of the inputs. Simulations were conducted for the case of reducing the reactor power from rated value at 100% to 20% and for the case of raising reactor power from 20% to 100% linearly. In these simulations, the proposed design for controller exhibits faster and more accurate response than conventional controller.

Keywords:
ANFIS controller, Fuzzy logic, GEN IV reactors, Reactor power control.


References:

1. D. Ruan. (1995 Aug.). Fuzzy logic in the nuclear research world. Fuzzy Sets and Systems, 74 (1), pp. 5-13. http://www.sciencedirect.com/science/article/pii/016501149500020L
2. D. Ruan. (1998 Mar.). Intelligent systems in nuclear applications. Int. J. of Intelligent Systems, 13 (2/3), pp. 115-125. http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1098- 111X(199802/03)13:2/3%3C115::AID-INT2%3E3.0.CO;2-2/abstract

3. F. Adda, C. Larbes, M. Allek, M. Loudn, 2005. Design of an intelligent fuzzy logic controller for a nuclear research reactor. Progress in Nuclear Energy, 46 (3/4), pp. 328-347. http://www.sciencedirect.com/science/article/pii/S0149197005000296

4. C. Liu, J. Peng, F. Zhao, C. Li. (2009, Nov.).Design and optimization of fuzzy-PID controller for the nuclear reactor power control. Nucl. Eng. Des., 239 (11), pp. 2311–2316. http://www.sciencedirect.com/science/article/pii/S0029549309003215

5. P. Tiwari, B. Bandyopadhyay, G. Govindarajan, (1996, Aug.).Spatial control of a large pressurized heavy water reactor. IEEE Trans. Nucl. Sci., 43(4), pp. 2440-2453. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=531794

6. H.M. Emara, A. Elsadat, A.Bahgat, and M. Sultan, (2002, May), “Power stabilization of nuclear research reactor via fuzzy controllers”, Proceedings of the American Control. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1025469

7. P.S. Londhe, B.M. Patre, A.P. Tiwari. (2014, Jul.). Fuzzy-like PD controller for spatial control of advanced heavy water reactor. Nucl. Eng. Des., 274, pp. 77–89. ttp://www.sciencedirect.com/science/article/pii/S0029549314002362#

8. X.K. Wang, X.H. Yang, G. Liu, H. Qian. (2009, Jul.), “Adaptive neuro-fuzzy inference system pid controller for sg water level of nuclear power plant”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5212517

9. M. N. Khajavi, M. B. Menhaj, A.A. Suratgar, “Fuzzy adaptive robust optimal controller to increase load following capability of nuclear reactors”, International Conference on Power System Technology PowerCon 2000. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=900041

10. K.D. Badgujar, O.P. Singh, S. Singh, S. T. Revankar. (2012, Jul.), “Power Coefficient of Reactivity Determination for HTPBR and Its Application for Reactivity Initiated Transients”, Proceedings of the 20th ICONE POWER2012, ICONE20POWER2012-55058. [Online]. Available: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1762248

11. K. D. Badgujar, S. T. Revankar, J. C. Lee, M. H. Kim. (2012, Oct.) , “Design of fuzzy-PID controller for high temperature pebble bed reactor”, Trans. of the Korean Nuclear Society,[Autumn Meeting Gyeongju,Gyeongsang,Korea].[Online].Available: http://www.kns.org/kns_files/kns/file/30Kushal.pdf

12. K. D. Badgujar, S. T. Revankar, (2013, Jul.), “Design of fuzzy-pid controller for hydrogen production using HTPBR”, Proceedings of the 21st ICONE POWER2013, ICONE21-15037. [Online].Available: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1829534

13. K. D. Badgujar, “Studies on Dynamics of High Temperature Pebble Bed Reactor”, M.S. thesis, NETP, IIT Kanpur, Uttar Pradesh, India (April 2009).

14. Li, H., Gatland, H., (1996, Oct.).Conventional fuzzy control and its enhancements. IEEE Trans. Syst. Man Cybern, B 26 (5), pp.791–797. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=537321

 

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15.

Authors:

Sujatha K, Gunasekaran M

Paper Title:

Qualitative and Quantitative Approaches in Dynamics of Two Different Prey-Predator Systems

Abstract: This paper describes the dynamical behavior of two different systems consisting of two preys and a predator. It also deals with the stability of tri-species community in the systems by means of both qualitative and quantitative approaches. The existence and local stability of the equilibrium points of the systems were analyzed. Harvesting activity in both prey and predator populations plays a significant role in controlling the spread of disease.

Keywords:
Prey-Predator system, Qualitative stability, Iteration matrix, Quantitative stability, Harvesting Activity.


References:

1. Ashby .W. R.(1952). Design for a Brain. Chapman & hall, London. Revised edition 1960.
2. C.S. Holling, The functional response of predator to prey density and its role in mimicry and population regulation Mem Ent Sec Can., 45, (1965), 1-60.

3. Dubey B, Chandra P, Sinha P (2003). A model for fishery resource with reserve area. Nonlinear Anal R world Appl 4, 625-637.

4. Gardner. M.R and W.R.Ashby.1970. Connectance of large dynamical (cybernetic) systems: Critical values for stability. Nature 228 : 784.

5. J.D. Murray, Mathematical Biology I – An Introduction, Springer International Edition,2004.

6. Lotka AJ. 1925. Elements of mathematical biology. Williams and Wilkins, Baltimore,USA.

7. Madhusudanan.V, Gunasekaran.M, Vijaya.S. Diseased Prey with Harvesting predator in prey-predator system – An Analytical study IOSR Journal of Mathematics, ISSN: 2319-765X. volume 9, Issue 6.

8. Madhusudanan. V, Vijaya.S, Gunasekaran.M .Imapact of Harvesting inThree Species Food web Model with Two Distinct Functional Responses. IJIRSET, ISSN: 2319-8753, vol.3, Issue 2.

9. May.R.M ,Leonard W (1975) Nonlinear aspects of competition between three species. SIAM J Appl Math 29. 243-253.

10. Md.Sabir Rahman, Santabrata Chakravarty. A predator-prey model with disease in prey. Nonlinear Analysis:Modelling and control,2013, vol.18,No.2,191-209.

11. Odum.E.P. 1953. Fundamentals of Ecology. W.B.Saunders, Philadelphia-London.

12. Port.R.F.and Gelder, T.Van(eds),1995.Mind as Motion: Explorations in the Dynamics of Cognition.MIT Press,Cambridge,Mass.

13. Quirk.J.P. and R.Ruppert, 1965. Qualitative economic and the stability of equilibrium. Rev.Econ stud.32 : 311-326.

14. R.M.May., (1983) Ecology: The structure of food webs, Nature (London) 301, 566-568.

15. R.M.May.(1973) Qualitative stability in Model Ecosystems, Ecology 54, 638-641.

16. S.A.Wuhaib, Y.Abu Hasan. A predator-infected prey model with harvesting of infected prey. Science Asia 39S(2013): 37-41.

17. T.K.Kar and Ashim Batabyal. Persistence and stability of a two prey one predator system. International Journal of Engineering Science and Technology. Vol.2,No.2,2010, 174-190.

18. T.K.Kar, H.Matsuda,2007. Global dynamics and controllability of a harvested prey-predator system with Holling type – III functional response, Non-linear Anal: Hybrid system1, 59-67.

19. Voltera V. 1962 Opere matematiche mmemorie e note, Vol V. Roma(cremon): Acc.Naz.dei Lincei, Italy.

20. Zhang X, Chen L, Neumann A (2000). The stage structured predator – prey model and optimal harvesting policy. Math Biosci 168, 201-210.

 

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16.

Authors:

Ratul Chakraborty

Paper Title:

Web Browser Based Statistical Software – The Next Generation of Statistical Computing

Abstract: There are essentially two ways to deliver an application on PC/Laptop/tablet/smartphone: as a client-side/native application (developed using the appropriate platform-dependent development kit and installed on user devices) or as a web application (developed using web standards and accessed through a web browser-there’s nothing to install on user devices). Traditionally, we are familiar with the native applications. But the recent trend shows that, in near future web applications will become more competitive with native applications due to the ubiquity of web browsers and platform independent programming features. HTML5, Java Scrip and WebGL will bring a new level of computing to the web. At present we have a bunch of native Statistical Computing applications (for PCs and Laptops only) but there is a scarcity of good web application of such type which can run on any computing device (from PC to smartphone) without any hazard.

Keywords:
Native applications, Web applications, Graphical User Interfaces, Programming Language, Statistical Software.


References:

1. http://caniuse.com/
2. http://statcounter.com/

3. http://clicky.com/

4. http://w3counter.com/

5. http://wikimedia.org/

6. http://statpages.org/

7. http://statcrunch.com/

8. http://www.math.montana.edu/Rweb/

9. http://www.socr.ucla.edu/

10. http://jstat.github.io/

11. http://www.openepi.com/

12. http://www.quantitativeskills.com/sisa/

 

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17.

Authors:

M. S. Chennakesava Rao, N. V. N. Prabath

Paper Title:

Green Concrete using Agro Industrial Waste(Sugarcane Bagasse ASH)

Abstract: Today researches all over the world are focusing on ways of utilizing either industrial or agricultural wastes as a source of raw materials for the construction industry. These wastes utilization would not only be economical, but may also help to create a sustainable and pollution free environment. The utilization of industrial and agricultural waste produced by industrial processes has been the focus of waste reduction research for economic, environmental and technical reasons. Sugar-cane bagasse is a fibrous waste-product of the sugar refining industry, along with ethanol vapor. This waste product (Sugar-cane Bagasse Ash) is already causing serious environmental pollution, which calls for urgent ways of handling the waste. Bagasse has mainly contained silica and aluminum ion. In this project, the Bagasse ash has been chemically and physically characterized, and partially replaced in the ratio of 0%, 5%, 10%, 15% and 25% by the weight of cement in concrete. The bagasse ash was then ground until the particles passing the 90 μm sieve size reach about 85% and the specific surface area about 4716 cm2/gm.Ordinary Portland cement was replaced by ground bagasse ash at different percentage ratios. The compressive strengths of different mortars with bagasse ash addition were also investigated. M25 concrete mixes with bagasse ash replacements of 0%, 5%, 10%, 15%, 20% and 25% of the Ordinary Portland cement were prepared with water-cement ratio of 0.42 and cement content of 378 kg/m3 for the control mix. I will test fresh concrete tests like slump cone test where under taken as well as hardened concrete test like compressive strength, split tensile strength, flexural strength at the age of 7days, 28 days and 90 days was obtained. The test results indicated that up to 10% replacement of cement by bagasse ash results in better or similar concrete properties and further environmental and economic advantages can also be exploited by using bagasse ash as a partial cement replacement material ..

Keywords:
Baggase ash, Fibrous waste product.


References:

1. , K., Rajagopal, K., &Thangavel K., 2007. “Evaluation of Bagasse Ash as Supplementary Cementitious Material”, Journal of Cement and Concrete Composites
2. R. Srinivasan, K Sathiya, 2010. “Experimental study on bagasse ash in concrete”, International Journal of Service Learning in Engineering 5(2), p. 60.

3. Payá,J.,et. al., 2002. Sugarcane bagasse ash (SCBA): “Studies on its properties for reusing in concrete production”, Journal of Chemical technology and Biotechnology 77, p.. 321.

4. N. B. Singh, V. D. Singh and SaritaRai, 2000. Hydration of Bagasse Ash-Blended Portland cement, Journal of Cement and Concrete Research 30, p. 1485.

5. SumrerngRukzon, PrinyaChindaprasirt, 2012. Utilization of Bagasse Ash in High Strength Concrete, Journal of Materials and Design 34, p. 45.

6. V. S. Aigbodion, S. B. Hassan, T. Ause and G.B. Nyior, 2010. Potential Utilization of Solid Waste (Bagasse Ash), Journal of Minerals & Materials Characterization & Engineering 9, p.67-77.

7. Ganesan, K., Rajagopal, K., &Thangavel, K. 2007. “Evaluation of bagasse ash as supplementary cementitious material”. Cement and Concrete Composites, 29, 515-524.

8. Committee Board of sugar cane and sugar (2004). Summary of sugar cane and sugar industry in Thailand in 2003/2004, Division of sugar cane and sugar
industry Policy, Ministry of Industry, Vol.2 Bangkok Thailand (in Thai).

9. Baguant,K., Properties of concrete with bagasse ash as fine aggregate, In Proc 5th CANMET/ACI Intl. conf. on fly ash, silica fume, slag and natural pozzolans in concrete, Ed by Malhotra VM, USA, ACI SP, (1995)153(18), 315-337.

10. Payá,J.,et. al.,Sugarcane bagasse ash (SCBA): studies on its properties for reusing in concrete production, Journal of Chemical technology and Biotechnology, (2002)77, 321-325. 6. IS 383 -1970 “Specifications for Coarse and Fine Aggregates from Natural Sources for Concrete”, Bureau of Indian Standards, New Delhi.

11. IS 10262 -1981 “IS Method of Mix Design”, Bureau of Indian Standards, New Delhi

12. IS 516 -1959 “Methods of Tests for strength of concrete”, Bureau of Indian Standards, New Delhi

13. IS 456 -2000 “Code of Practice for Plain and Reinforced Concrete”, Bureau of Indian Standards, New Delhi.

14. Ali I (2004) Biomass: An ideal fuel for sugar mills for steam/power generation. Fuel Research Centre, PCSIR, Karachi, XVII(197), Dec

15. Wang R, Trettin V, Rudert R (2003) Umlauf recrystallization of granulated blast furnace slag and the significance for the hydraulic Reactivity. Institute for Building and Material Chemistry, Siegen University, Wilhelm Dyckerhoff Institute for Building Material Technology. Advances in Cement Research 15:29–33.

16. Bhatty JI, Gajda J, Miller FM (2001) Use of high-carbon fly ash in cement manufacture. Cement Americas May/June 32–34, 1, 2001.

 

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18.

Authors:

Narinder Singh Rana, S. N. Panda

Paper Title:

Spectrum of Cyber Threats and Attack Trends in Indian Scenario

Abstract: With the growth of Internet in the country the dependence of the Indian economy on ICT (Information and Communication Technology) has increased tremendously in last couple of decades, and corresponding has been growth of cyber incidents in the country. In the wake of increasing cyber incidents in India, Indian Computer Emergency Response Team (CERT-In) was constituted by government of India in 2004. In this paper the authors have studied the scope and scale of cyber incidents happening in the country. Website defacement being the most visible part of a cyber incident, have been used to study the trend of cyber attacks in India. Analysis has also been done regarding the various types of domain that have been attacked and the motivation behind these attacks, other common attacks and their growth trends have also been studied with the help of CERT-In data.

Keywords:
CERT-In, Cyber Incident, Security, Website Defacement.


References:

1. Kamluk V, “The Botnet Business”, Securelist (May 13, 2008),
2. https://www.securelist.com/en/analysis/204792003/The_botnet_business?print_mode=1

3. Srijith K. N, “Analysis of Defacement of Indian Web Sites”, First Monday Journal, 7(12)

4. Cyber Crime & Security Survey Report 2013, CERT-Australia

5. Indian Computer Emergency Response Team CERT-In, Annual reports 2006-13

6. Baker Y S, Bhattacharya S, “Analyzing security threats as reported by the United States Computer Emergency Readiness Team (US-CERT)”, IEEE conference on Intelligence and Security informatics, 2013, pp 10-12

7. Arce I, “More bang for the bug: An account of 2003’s attack trends”, IEEE Security & Privacy, 2004, 2(1), pp 66-68

8. Stiawan D , Idris Y, AbdullahA H, “Attack and Vulnerability Penetration Testing: FreeBSD”, TELKOMNIKA Telecommunication, Computing, Electronics and Control, 11(2)

9. Ransbotham S, Mitra S, “The Impact of Immediate Disclosure on Attack Diffusion and Volume”, Economics of Information Security and Privacy, 2013, pp 1-12

10. Common cyber attacks: reducing the impact – CERT UK, Director GCHQ, http://goo.gl/2RaCGD

11. Eeten M V, Bauer J M, “The Role of Internet Service Providers in Botnet Mitigation An Empirical Analysis Based on Spam Data”, Social Science Research Network, 2011.

 

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Authors:

R. R. Nanayakkara, Y. P. R. D. Yapa, P. B. Hevawithana, P. Wijekoon

Paper Title:

Automatic Breast Boundary Segmentation of Mammograms

Abstract: Accurate breast boundary estimation and segmentation of breast tissue region from the background of the mammogram image is an important pre-processing task in computer-aided diagnosis of breast cancer. This paper presents an automated system to estimate skin-line and breast segmentation. The proposed method is based on automatic seed region selection, modified fast marching algorithm to propagate the seed region and automatic boundary point selection with intensity gradient information to initial boundary estimation and morphological operators to final boundary estimation and breast tissue region segmentation. Performance of the proposed method was tested by using 136 mammogram images with all types of breast tissues taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that the sensitivity of this algorithm is 99.2% of the ground truth breast region and accuracy of the segmentation is 99.0%. By analyzing the results we can conclude that this system is capable of estimate the breast boundary and segment the breast area from background for all three types of breast tissues with high accuracy level.

Keywords:
Breast Cancer, Mathematical Morphology, Modified Fast Marching Algorithm


References:

1. J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, F. Bray, GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 11 [Internet].Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr, accessed on 11/2/2015.
2. R. Bird, Professional quality assurance for mammography screening programs. Radiology. 1990;175(2):587-605. Pub Med PMID: 2217807. DOI: 10.1148/radiology.177.2.2217807.

3. R.D. Yapa, K. Harada, Breast skin-line estimation and breast segmentation in mammograms using fast-marching method. International Journal of Biological, Biomedical and Medical Sciences. 2008;3(1):54-62.

4. M.A. Wirth, J. Lyon, D. Nikitenko, A. Stapinski, editors. Removing radiopaque artifacts from mammograms using area morphology. Image Processing Proc SPIE 2004: Proceedings of SPIE Medical Imaging; 2004; San Diego, California, USA: SPIE; 2004. p. 1054–65. DOI: 10.1117/12.535372.

5. P. Salembier, J. Serra, Flat zones filtering, connected operators, and filters by reconstruction. Image Processing, IEEE transactions on. 1995;4(8):1153-60. DOI: 10.1109/83.403422.

6. J. Nagi, S. Abdul Kareem, F. Nagi, S. K. Ahmed, editors. Automated breast profile segmentation for ROI detection using digital mammograms. Biomedical
Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on; 2010. IEEE. DOI: 10.1109/iecbes.2010.5742205.

7. B. Eli, Eli Billauer’s Freelancing Electrical Engineer. Israel: Eli Billauer; [20 July 2012; cited 2013 03]; Available from: http://billauer.co.il/peakdet.html.

8. T. Ojala, J. Näppi, O. Nevalainen. Accurate segmentation of the breast region from digitized mammograms. Computerized Medical Imaging and Graphics. 2001;25(1):47-59. DOI: 10.1016/s0895-6111(00)00036-7.

9. T-K. Lau, W. F. Bischof. Automated detection of breast tumors using the asymmetry approach. Computers and biomedical research. 1991;24(3):273-95. DOI: 10.1016/0010-4809(91)90049-3.

10. U. Bick, M. L. Giger, R. A. Schmidt, R. M. Nishikawa, D. E. Wolverton, K. Doi. Automated segmentation of digitized mammograms. Academic Radiology. 1995;2(1):1-9. PubMed PMID: 9419517. DOI: 10.1016/s1076-6332(05)80239-9.

11. J. J. Heine, M. Kallergi, S. M. Chetelat, L. P. Clarke. Multi resolution wavelet approach for separating the breast region from the background in high resolution digital mammography. Digital Mammography: Springer; 1998. p. 295-8. DOI: 10.1007/978-94-011-5318-8_49.

12. R. Chandrasekhar, Y. Attikiouzel, editors. Gross segmentation of mammograms using a polynomial model. Engineering in Medicine and Biology Society, 1996 Bridging Disciplines for Biomedicine Proceedings of the 18th Annual International Conference of the IEEE; 1996. IEEE. DOI: 10.1109/iembs.1996.652707.

13. M. M. Goodsitt, H-P. Chan, B. Liu, S. V. Guru, A. R. Morton, S. Keshavmurthy, et al. Classification of compressed breast shapes for the design of equalization filters in x-ray mammography. Medical Physics. 1998;25(6):937-48. PubMed PMID: 9650184. DOI: 10.1118/1.598272.

14. C. Zhou, H-P. Chan, N. Petrick, M. A. Helvie, M. M. Goodsitt, B. Sahiner, et al. Computerized image analysis: Estimation of breast density on mammograms. Medical physics. 2001;28(6):1056-69. PubMed PMID: 11439475. DOI: 10.1118/1.1376640.

15. M. Abdel-Mottaleb, C. S. Carman, C. R. Hill, S. Vafai,Skinline detection in digitized mammograms. Journal of digital imaging. 1997;10:224-5. PubMed Central PMCID: PMC3452812.DOI: 10.1007/BF03168707.

16. Morton, H. Chan, M. Goodsitt. Automated model-guided breast segmentation algorithm. Med Phys. 1996;23:1107-8.

17. N. Karssemeijer, G. Te Brake. Combining single view features and asymmetry for detection of mass lesions. Digital Mammography: Springer; 1998. p. 95-102. DOI: 10.1007/978-94-011-5318-8_16.

18. R. Ferrari, R. Rangayyan, J. Desautels, A. Frere, Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc. Proceedings of the 5th international workshop on digital Mammography. 2000:573-9. PubMed PMID: 15125. DOI: 10.1148/radiol.2291032535.

19. S. Thiruvenkadam, M. Acharyya, N. Neeba, P. Jhunjhunwala, S. Ranjan, editors. A region-based active contour method for extraction of breast skin-line in mammograms. Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 2010. IEEE. DOI: 10.1109/isbi.2010.5490383.

20. M. A. Wirth, A. Stapinski, editors. Segmentation of the breast region in mammograms using snakes. Computer and Robot Vision, 2004 Proceedings First Canadian Conference on; 2004. IEEE. DOI: 10.1109/cccrv.2004.1301473.

21. R. Martí, A. Oliver, D. Raba, J. Freixenet, Breast skin-line segmentation using contour growing. Pattern Recognition and Image Analysis: Springer; 2007. p. 564-71. DOI: 10.1007/978-3-540-72849-8_71.

22. P. K. Saha, J. K. Udupa, E. F. Conant, D. P. Chakraborty, D.Sullivan, Breast tissue density quantification via digitized mammograms. Medical Imaging, IEEE Transactions on. 2001;20(8):792-803. PubMed PMID: 11513030. DOI: 10.1109/42.938247.

23. H. E. Rickard, G. D. Tourassi, A. S. Elmaghraby, editors. Self-organizing maps for masking mammography images.Information Technology Applications in Biomedicine, 2003 4th International IEEE EMBS Special Topic Conference on; 2003. IEEE. DOI: 10.1109/itab.2003.1222538.

24. M. Wirth, J. Lyon, D. Nikitenko, editors. A fuzzy approach to segmenting the breast region in mammograms. Fuzzy Information, 2004 Processing NAFIPS’04 IEEE Annual Meeting of the; 2004. IEEE. DOI: 10.1109/nafips.2004.1336329.

25. J. M. Gauch. Image segmentation and analysis via multiscale gradient watershed hierarchies. Image Processing, IEEE Transactions on. 1999;8(1):69-79. DOI: 10.1109/83.736688.

26. W. Wirth, D. Nikitenko, J. Lyon, Segmentation of Breast Region in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm. International Journal on Graphics, Vision and Image Processing. 2005;5(2):45-54.

27. J.Suckling, The mini-MIAS database of mammograms. In: Society TMIA, editor. Digital Mammography Database ver 1.2.ed.

28. P. C. Johns, M. J. Yaffe. X-ray characterisation of normal and neoplastic breast tissues.Physics in medicine and biology. 1987;32(6):675. DOI: 10.1088/0031-9155/32/6/002.

29. J. Méndez, P. G. Tahoces, M. J. Lado, M. Souto, J. Correa, J. J. Vidal. Automatic detection of breast border and nipple in digital mammograms. Computer methods and programs in biomedicine. 1996;49(3):253-62. DOI: 10.1016/0169-2607(96)01724-5.

30. R. Chandrasekhar, Y. Attikiouzel, editors. Automatic breast border segmentation by background modeling and subtraction. 5th International Workshop on Digital Mammography (IWDM),(Yaffe M ed), Medical Physics Publishing, Madison, USA; 2000.

31. D. Raba, A. Oliver, J. Martí, M. Peracaula, J.Espunya, Breast segmentation with pectoral muscle suppression on digital mammograms. Pattern Recognition and Image Analysis: Springer; 2005. p. 471-8. DOI: 10.1007/11492542_58.

32. S. Dehghani, M.Dezfooli, A method for improve preprocessing images mammography. International Journal of Information and Education Technology. 2011;1(1):90-3. DOI: 10.7763/ijiet.2011.v1.15.

 

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Authors:

S. M. Rajbhoj, P. B. Mane

Paper Title:

An Approach of Combining Iris and Fingerprint Biometric At Image Level in Multimodal Biometrics System

Abstract: Biometric systems depending on single source of information has many limitations. These are noisy input data, inability to enroll, unacceptable error rates, universality of traits and spoofing. Multimodal biometric system overcomes these limitations by combining information from multiple sensors. In Image fusion usually images are extracted from single trait using different sensors. This type of fusion is generally used when feature set are homogenous. In this paper a multibiometric system using image level fusion of two most used biometric traits, fingerprint and iris is proposed. The feature set obtained from iris and fingerprint images are incompatible, non-homogenous and relationship between them is not known. Here the pixel information is fused at image or feature level. A unique feature vector is constructed from the textural information of fused image of fingerprint and iris. Feature vector is stored as template and used for matching. Matching is carried using Hamming distance. The proposed framework is evaluated using standard database and database created by us. The system overcomes limitation of unimodal biometric system and equal error rate of 0.4573 has been achieved.

Keywords:
biometric, fingerprint, iris; wavelet transform, texture, feature level, fusion, hamming distance.


References:

1. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, pp. 4–20, Jan 2004.
2. Ross and A. K. Jain, “Information fusion in biometrics,”Pattern Recognition Letters, vol. 24, pp. 2115–2125, Sep 2003.

3. K. Jain and A. Ross, Multibiometric Systems. Communications of the ACM, Special Issue on Multimodal Interfaces, 47(1):34–40, January 2004.

4. Prabhakar, S. and Jain, A. K. “Decision-level Fusion in Fingerprint Verification.”, Pattern Recognition, 35(4):861-874. (2002).

5. Bhatnagar, J., Kumar, A., Saggar, N.: ‘‘A novel approach to improve biometric recognition using rank level fusion,’’ Proc.CVPR 2007, Minneapolis, MN, pp. 1–6, (2007).

6. L. Hong and A. K. Jain, “Integrating faces and fingerprints for personal identification,” IEEE Transactions on PAMI, vol. 20, pp. 1295–1307, Dec 1998.

7. Kumar, D. C. M. Wang, H. C. Shen, and A. K. Jain, “Personal verification using palmprint and hand geometry biometric,” in Proc. of 4th Int’l Conf. on Audio and Video-based Biometric Person Authentication (AVBPA), (Guildford, UK), pp. 668–678, Jun 2003.

8. Fierrez-Aguilar, J., Nanni, L., Lopez-Penalba, J., Ortega-Garcia, J., and Maltoni, D. An On-line Signature Verification System based on Fusion of Local and Global Information. In Fifth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Rye Brook, USA pages 523-532. (2005).

9. Y. Wang, T. Tan, and A. K. Jain. “Combining Face and Iris Biometrics for Identity Verification.”, In Fourth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Guildford, UK, pages 805-813, June 2003

10. Ross, A. K. Jain, and J. Reisman, “A hybrid fingerprint matcher,” Pattern Recognition, vol. 36, pp. 1661–1673, Jul 2003

11. X. Lu, Y. Wang, and A. K. Jain, “Combining classifiers for face recognition,” in Proc. IEEE Int’l Conf. on Multimedia and Expo (ICME), vol. 3, (Baltimore, MD), pp. 13–16, Jul 2003

12. G. L. Marcialis and F. Roli, “Fingerprint verification by fusion of optical and capacitive sensors,” Pattern Recognition Letters, vol. 20, pp 1315-1322, Aug 2004.

13. K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proc. Of Workshop on Multimodal User Authentication, (Santa Barbara, CA), pp. 25–32, Dec 2003

14. Ross, A. and Govindarajan, R. “Feature Level Fusion Using Hand and Face Biometrics.” In Proceedings of SPIE Conference on Biometric Technology for Human Identification II, volume 5779, pages 196-204, Orlando, USA., (2005)

15. Asim Baig, Ahmed Bouridane, Fattih K., Gang Qu, “Fingerprint-Iris Fusion based Identification System using a Single Hamming Distance Matcher.”,International Journal of Bio-Science and Bio-Technology, Vol 1, No. 1, Dec 2009.

16. V. Conte,C. Militello, F Sorbello, “A Frequency based approach for Feature Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems”, IEEE Transactions of System, Man and Cybernetics, vol-40, No.4, July2010.

17. Rossani F., Eslava M.T., Ea T., Aml F., Amara A., “Iris Identification and robustness evaluation of wavelet packets based algorithm”, IEEE International Conference on image processing, vol.3, pp. III -257-260

18. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1993, 1148–1161

19. Libor Masek, “Recognition of Human Iris Patterns for Biometric Identification”, Thesis Report School of Computer Science and Software Engineering, Western Australia, 2003

20. W. W. Boles, & B. Boashah, A Human Identification Technique Using Images of the Iris and Wavelet Transform, IEEE Transaction on Signal Processing, 46(4), 1998, 1185-1188

21. Digital Persona homepage, http://www.digitalpersona.com

22. Center for Biometrics and security Research. http://www.cbsr.ia.ac.cn/IrisDatabase.htm.

 

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Authors:

D.V. Biradar, Praful P. Maktedar

Paper Title:

Performance Exploration of QoS parameters in MANET

Abstract: Nowadays there are large applications expanding for the reliable transport of data packets from source node to the sink node amongst them Mobile Ad hoc network (MANET) has very vast extent of study. Different mobile sensor nodes are arbitrarily positioned in given network without having much loss of data packets in the network. It encompasses number of sensor nodes having inadequate processing power, communicating over a network. These sensors are scattered in specified network environment so that they gathers data, process that data and send it back to the destination. Various factors are affecting on data transmission process like reporting rate, packet size. Here by changing reporting rate, we calculate packet Delivery Ratio, Packet loss Ratio as well as throughput, control overheads and Energy consumption of a system.

Keywords:
Mobile Ad hoc Network; Reliability; Reporting Rate; Packet Delivery ratio; Congestion Control.


References:

1. Lajos Hanzo, Rahim Tafazolli,”QoS aware Routing and Admission Control in Shadow-Fading Environments for Multirate MANETs” in IEEE journal, vol. 10, No. 5, May 2011.
2. Mahmoud Al-Shugran, Osman Ghazali, Suhaidi Hassan, Kashif Nisar, and A. Sukhi, M. Arif,”A qualitative Comparison Evaluation of the Greedy forwarding Strategies in Mobile Ad hoc Network”, vol. 36, pp. 887-897, Nov. 2012.

3. X. Xiang, X. Wang, Y. yang,”Supporting Efficient and Scalable Multicast for Mobile andAd hoc Networks”, vol. 10, No 5, April 2011.

4. Richard j. La and Eunyoung Seo,” Expected Routing Overheads for location service in MANET under flat geographic routing”, vol. 10, issue 3, March 2011.

5. Seungjin Park, Seong-Moo Yoo”An Efficient Reliable one- hop broadcast in Mobile Ad hoc Networks”, vol. 11, pp 19-28, April 2012.

6. Shengbo Yang, Chai Kiat Yeo, Bu Sung Lee ,“Towards Reliable data delivery for highly Dynamic Mobile Ad hoc Networks” Journal IEEE transactions on parallel and Distributed Syatems, vol. 22, issues 12, pp 2100-2107, Dec. 2011.

7. D.G. Reina, S.L. Toral, P. Johnson and F. Barreiro,”A Reliable Route Selection based on Caution Zone and Arrival Angle”, IEEE communication letters, vol. 15, issues 11, pp. 1252-1255, Nov. 2011.

8. Xiaoqin Chen, Haley M. Jones and Dhammika Jayalath,” Channel aware Routing in MANET with route Handoff”, vol. 10, issues 1, Jan 2011.

9. Robert J.Hall,” An Improved Geocast for Mobile Ad hoc Networks”, IEEE communication letters, vol. 10, No. 2, Feb. 2011.

10. Zhan Bo Su, Yuan Ming Wu,”Prediction based Event to Sink Reliability in Wireless Sensor Network”, vol. 1, pp. 4244-4249, 2009.

11. Zhenzhen Ye ,AlhussionA.Abouzeid,” Optimal Stochastic Location updates in mobile Ad Hoc Networks”, vol. 10, No. 5, May 2011.

 

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Authors:

V.S. Malunjkar, M.G. Shinde, R.D. Bansod, A.A. Atre

Paper Title:

Development of a Soft Tool for Estimating Direct Runoff From Watersheds

Abstract: Natural Resources Conservation Services-Curve Number (NRCS-CN) model is the most commonly used hydrological model for runoff estimation. This paper introduces about the interface developed to estimate curve number and runoff depth for hydrologic evaluations. The programming syntax was developed in Visual Basic 10.0 for its simplicity. The developed tool is easy to handle and can be useful for academicians, scientists and decision makes involved in watershed planning and development.

Keywords:
Antecedent moisture condition, curve number NRCS-CN method, runoff, watershed.


References:

1. M. J. Mack, “HER-Hydrologic evaluation of runoff; The Soil Conservation Service Curve Number technique as an interactive computer model”. Comput. Geosci. 1995, 21, p. 929-935.
2. S. Shadeed and M. Almasri, “Application of GIS based SCS-CN method in West bank catchments, Palestine.” WSE-3, 2010, p. 1-13.

3. USDA, “Urban hydrology for small watersheds”. United States Department of Agriculture. Natural Resources Conservation Service. Conservation Engineering Division. Tech. Release 55. 2nd ed. Washington, DC. 1986. P. 164.

4. USDA, “Estimation of direct runoff from storm rainfall”. Part 630. Hydrology National Engineering Handbook. Ch. 10. United States Department of Agriculture. Natural Resources Conservation Service. Washington, DC. 2004. P. 22.

5. S. K. Mishra and V. P. Singh. “Long-term hydrological simulation based on the Soil Conservation Service curve number”. Hydrol. Process. 2004. 18. p. 1291-1313.

6. S. K. Mishra and V. P. Singh. “A relook at NEH-4 curve number data and antecedent moisture condition criteria”. Hydrol. Process. 2006. 20. p. 2755-2768.

7. S. K. Mishra, M. K. Jain, R. P. Pandey and V. P. Singh. “Catchment area-based evaluation of the AMC-dependent SCS-CN-based rainfall-runoff models”. Hydrol. Process. 2005. 19. p. 2701-2718.

8. N. W. Kim and J. Lee. “Temporally weighted average curve number method for daily runoff simulation” Hydrol. Process. 2008. 22. p. 4936-4948.

9. R. K. Sahu, S. K. Mishra and T. I. Eldoh. “An improved AMC-coupled runoff curve number model”. Hydrol. Process. 2010. 24. p. 2834-2839.

10. H. Cao, R. W. Vervoort, and S. M. Dabney. “Variation in curve numbers derived from plot runoff data for New South Wales (Australia)”. Hydrol. Process. 2011. 25. p. 3774-3789.

11. V. M. Ponce and R. H. Hawkins. “Runoff curve number: has it reached maturity?” J. Hydrol. Eng. 1996. 1. p. 11-19.

12. J. P. Patil, A. Sarangi, O. P. Singh, A. K. Singh and T. Ahmed. “Development of a GIS interface for estimation of runoff from

 

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23.

Authors:

Salem S. M. Khalifa, Kamarudin Saadan, Norita Md Norwawi

Paper Title:

Development of Framework for Wireless Intelligent Landmines Tracking System Based on Fuzzy Logic

Abstract: The losses of developing countries from landmines accidents are very large. Thus, the need for new techniques to improve the efficiency of Landmines tracking systems is evident. In the recent years, many of research efforts have been directed to develop new and improved landmine detection methods. However, the increased costs of improving these methods led to drive up their prices. Thus they will not be available to the general public. The aim of this paper is to find a cheap and an effective method to help people for protecting and warning them from landmines risk during practiced their daily lives. In this context, this paper presents the design and development of framework for a Wireless Intelligent Landmines Tracking System (IWLTS) using mobile phone based on GPS and fuzzy logic. Proposed framework is really very helpful for the users who living near mine affected areas to track their children and themselves through Smart phones from landmines risk.

Keywords:
Landmines, Fuzzy logic, Fuzzy set, MATLAB.


References:

1. BBCNEWS. “South Africa destroys landmine stocks,”1997. http://news.bbc.co.uk/2/hi/19940.stm
2. V. E. Stephen. “THE ORIGINS OF THE FIRST WORLD WAR,” unpublished.

3. Handicap International. “Handicap International prepares to launch demining actions in Tripoli,” 2011. http://www.handicap-international.org.uk/resources/latest_news/emergencies/20111021_demining_tripoli

4. UNICEF. “Children and Landmines: A Deadly Legacy,” 2012. http://www.unicef.org/spanish/protection/files/Landmines_Factsheet_04_LTR_HD.pdf

5. M, Habib. “Humanitarian demining: The problem, difficulties, priorities, demining technology and the challenge for robotics,” Humanitarian Demining Innovative Solutions and the Challenges of Technology, M. Habib, Ed. I-Tech Education and Publishing, Croatia .2008. pp 1-56.

6. GAO “Landmine Detection, DoD’s research program need a comprehensive evaluation strategy,” United States General Accounting Office, Apr 2001.

7. F. Abujarad. “Ground penetrating radar signal processing for landmine detection,” PhD diss., Otto-von-Guericke-Universität Magdeburg,
Universitätsbibliothek, 2007.

8. S. Photiou. “CBRNE-Terrorism Newsletter–2012©.”

9. J Belezina. “SAPER app turns a smart phone into a mobile bomb sniffer Gizmag,” 2012. Available on http://www.gizmag.com/saper-explosives-detector-app/22614/

10. D. Guelle, A. Smith, A., Lewis and T. Bloodworth “Metal detector handbook for humanitarian demining,” European Communities. 2003. ISBN 92-894-6236-1.

11. K. Park, S. Park, K. Kim, and K. H. Ko, “MULTI-FEATURE BASED DETECTION OF LANDMINES USING GROUND PENETRATING RADAR,” Progress In Electromagnetics Research 134. 2013.

12. M. K. Habib. “Mechanical Mine Clearance Technologies and Humanitarian Demining: Applicability and Effectiveness,” In Proceedings of the 5th International Symposium on Technology and Mine Problem, California, USA. 2002.

13. J. MacDonald. Alternatives for landmine detection. No. 1608. RAND Corporation, 2003.

14. IRIN. “Laying Landmines to Rest? Report, IRIN Web Special on Humanitarian Mine Action, (with special focus on the 2004 Nairobi Summit of a Mine Free World),” 2004.

15. Weilenmann , and J. Oskar. “Understanding people and animals: the use of a positioning system in ordinary human-canine interaction.” InProceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2631-2640. ACM, 2011.

16. J., G. F. Weetjens, B. K. Mgode, C. C. Davis, and N. W. Beyene. “African giant rats for tuberculosis detection: A novel diagnostic technology.” In Global forum update on research for health, vol. 6, pp. 39-42. 2009.

17. Poling, B. J. Weetjens, C. Cox, N. Beyene, H. Bach, and A. Sully. “Teaching giant African pouched rats to find landmines: Operant conditioning with real consequences,” Behavior analysis in practice 3, no. 2 (2010): 19.

18. Poling, B. J. Weetjens, C. Cox, N. Beyene A. Durgin and A. Mahoney. “Tuberculosis detection by giant African pouched rats.”The Behavior Analyst 34, no. 1. 2011: 47.

19. Poling, et al. “Two strategies for landmine detection by giant pouched rats.”The Journal of ERW and Mine Action 14 .2010.

20. R. L. Woodfin, ed. Trace chemical sensing of explosives. John Wiley & Sons, 2006. . ISBN-10: 0-471-73839-5.

21. HCR “HUMANITARIAN DEMINING 2012,” The 9th International Symposium. The Croatian Mine Action Center for testing, development and training. Zagreb. 2012. http://www.ctro.hr/eng/

22. Anuar, S. Yussof, I, Said and J. T. T. Chuan. “The Development Of An Autonomous Personal Mobile Robot System For Land Mines Detection On Uneven Terrain: An Experience,” Advanced Technology Congress, Putrajaya, Malaysia.2003.

23. H. Najjaran and A. A. Goldenberg. . “Landmine detection using an autonomous terrain-scanning robot.” Industrial Robot: An International Journal32, no. 3 .2005.pp 240-247.

24. S. Havlik. “Land Robotic Vehicles for Demining, Humanitarian Demining,” Maki K. Habib (Ed.), ISBN: 978-3-902613-11-0, InTech ,2008. Available from: http://www.intechopen.com/books/humanitarian_demining/land_robotic_vehicles_for_demining

25. Khamis. “Minesweepers: towards a landmine-free Egypt,” The Journal of ERW and Mine Action, No. 17.1.2013.

26. M. Bisgaard, A. la Cour-Harbo and J. Bendtsen “Full state estimation for helicopter slung load system.” In AIAA Guidance, Navigation and Control Conference and Exhibit. 2007.

27. M. Bisgaard. Modeling, Estimation, and Control of Helicopter Slung Load System. PhD thesis, Department of Signals and Systems, Aalborg University, Denmark. Pages256 ISBN 87-90664-34-5.2008.

28. T. Lardner. “A Study of Manual Mine Clearance.” Geneva International Centre for Humanitarian Demining (GICHD) .2005.

29. M. K. Habib. “Humanitarian Demining: Reality and the Challenge of Technology-The State of the Arts.” International Journal of Advanced Robotic Systems. Vol. 4, No. 2. ISSN 1729-8806, pp. 151-172. 2007.

30. M. Cain and T. V. Meidinger. “The improved landmine detection system.” InThe Detection of Abandoned Land Mines: A Humanitarian Imperative Seeking a Technical Solution, EUREL International Conference on (Conf. Publ. No. 431), pp. 188-192. IET, 1996.

31. P. Gooneratne, S. C. Mukhopahyay and G.S. Gupta “A review of sensing technologies for landmine detection: Unmanned vehicle based approach.” In second I Conf on Autonomous Robots and Agents (Palmerston North, New Zealand). 2004.

32. D.W. Gage. “Many-robot MCM search systems.” In Proceedings of the Autonomous Vehicles in Mine Countermeasures Symposium, vol. 9, pp. 56-64. 1995.

33. L. A. Zadeh. “Fuzzy sets.” Information and control 8, no. 3 .1965. pp 338-353.

34. Netto. Lehrbuch der Combinatory. Vol. 7. BG Teubner, 1901.

35. N. J. Nilsson, “Problem-solving methods in.” Artificial Intelligence .1971.

36. P. Rodjito. “Position tracking and motion prediction using Fuzzy Logic” .Honors Theses, 520.2006.

37. T. Munakata,. Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science). Springer Publishing Company, Incorporated, 2008.

38. Lahsasna. “Evaluation of credit risk using evolutionary-fuzzy logic scheme.” 2009.

39. Grosanand and A. Abraham. Intelligent systems: A modern approach. Vol. 17. Springer, 2011.

40. M, Negnevitsky. Artificial intelligence: a guide to intelligent systems. Pearson Education .2005.

41. R. Yager and D. Filev. “Generation of Fuzzy Rules by Mountain Clustering,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, 1994. pp. 209-219.

 

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24.

Authors:

Ram Naresh Mishra, Prabhat Kumar

Paper Title:

Automatic Generation Control of Multi-Area Power Systems with Parallel EHVAC/ HVDC Inter-Ties

Abstract: This paper applies the modern control theory to design optimal AGC regulators using full state vector feedback for multi-area interconnected hydro-thermal power systems and implemented under considerations in the wake of 1% step load perturbation in thermal/hydro area. For the present study, power system model consists of one area with reheat thermal power plant and two area with hydro power plants having identical capacity. The system interconnection is considered namely (I) EHVAC inter-ties only (II) EHVAC in parallel with HVDC inter-ties. The dynamic model of incremental power flow through HVDC transmission link is derived based on frequency deviation at both rectifier and inverter ends. Moreover, the HVDC link is considered to be operating in constant current control mode. The system responses have been simulated in Mat lab. Responses of deviation in frequencies, deviation in tie line powers (EHVAC as well as HVDC) and integral of area control errors have been plotted for 3- area. Thus, on the basis of these responses, the dynamic performance of the system has been studied. Besides this, to study the closed loop system stability, the closed loop system eigen values are computed.

Keywords:
Interconnected power systems; HVDC transmission links; System dynamic performance; EHVAC//HVDC transmission link; Optimal AGC regulator.


References:

1. FOSHA C.E., and ELGERD 0.I.,”The megawatt- frequency control problem: a new approach via optimal control theory”, IEEE Trans., PAS-89, 1970, pp.563-577.
2. IEEE Committee Report,” Dynamic models for steam and hydro turbines in power system Studies”, IEEE Trans., PAS-92, 1973, pp. 1904- 1915.

3. M Calovic, “linear regulator design for a load and frequency control”, IEEE transactions, PAS (91), Nov/Dec1972, pp. 2271-2285.

4. M.L. Kothari and J. Nanda, “Application of optimal control Strategy to Automatic generation control of a hydrothermal system”, IEE proceedings, Vol. 135, Pt. D, No. 4, July, 1988, pp.268-274.

5. Prabhat Kumar, K E Hole’ and R P Aggarwal, “Design of suboptimal AGC Regulator for Hydro-Thermal Power System”. IE (I) Journal. pt EL6, June 1983 ,pp 304-309.

6. Ibraheem, P Kumar and S Ahmad, “Dynamic Performance Enhancement of Hydro-Power Systems with Asynchronous Tie-lines”. IE (I) Journal Vol 85, June 2004 pp. 23-34.

7. O. I. Elgerd and C. Fosha, “Optimum megawatt frequency control of multi-area electric energy systems,” IEEE Trans. Power App. Syst., vol. PAS-89, no. 4, Apr. 1970, pp. 556–563.

8. CARPENTIER, J.: ‘State of the art review, “To be or not to be modern” that is the question for automatic generation control (point of view of a utility engineer)’,Int. J. Electr. Power & Energy Syst., 1985,7, pp. 81-91.

9. D. Das, J. Nanda, M. L. Kothari, and D. P. Kothari, “Automatic generation control of hydrothermal system with new area control error considering generation rate constraint,” Elect. Mach. Power Syst., vol. 18, no. 6, Nov. /Dec. 1990. pp. 461–471.

10. Ibraheem, Prabhat Kumar, and Dwarka P. Kothari, “Recent Philosophies of Automatic Generation Control Strategies in Power Systems”, IEEE transactions on power systems, vol. 20, no. 1, February 2005, pp.346- 357.

11. IEEE PES Working Group, Hydraulic turbine and turbine control models for system dynamic,” IEEE Trans. Power Syst., vol. PWRS-7, no. 1, Feb. 1992, pp. 167–174,

12. IEEE Committee Report, “Standard definitions of terms for automatic generation control on electric power systems”, IEEE Trans. Power App. Syst., vol. PAS-89, Jul./Aug. 1970.pp.7-11.

13. E.V. Bohn, S.M. Miniesy, “Optimum load-frequency continuous control with unknown Deterministic power demand”, IEEE committee report, 1971, pp.1910-1915.

14. Ibraheem & Prabhat Kumar, “Study of Dynamic Performance of Power Systems with Asynchronous Tie- lines considering Parameter Uncertainties”, IE (I) Journal vol83, June 2004, pp.35-42.

15. Ibraheem & Prabhat Kumar, “Current Status of the Indian Power Systems and Dynamic Performance Enhancement of Hydro Power Systems with Asynchronous Tie Lines”, Electric Power Components &Systems 2003, pp.605-626.

16. H. L. Zeynelgil, A. Demiroren, and N. S. Sengor, “The application of ANN technique to automatic generation control for multi-area power system,” Elect. Power Energy Syst., vol. 24, no. 5, Jun.2002,pp. 345–354.

17. E. V. Bohn and S. M. Miniesy, “Optimum load frequency sample data control with randomly varying system disturbances,” IEEE Trans. Power App. Syst., vol. PAS-91, no. 5, pp. 1916–1923, Sep./Oct. 1972.

 

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25.

Authors:

Esther Njoki Gacheru, Stephen Onyango Diang’a

Paper Title:

Regulating Building Contractors in Kenya and Challenges of Enforcing the National Construction Authority Mandate

Abstract: The construction industry in Kenya has not had a regulating body since the disbandment of the National Construction Cooperation in 1988. The National Construction Authority (NCA) was then established in 2012 to regulate the construction sector and was mandated to register and regulate the undertakings of contractors. This research deals with the regulation of building contractors in Kenya and challenges of enforcing the NCA mandates. The main objective of this study is to investigate and document the challenges faced by the NCA in regulating building contractors in Kenya. Data was obtained from building contractors by means of questionnaires. The findings of the research indicated that the major challenges to the effectiveness of the NCA in registering and regulating the practices of building contractors include; corruption, poor sensitization, lack of proper organization of the NCA contractor training programs and centralization of the NCA services.

Keywords:
Contractors, Kenya, NCA, regulation, registration.


References:

1. Regulation. (2014, July 29). The American Heritage® Stedman’s Medical Dictionary. Retrieved from Dictionary.com website: http://dictionary.reference.com/browse/regulation
2. Gelder, J. d. (2004). Conceptual modelling of building regulation knowledge. Artificial Intelligence in Engineering, Pages 273–284.

3. G.O.K. (2012). National Construction Authority Regulation 2012. Nairobi: Government Press.

4. Nahinja, D. (2014, July 29). Ujenzibora. Retrieved from Ujenzibora: http://www.ujenzibora.com/

5. Nyaanga, J. K. (2014). The effect of competence of contractors on the. Prime Journal of Social Science (PJSS).

6. Cattell, K. (1994). Small black builders in South Africa: problems and prospects. Research Paper Series No. 2,. Cape Town: Department of Construction Economics and Management.

7. CIDB. (2000). Construction Industry Development Board Act no. 38 of 2000. Republic of South Africa.

8. CIDB. (2004). Construction Industry Development Board Directory. Kuala Lumpur: CIDB.

9. GOK. (2013, November 26). About Us: The National Construction Authority. Retrieved from The National Construction Authority Web Site: http://www.nca.go.ke/

10. James E. Fenn, D. M. (2008). The Purposes for State Residential General Contractor Licensing in the United States. International Journal of Construction Education and Research.

11. Njuguna, H. B. (2002). The construction Industry in Kenya and Tanzania: Understanding the mechanisms that promote growth. The construction industry value chain, 32-41.

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26.

Authors:

H. Mohssine, H. Bouhouch, F. Debbagh

Paper Title:

Calculated and Measured Dark Conductivity in P-Type Polycrystalline CdTe Thin Films

Abstract: In this study we describe a numerical procedure for modeling the dark conductivity in a p-type polycrystalline Cadmium Telluride (CdTe). We base our approach on the comparison between measured and computed conductivity. For this purpose, the Fermi-Dirac statistic combined with the numerical solution of the charge neutrality equation allows to calculate the exact dark conductivity as function of the temperature. The results are then used to fit the experimental conductivity. Measures have been undertaken on CdTe thin films produced by r-f sputtering on glass substrates at room temperatures. It is shown that the amount of the experimental conductivity can be modeled, Quito precisely, by suitably choosing parameters of localized states, without needing complicated approaches like Mott and seto’s models. However, from a point of view of experimental fitting, it is verified, in accordance with our previous general treatment that the model’s parameters are not unique and cannot be derived from Arrhenius diagram analyses.

Keywords:
Thins films, CdTe, Sputtering, Conductivity.


References:

1. Thèse de Doctorat, Spécialité: Matière Condensée, Surface et Interface, Institut National des Sciences Appliquées, Lyon, Mars 2007.
2. Boudghene Stambouli, ‘Solar Photovoltaic at the Tipping Point: Pathways from Evolutionary to Disruptive and Revolutionary Technologies’, Université des Sciences et de la Technologie d’Oran, USTO.MB, 2009.

3. R.C. Campbell, ‘A Circuit-Based Photovoltaic Array Model for Power System Studies’, 39th North American Power Symposium, NAPS’07, pp. 97 – 101, Sept. 30 –
Oct. 2, 2007.

4. Ongaro R. , Pillonnet A. and Garoum M. J.Phys.D: Appl.Phys. 28. 129-37. (1995)

5. Press.W.H. Flannery.B.P, Teukolsky.S.A and Vetterlihg.W.T Numerical Recipes in Pascal.Cambridge University Press. (1989)

 

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27.

Authors:

Usha Yadav, Nilmani Verma

Paper Title:

A Survey on Text Recognition from Natural Scene Images and Videos

Abstract: Text recognition from any natural scenes images and videos is application of image processing technique. Basically text recognition is belongs to the pattern recognition which is part of image processing techniques. Now these days text recognition from natural scene images and videos is very difficult task.For make it easy four basic steps must be apply that approaches are (i) Text image pre processing (ii) character segmentation (iii) character recognition and (iv) Text recognition. In the state of art methods , character segmentation having two major approaches that is Segmentation –based approaches which segment the text into individual character before recognizing and segmentation-free approaches which recognizes character directly from whole text images without any segmentation. Character can also be recognized with two approach that is pattern matching methods in that particular method character are usually identified by a set of features and machine learning methods in which the methods are designed that are learn automatically from the image or after extracting feature. Various method has been applied earlier for extracting text from images and videos. These all methods are trying to provide better result .Various paper use printed text images for recognition their we never required any preprocessing for extracting text . Here is the name of some methods that are used for text recognition that are are Specific directed acyclic graph techniques, scalable feature learning algorithm, k nearest neighbour technique and back propagation algorithm. All those method which has been applying for text recognition , they all provide accuracy in result or we can say that the recognized text are nearly matched with the original one.

Keywords:
Neural based OCR, Character segmentation , character recognition, Back propagation neural network model, Unsupervised learning.


References:

1. Vibhor Goel, Anand Mishra, Karteek Alahari, C. V. Jawahar. Whole is Greater than Sum of Parts: Recognizing Scene Text Words. International Conference on Document Analysis and Recognition, Aug 2013, Washington DC, United states.
2. Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, Andrew Y. Ng. Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning.International conference on document analysis and recognition 2011.

3. Saıdane, Z., Garcia, C., Dugelay, J.: The image text recognition graph (iTRG). In: International Conference on Multimedia and Expo, pp. 266–269 (2009).

4. kaoula Elagouni, Christophe Garcia ,Pascale Sébillot.A Comprehensive Neural-Based Approach for Text. rognition in Videos using Natural Language Processing. ICMR, Trento : Italy (2011).

5. Khaoula Elagouni, Christophe Garcia, Franck Mamalet, Pascale S ebillot.Combining Multi-Scale Character Recognition and Linguistic Knowledge for Natural Scene Text OCR .10th IAPR International Workshop on Document Analysis Systems 2012

6. T. Wang, D. Wu, A. Coates, and A. Ng. End-to-end text recognitionwith convolutional neural networks. In ICPR, 2012.

7. Mishra, K. Alahari, and C. V. Jawahar. Top-down and bottom-upcues for scene text recognition. In CVPR, 2012.

8. Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang,

9. D. J. Wu, and A. Y. Ng. Text detection and character recognition inscene images with unsupervised feature learning. In ICDAR, 2011.

10. Elagouni, K., Garcia, C., Sebillot, P.: A comprehensive neural-based approach for text recognition in videos using natural language processing. In: International Conference on Multimedia Retrieval (2011).

11. Khaoula Elagouni, Christophe Garcia, Franck Mamalet,Pascale Sebillot. Text Recognition in Multimedia Documents: A Study of two Neural-based OCRs Using and Avoiding Character Segmentation. International Journal on Document Analysis and Recognition, IJDAR, 2014, 17 (1), pp.19-31

12. Kopf, S., Haenselmann, T., Eelsberg, W.: Robust character recognition in low-resolution images and videos.Universitat Mannheim/Institut fur Informatik (2005)

13. Saıdane, Z., Garcia, C.: Automatic scene text recognition using a convolutional neural network. In: Conference on Computer Vision and Pattern Recognition, pp. 100–106 (2007).

14. Kusachi, Y., Suzuki, A., Ito, N., Arakawa, K.: Kanji recognition in scene images without detection of text fields robust against variation of viewpoint, contrast, and background texture. In: International Conference on Pat tern Recognition, vol. 1, pp. 457–460 (2004).

15. Chen, D., Odobez, J., Bourlard, H.: Text detection and recognition in images and video frames. Pattern Recognition 37(3), 595–608 (2004).

16. Huihuang . Zhao, Dejian. Zhou, Zhaohua. Wu. SMT Product character Recognition Based on BP Neural Network. 2010 Sixth International Conference on Natural Computation (ICNC 2010).

17. Z. Saidane and C. Garcia. Automatic scene text recognition using a convolutional neural network. In Proceedings of the Second International Workshop on Camera-Based Document Analysis and Recognition (CBDAR), Sept. 2007.T. M. Rath and R. Manmatha. Word image matching using dynamic time warping. In CVPR, 2003.

18. A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer networks an unsupervised feature learning,” in International Conference on Artificial Intelligence and Statistics, 2011.

19. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Back propagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, pp. 541–551, 1989.

20. T. Q. Phan, P. Shivakumara, B. Su, and C. L. Tan. A gradient vectorFlow based method for video character segmentation. In ICDAR, 2011.Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang,

21. D. J. Wu, and A. Y. Ng. Text detection and character recognition inscene images with unsupervised feature learning. In ICDAR, 2011.

 

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28.

Authors:

Mayank Agrawal, Manuj Mishra, Shiv Pratap Singh Kushwah

Paper Title:

Association Rules Optimization using Particle Swarm Optimization Algorithm with Mutation

Abstract: In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for solving different NP-complete problems. The basic drawback with PSO algorithm is getting trapped with local optima. So in this work, particle swarm optimization algorithm with mutation operator is used to generate high quality association rules for finding frequent item sets from large data sets. The mutation operator is used after the update phase of PSO algorithm in this work. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using PSO algorithm over these rules the system can predict the rules which contains negative attributes.

Keywords:
Particle Swarm Optimization (PSO), Mutation, Association rule, Support, Confidence, Frequent item set, Data mining.


References:

1. U. Fayyad and R. Uthurusamy, “Data Mining and Knowledge Discovery in Databases”, Communications of the ACM, vol. 39, no. 11, 1996, pp.24–34.
2. J. Han and M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann, 2006.

3. W. Soto and A. Olaya-Benavides, “A Genetic Algorithm for Discovery of Association Rules.” In Computer Science Society (SCCC), 2011, pp. 289-293.

4. X. Yan, C. Zhang and S. Zhang, “Genetic Algorithm- Based Strategy for Identifying Association Rules without Specifying Actual Minimum Support”, Expert Systems with Applications, vol. 36, 2009, pp. 3066–3076.

5. S. N. Sivanandamand and S. N. Deepa, “Introduction to Genetic Algorithms”, Springer-Verlag Berlin Heidelberg, 2008.

6. M. Anandhavalli and S. Kumar Sudhanshu, A. Kumar and M.K. Ghose, “Optimized Association Rule Mining Using Genetic Algorithm”, Advances in Information Mining, vol. 1, issue 2, 2009, pp. 01-04.

7. P. Wakabi-Waiswa and V.

Volume-5 Issue-2

Download Abstract Book

S. No

Volume-5 Issue-2, May 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Mustafa Abdulkadhim

Paper Title:

Service Performance Evaluation for WiMAX Networks Based on Node Trajectory

Abstract: WIMAX is a wireless communications standard designed to provide 30 to 40 megabit-per-second data rates, [1] with the 2011 update providing up to 1 Gbit/s [1] for fixed stations. The name “WiMAX” was created by the WiMAX Forum, which was formed in June 2001 to promote conformity and interoperability of the standard. The forum describes WiMAX as “a standards-based technology enabling the delivery of last mile wireless broadband access as an alternative to cable and DSL”. [2]. This paper aim to spot the light on how node trajectory within the WiMAX cell may effects the network performance, also how QoS parameters and the choice we make in the network configuration might changes how the network react and how it may have a direct effect on its performance.

Keywords:
component; WIMAX, QoS, Trajectory.


References:

1. K. Fazel and S. Kaiser, Multi-Carrier and Spread Spectrum Systems: From OFDM and MC-CDMA to LTE and WiMAX, 2nd Edition, John Wiley & Sons, 2008, ISBN 978-0-470-99821-2
2. M. Ergen, Mobile Broadband – Including WiMAX and LTE, Springer, NY, 2009 ISBN 978-0-387-68189-4 .

3. V.Mehta, Dr. N.Gupta “Performance Analysis of QoS Parameters for Wimax Networks” International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012

4. Sedoyeka, E “Evaluation of WiMAX QoS in a developing country’s” International Conference on environment Computer Systems and Applications (AICCSA), 2010 IEEE/ACS

5. Othman, H.R ” Performance analysis of VoIP over mobile WiMAX (IEEE 802.16e) best-effort class ” IEEE 5th Control and System Graduate Research Colloquium (ICSGRC), 2014

6. Boone, P ” Using time-of-day and location-based mobility profiles to improve scanning during handovers” IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010

 

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2.

Authors:

Muluneh L. Woldesemayat, K. D. Badgujar, Won Sangchul

Paper Title:

A Simplified Design of Space Vector Modulation for Speed and Torque Control of Induction Motor

Abstract: This paper proposes a simplified Space Vector Modulation technique which is used to control an inverter that supplies voltage to an induction motor. A simplified dynamic model of an induction motor model was also designed and voltage is supplied to it using SVM technique. A step by step design procedure with the help of matlab and Simulink made the complexity of the system simpler than existing models. This paper briefly explains design of space vector modulation technique and induction motor modeling. With the help of appropriate interfacing the design method will be used in industrial applications where the space vector modulation technique is used to achieve smooth control of speed and torque. Finally, on-line starting of the designed Induction Motor model was simulated. Moreover comparison of existing and the simplified SVM-based direct torque control method was simulated and results were shown.

Keywords:
Decoupling, Dynamic Model, Reference frame, Squirrel-cage.


References:

1. Santhi Kumar and K.Nagalinka, “Flux vector control with space vector modulation for PWM inverter fed induction motor drive,” International Journal of application or innovation in Engineering and Management (IJAIEM), vol.2, 2013.
2. http://www.ijaiem.org/volume2issue8/IJAIEM-2013-08-25-054.pdf

3. Fouad Giri, “Ac Electric Motors Control, Advanced design techniques and applications,” A John Willey and Sons, Ltd., 2013, pp. 17-31.

4. R.Krishnan, “Electric Motor Drives, Modeling Analysis and control,” Prentice Hall, pp. 188-191, 2001.

5. Andres Diaz, Roger Saltarez, Christian Rodrigues, Roberto F.Nunez, Eduardo I. Ortiz-Rivera and Jesus Gonzalez-Liorente, “Induction Motor Equivalent Circuit for Dynamic Simulation,” Electrical Machines and Drives IEEE International Conference, IEMDC, 2009, pp. 858-863.

6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5075304

7. Aleck W. Leedy, “Simulink/Matlab Dynamic Induction Motor Model for Use as A Teaching and Research Tool,” International Journal of Soft Computing and Engineering (IJSCE), vol. 3, Sep. 2013, pp. 102–107.

8. http://www.ijsce.org/attachments/File/v3i4/D1786093413.pdf

9. Mohamad H. Moradi and Pouria G. Khorasani, “A New Simulation of Induction Motor,” Australasian Universities Power Engineering Conference, (AUPEC), 2008, pp. 1-6.

10. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4812992

11. Burak Ozpineci and Leon M. Tolbert, “Simulink Implementation of Induction Machines-A Modular Approach,” IEEE, 2003, pp. 728-734.

12. http://web.eecs.utk.edu/~tolbert/publications/iemdc_2003_im_model.pdf

13. Adel Aktaibi and Daw Ghanim, “Dynamic Simulation of a Three-Phase Induction Motor Using Matlab Simulink (unpublished work style),” unpublished. file:///C:/Users/Muluneh/Downloads/Dynamic%20simulation%20of%20a%20three%20phase%20induction%20motor.pdf

14. Anjana Manuel and Jebin Francis, “ Simulation of direct torque controlled induction motor drive by using space vector pulse width modulation for torque ripple reduction,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 2013, pp. 4471-4478.

15. http://www.ijareeie.com/upload/2013/september/34_Simulation.pdf

16. Implementing Space Vector Modulation with ADMCF32X, ANF32X-17, Analog Devices Inc., pp.1-12, 2000.

17. ftp://ftp.analog.com/pub/www/marketSolutions/motorControl/applicationCode/admcf32x/pdf/svpwm.pdf

18. Kwang Hee Nam, “Ac Motor Control and Electric Vehicle Application,” CRC Press, 2010.

19. Rohit Chandan, “Three phase space vector pulse width modulation using generalized multiphase space vector approach (unpublished style),” unpublished.

20. http://kr.mathworks.com/matlabcentral/fileexchange/44343-three-phase-svpwm/content//ph3_svmp.m

21. Jose Andres Santisteban, and Richard M. Stephan, “Vector control Methods for Induction Machines: An overview,” IEEE Transactions on Education, Vol. 44, No. 2, 2001, pp. 170-175.

22. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=925828

23. E. Mageswari, Yuvaleela. , M.Rajeshwari and P. Amuthithini, “Implementation of Low-Cost Direct Torque Control Algorithm for Induction Motor without AC Phase Current Sensors,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, (IJAREEIE), Vol.3, October 2014, pp. 12587-12593.

24. http://www.ijareeie.com/upload/2014/october/22T_Implementation.pdf

 

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3.

Authors:

Alanoud Al Mazroa, Mohammed Arozullah

Paper Title:

Detection and Remediation of Attack by Fake Base Stations in LTE Networks

Abstract: Rogue base station attack can compromise the privacy of user equipment (UE) in LTE networks. To address this issue, we propose a rogue base station identification protocol to protect UE privacy. Our protocol utilizes the mobile property of the UE and is designed based on the observation that rogue base station can only cover a small area. We use the measurements of UE in different locations to estimate the power and location of the base stations. The UE also tracks the signatures of each legitimate base station. If the base station is already verified by the detection protocol, then the UE connects to the base station according to LTE standard. For any new appearing base stations, it sends the power of the base station and the GPS location itself to a cloud server to verify the legitimacy of the base station. The cloud server maintains a database of real base stations. Our proposed protocol does not need to change existing LTE standard and no base station modification is required. Our protocol is implemented on NS3 LTE module and evaluated with various practical settings. The results indicate our protocol can ensure that the UE can successfully detect rogue base stations and avoid sending privacy data to rogue base station.

Keywords:
user equipment (UE) in LTE networks, identification protocol to protect UE privacy, GPS, legitimacy, NS3 LTE module.


References:

1. M. Arapinis, L. Mancini, E. Ritter, M. Ryan, N. Golde, K. Redon, and R. Borgaonkar. New privacy issues in mobile telephony: fix and verification. In Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 2012.
2. C.-M. Chen, Y.-H. Chen, Y.-H. Lin, and H.-M.

3. Sun. Eliminating rouge femto cells based on distance bounding protocol and geographic information. Expert Systems with Applications, 2014.

4. J. A. Del Peral-Rosado, J. A. Lopez-Salcedo, G. Seco-Granados, F. Zanier, and M. Crisci. Analysis of positioning capabilities of 3gpp lte. In Proceedings of the ION GNSS, pages 1–10, 2012.

5. N. Golde, K. Redon, and R. Borgaonkar.

6. Weaponizing femto cells: The effect of rogue devices on mobile telecommunications. In NDSS, 2012.

7. C.-K. Han, H.-K. Choi, and I.-H. Kim. Building femtocell more secure with improved proxy signature. In Global Telecommunications Conference, 2009.
GLOBECOM 2009. IEEE. IEEE, 2009.

8. N. K. M. Mishra Sandip D. False base station attack in gsm network environment. In International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), 2014.

9. D. Peral-Rosado, J. A. Lopez-Salcedo,

10. G. Seco-Granados, F. Zanier, M. Crisci, et al. Achievable localization accuracy of the positioning reference signal of 3gpp lte. In Localization and GNSS (ICL-GNSS), 2012 International Conference on, pages 1–6. IEEE, 2012.

11. Y. Song, K. Zhou, and X. Chen. Fake bts attacks of gsm system on software radio platform. Journal of Networks, 2012.

12. D. Strobel. Imsi catcher. 2007.

 

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4.

Authors:

Ekta Gupta, Shrisha Vanga, Sonal Kachare, Jay Borade

Paper Title:

Fashion Accessories using Virtual Mirror

Abstract: In this paper, we are comparing various technologies for e-business trying to create a Virtual Mirror made up by a large digital screen like a computer monitor, a sensory device like a webcam. The system will work in the following way: When a person enters the field of view of the camera, the camera will start capturing the image of the person, and the algorithm of the system will start tracking the image of the person in order to recognize various body feature of that person. Each new person entering the camera’s field of view will trigger the computer to track the movements of the person. Thereafter, whenever the person selects an item from the shopping list, the item will be directly placed on that particular body part. This way, the person can try out different items and buy those items that suit him/her.

Keywords:
Virtual Mirror (VR), Try It Online (TIO), Camera (CAM), Webcam Social Shopper (WSS), Virtual Mirror Application (VMA), Graphic User Interface (GUI), Fitting Box(FB).


References:

1. M. Usman, K. Sporsheim, E. Bergstøl, I. Milanovic, R. Hercz, “The Virtual Mirror” , Human-Computer-Interface course, University of Oslo, pp. 6-7, 2007.
2. Tortoise and Blonde, ” The Tortoise and Blonde Experience”, http://tortoiseandblonde.com/about-us.

3. SiliconIndia, “LensKart.com Launches A Virtual Mirror For Eye Gear”, http://www.siliconindia.com/shownews/LensKartcom_Launches_A_Virtua l_Mirror_For_Eye_Gear-nid-100662-cid-100.html, 2011.

4. Zugara, ”Our Story”, http://zugara.com/about-zugara/zugaras-story. 2008.

5. Radar, “Virtual Shopping Made Easy with Zugara’s Fashionista App”, http://www.ladylux.com/articles/virtual-shopping-made-easy-with-Zugaras -fashionista-app. 2009.

6. D.Adams, “Augmented Reality Virtual Mirror: Try Before You Buy Online”,http://www.bitrebels.com/technology/augmented-reality-virtual-mi rror-try-before
you-buy-online.

7. EZface,”EZface Virtual Mirror”,

8. http://www.info4u.com.br/index.php/videos.

9. EZface,”EZface Virtual Mirror Application”, http://staging.ezface.com/about-us.html

10. FrameFish virtual try-on software, “Better Virtual Try-On Solutions For Eyewear”, http://virtualmirror.framefish.com/features.

11. Virtual Try On Solutions for Glasses, “TURNKEY SOLUTIONS FOR THE EYEWEAR AND EYECARE INDUSTRIES” http://www.trylive.com/solutions/trylive-eyewear-virtual-mirror/overview.

12. C.O’Brien, “Ray-Ban Uses Augmented Reality For Their Virtual Mirror”,http://thefutureofads.com/ray-ban-uses-augmented-reality-for-their -virtual-mirror.

13. Innovation Offerings, “Magic Mirror Technology”, https://www.etpl.sg/innovation-offerings/technologies-for-license/tech-offe rs/1911.

 

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5.

Authors:

Mustafa Tunay, Rahib H. Abiyev

Paper Title:

Hybrid Local Search Based Genetic Algorithm and its Practical Application

Abstract: This paper presents an intense hybrid search method that uses Genetic Algorithms (GAs) and local search procedure for global optimization. The Genetic Algorithms (GAs) comprise a selection process, a crossover process and a mutation processes and local search procedure that uses Powell’s method for updating the parameters of the objective functions. The performance of the designed algorithm is tested on specific benchmarking functions namely; Rastrigin function, Rosenbrock function, Schwefel’s function 2.22, Schwefel’s function 2.21 and Sphere’s function. The computational results have demonstrated that the performance of Genetic Algorithms with Powell’s Method is much improved specific benchmarking functions. The use of a hybrid search method approach allows it to speed up the learning of the system with faster convergence rates. The Genetic Algorithm with Local Search Procedure (GALSP) is applied for soling exam timetabling problem. The GALSP seems to be a promising approach and is comparable to specialized algorithm for solving a set of global optimization problems. The algorithms of these processes have been designed and presented in the paper.

Keywords:
Genetic algorithms, local search procedure, evolutionary theory, search methods.


References:

1. Abiyev Rahib H. (2009). Fuzzy Wavelet Neural Network for Prediction of Electricity Consumption. AIEDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing , 23(2) 109-118.
2. Abiyev Rahib H., Tunay M. (2015). Optimization of High Dimensional Functions through Hypercube Evaluation, Computational Intelligence and Neuroscience, Volume 2015, 2015.

3. Burke E. K, Elliman DG, Weare RF. ‘‘A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems,’’ 6th International Conference on Genetic Algorithms (July 1995,Pittsburgh, USA), 15-19.

4. Burke E. K, Newall JP.‘‘A multistafe Evolutionary Algorithm for Timetable Problem’’ Trans. On Evolutionary Computation 1999, 3(1).

5. Eiben A. E, Smith JE. Introduction to evolutionary computing. Springer 2003, New York.

6. Goldberg D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading MA

7. Hansen N, Auger A, Ros R, Finck S, Posik P. Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, in Proceedings of the 12th International Conference on Genetic and Evolutionary Computation Conference (GECCO’10), Companion Material, Portland, ed. by M. Pelikan, J. Branke (ACM, New York, 2010), pp. 1689–1696.

8. Hansen N, Kern S. Evaluating the CMA evolution strategy on multimodal test functions, in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN VIII), Birmingham. Volume 3242 of Lecture Notes in Computer Science, ed. by X. Yao et al. (Springer, 2004), pp. 282–291.

9. Holland J. H. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.

10. Kalayci C. B., Gungor A. A Genetic Algorithm Based Examination Timetabling Model Focusing on Student Success for the Case of the College of Engineering at Pamukkale University, Turkey. Gazi University Journal of Science, 25(1) (2012) 137-153.
11. Karaboga D., Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing , 687–697.
12. Kennedy J. (1997). The particle swarm: social adaptation of knowledge, Proc. IEEE International Conference on Evolutionary Computation (Indianapolis, Indiana), IEEE Service Center 1997, Piscataway, NJ, 303–308.

13. Kramer O., Ciaurri D. E, Koziel S. (2011). Derivative-free optimization, in Computational Optimization and Applications in Engineering and Industry, Studies in Computational Intelligence (Springer, New York, 2011). pp. 61–83.

14. Lee K. Y., El-Sharkawi MA. Modern heuristic optimization techniques: theory and applications to power systems. Wiley 2008, New York.

15. Michalewicz Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag 1996, 3rd edition, New York.

16. Moscato P. Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill 1999, New York.

17. Mukhopadhyay D., Maulik U., Bandyopadhyay S., and Coello Coello, CA. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I. IEEE Trans. On Evolutionary Computation 2014, 18(1).

18. Naji-Azimi Z. Comparison of metaheuristic algorithms for examination timetabling problem. Applied Mathematics and Computation 2004, 16(1-2) 337-354.

19. Ostermeier A., Gawelczyk A, Hansen N. A derandomized approach to self-adaptation of evolution strategies. Evol. Comput. 1994, 2(4), 369–380.

20. Pohlheim H. (December 2006). Genetic and Evolutionary Algorithm. Retrieved from website: http://www.Geatbx.com.

21. Rothlauf F. Representations for genetic and evolutionary algorithms. Springer 2006, Berlin.

22. Sudholt D. On the analysis of the (1+1) memetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 493–500. ACM Press 2006, New York

23. Vaisakh K, Srinivas LR, Meah K. Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis. Applied Softcomputing, 13, 4579-4593.

24. Zhu G., Kwong S. Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization, 2010.

 

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6.

Authors:

M. Manojkumar, R. Venkateshwaran, S. Yukesh, S. Rajiv Gandhi

Paper Title:

Risk Management on Construction Project

Abstract: There is an increase in the number of construction project experiencing extensive risks leading to exceeding the initial time and cost budget. Managing risks in construction project has been recognized as a very important management process in order to achieve the project objectives in terms of time, cost, quality, safety and environmental sustainability. To implement construction project, a proper planning and scheduling is of vital important in order for the project to be executed and run smoothly. The software tool Microsoft Project Planner is used for planning, scheduling and controlling. The activities required to complete the building structure excluding the interiors was identified and was fed as an input to Microsoft along with their durations. We have made a study about the risk management and identified the risk involved in the building and found out the causes for the risk occurred throughout the construction work. Finally we have provided the suggestions to avoid that risk. Apart from that we have made a study of journals about risk management on various buildings (hospital, commercial, etc.) and listed out the risk occurring during construction work and provided solution to avoid those risks.

Keywords:
Microsoft Project, (hospital, commercial, etc.), software, management


References:

1. Cost And Schedule-Control Integration Issues And Needs:by William J.Rasdorf and Osama Y.Abudayyeh
2. A Risk Management System For Preconstruction Phases Of Large Scale Development Projects In Developing Countries by Mohammad Baydoun, Project Manager, Millennium Development International & DBA Candidate, Grenoble Ecole de Management.

3. Construction Delays Causing Risk On Time And Cost- A Critical Review by Chidambaram Ramanathan, SP Narayanan and Arazi B Idrus.

4. Risk Management in Construction Project Management by Martin Schieg.

5. Methodology of Risk And Uncertainity Management In Construction’s Technological And Economical Problems by Darius Migilinskas, LeonasUstinovicius

6. Risk Management In Building Projects by AdanEnshassi, Jaser Abu Mosa

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7.

Authors:

K. Joshil Raj, S Siva Sathya, Kalyan Nandi

Paper Title:

A Modified Group Search Optimizer for Feature Selection and Parameter Determination of SVM

Abstract: Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Group Search Optimizer (GSO) is a new population based optimization algorithm inspired by animal searching behavior for developing optimum searching strategies to find out solutions for continuous optimization problems. This paper presents an experimental analysis of modifications to classical GSO & studies its effects on a GSO-SVM hybrid combination for feature selection and kernel parameters optimization. In the proposed algorithm, three modifications are introduced over classical GSO to improve its global search mechanism. The quality and effectiveness of the proposed methodology has been evaluated on standard machine learning datasets.

Keywords:
Evolutionary algorithm; Group Search Optimizer; GSO; Support Vector Machine; Machine learning; Feature Selection; Kernel parameters.


References:

1. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining. Pearson Education Inc., 2006..
2. Han, J., and Kamber, M., Data Mining Concepts and Techniques, Elsevier Inc., Second Edition, 2006.

3. Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, “Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood,” Journal of Machine Learning Research 12 , pp. 2181-2210,2011.

4. H. Witten, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, 2005.

5. Keller, J. M. , Gray, M. R. , Givens JR. , J. A. , A Fuzzy K-Nearest Neighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics, v. SMC-15, n. 4, pp. 258-260, 1985.

6. Kenneth A. De Jong, Evolutionary Computation A Unified Approach. The MIT Press, Cambridge, England, 2006

7. Marco Dorigo and Thomas Stützle, Ant Colony Optimization, The MIT Press Cambridge, England, 2004.

8. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948,1995.

9. Keerthi, S. S., & Lin, C.-J., Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation, 15, pp.1667–1689, 2003.

10. K. Joshil Raj, S Siva Sathya, Kalyan Nandi, GSO-SVM for Feature Selection and kernel Parameter Optimization, International Journal of Advanced Information Science and Technology(IJAIST), ISSN: 2319 – 2682, pp. 33-39, 2014.

11. L. Zhang, L.B. Jack, A.K. Nandi, Fault detection using genetic programming, Elsevier, Mechanical Systems and Signal Processing. 19 , pp. 271–289,2005.

12. P.-F. Pai, W.-C. Hong, Software reliability forecasting by support vector machines with simulated annealing algorithms, Elsevier Inc, The Journal of Systems and Software 79 pp.747–755, 2006.

13. Felipe Alonso-Atienza, José Luis Rojo-Álvarez, Alfredo Rosado-Muñoz, Juan J. Vinagre, Arcadi García-Alberola & Gustavo Camps-Valls, Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection, Elsevier Ltd, Expert Systems with Applications 39,pp. 1956–1967, 2012.

14. Yukun Bao, ZhongyiHu & TaoXiong, A PSO and pattern search based memetic algorithm for SVMs parameters optimization, Elsevier B.V, Neurocomputing 117, pp. 98–106, 2013.

15. Vapnik, V.N, “The Nature of Statistical Learning Theory”, Springer, Berlin Heidelberg New York ,1995.

16. S. He, Q. H. Wu, and J. R. Saunders, “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior,” IEEE Transactions On Evolutionary Computation, vol. 13,no. 5, pp. 973-990, 2009.

17. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer,,Peter Reutemann, Ian H. Witten; The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1,2009.

18. Lichman, M., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science,2013.

 

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8.

Authors:

G.Sai Manoj, T.Sreevatsav, V.Vidya Priyanka, S.V.K.S. Prasad, P.Rajesh

Paper Title:

An Area Efficient Low Power High Speed Pulse Triggered Flip Flop Using Pass Transistor

Abstract: The performance of flip-flop is an important element to determine the efficiency of the whole synchronous circuit. This paper presents an efficient explicit pulsed static single edge triggered flip flop with an improved performance and overcomes the drawbacks of the implicit type pulsed flip flops. The proposed flip flop is having a structure of explicit pulse-triggered with a modified true single phase clock latch based on signal feed through scheme. The proposed flip-flop is compared with existing explicit pulsed single edge triggered flip-flops in terms of power, speed and area. Simulation results based on PTM 90nm CMOS technology reveal that the proposed design features the best power, area and delay performance in several FF designs under comparison.

Keywords:
Explicit, Edge-Triggered, Feed through, Latch, Synchronous


References:

1. H. Kawaguchi and T. Sakurai, “A reduced clock-swing flip-flop (RCSFF) for 63% power reduction,” IEEE J. Solid-State Circuits, vol. 33, no. 5, pp. 807–811, May 1998.
2. K. Chen, “A 77% energy saving 22-transistor single phase clocking D-flip-flop with adoptive-coupling configuration in 40 nm CMOS,” in Proc. IEEE Int. Solid-State Circuits Conf., Nov. 2011, pp. 338–339.

3. V. Stojanovic and V. Oklobdzija, “Comparative analysis of masterslave latches and flip-flops for high-performance and low-power systems,” IEEE J. Solid-State Circuits, vol. 34, no. 4, pp. 536–548, Apr. 1999.

4. J. Tschanz, S. Narendra, Z. Chen, S. Borkar, M. Sachdev, and V. De, “Comparative delay and energy of single edge-triggered and dual edge triggered pulsed flip-flops for high-performance microprocessors,” in Proc. ISPLED, 2001, pp. 207–212.

5. F. Klass, C. Amir, A. Das, K. Aingaran, C. Truong, R. Wang, A. Mehta, R. Heald, and G. Yee, “A new family of semi-dynamic and dynamic flip-flops with embedded logic for high-performance processors,” IEEE J. Solid-State Circuits, vol. 34, no. 5, pp. 712–716, May 1999.

6. M. Alioto, E. Consoli, and G. Palumbo, “General strategies to design nanometer flip-flops in the energy-delay space,” IEEE Trans. Circuits Syst., vol. 57, no. 7, pp. 1583–1596, Jul. 2010.

7. B. Kong, S. Kim, and Y. Jun, “Conditional-capture flip-flop for statistical power reduction,” IEEE J. Solid-State Circuits, vol. 36, no. 8, pp. 1263–1271, Aug. 2001.

8. N. Nedovic, M. Aleksic, and V. G. Oklobdzija, “Conditional precharge techniques for power-efficient dual-edge clocking,” in Proc. Int. Symp. Low-Power Electron. Design, Aug. 2002, pp. 56–59.

9. P. Zhao, T. Darwish, and M. Bayoumi, “High-performance and low power conditional discharge flip-flop,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 12, no. 5, pp. 477–484, May 2004.

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9.

Authors:

Hussein Jaddu, Amjad Majdalawi

Paper Title:

Recursive Approximation Method for Solving Constrained Nonlinear Optimal Control Problems Using Legendre Polynomials

Abstract: A computational method is presented to solve a nonlinear quadratic optimal control problems subject to terminal state constraints, path inequality constraints on both the state and the control variables. The method is based on using a recursive approximation technique to replace the original constrained nonlinear dynamic system by a sequence of constrained linear time-varying systems. Then each of constrained linear time-varying quadratic optimal control problems is approximated by a quadratic programming problem by parameterizing each of the state variable by a finite length Legendre polynomials with unknown parameters. To show the effectiveness of the proposed method, simulation results of two constrained nonlinear optimal control problems are presented.

Keywords:
Nonlinear constrained quadratic optimal control problem; Iterative Technique; Legendre polynomials; State parameterization.


References:

1. J. Vlassenbroeck, A Chebyshev polynomial method for optimal control with state constraints, Automatica 24 (1988) 499–506.
2. P. A. Frick and D. J. Stech, “Epsilon-Ritz method for solving optimal control problems: Useful parallel solution method”, Journal of Optimization Theory and Applications, vol. 79, no. 1, (1993), pp. 31-58.

3. C. J. Goh and K. L. Teo, “Control parameterization: A unified approach to optimal control problems with general constraints”, Automatica, vol. 24, no. 1, (1988), pp 3-18.

4. Troch I., F. Breitenecker and M. Graeff, Computing optimal controls for system with state and control constraints, IFAC Control Applications of Nonlinear Programming and Optimization, France, pp. 39-44, 1989.

5. H. Jaddu, “Numerical methods for solving optimal control problems using Chebyshev polynomials”, PHD Thesis (1998).

6. H. Jaddu, “Direct solution of nonlinear optimal control problems using quasilinearization and Chebyshev polynomials”, Journal of the Franklin Institute,vol. 339, no. 4, (2002), pp 479-498.

7. H. Jaddu and M. Vlach, “Successive approximation method for non-linear optimal control problems with applications to a container crane problem”, Optimal Control Applications and Methods, vol. 23, no. 5, (2002), pp 275-288.

8. Cimen T., Banks S. P.,” Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria”, Systems & Control Letters, Vol 53, PP. 327 – 346, 2004

9. Cimen T., Banks S. P., ” Nonlinear optimal tracking control with application to super-tankers for autopilot design”, Automatica, Vol. 40, PP. 1845 – 1863, 2004.

10. Topputo F., Bernelli-Zazzera F., ” Approximate Solutions to Nonlinear Optimal Control Problems in Astrodynamics”, ISRN Aerospace Engineering, Vol. 2013, 2013

11. S. P. Banks and K. Dinesh, “Approximate optimal control and stability of nonlinear finite and infinite-dimensional systems”, Annals of Operations Research, vol. 98, (2000), pp. 19-44.

12. H. Jaddu and A. Majdalawi, ” Legendre Polynomials Iterative Technique for Solving A Class of Nonlinear Optimal Control Problems,” International Journal of Control and Automation, vol. 7, pp. 17-28, 2014

13. W. Bell, “Special Functions For Scientist and Engineers”, D. Van Nostrand Company, London, (1968).

14. Neuman C. P. and A. Sen, A suboptimal control algorithm for constrained problems using cubic splines, Automatica, Vol. 9, pp. 601-613, 1973.

15. Fegley K., S. Blum, J. Bergholm, A. Calise, J. Marowitz, G. Porcelli and L. Sinha, Stochastic and deterministic design and control via linear and quadratic
programming, IEEE Trans. Automatic Control, Vol. AC-16-6, pp. 759-766, 1971.

16. J. T. Betts, “Practical Methods for Optimal Control using Nonlinear Programming”, SIAM, Philadelphia, (2001).

17. G. Bashein and M. Enns, “Computation of optimal control by a method combining quasi-linearization and quadratic programming”, International Journal of Control, vol. 16, no. 1, (1972), pp. 177-187.

18. Sakawa Y. and Y. Shindo, Optimal control of container cranes, Automatica, Vol.18-3, pp. 257-266, 1982.

19. Teo K., C. Goh and K. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific & Technical, England, 1991.

20. Jaddu, H., M. Vlach, Successive approximation method for non-linear optimal control problems with applications to a container crane problem, Optimal Control Applications and Methods, Vol. 23, pp. 275-288, 2002.

21. Jaddu H. and A. Majdalawi, ” An Iterative method for Solving the Container Crane Constrained Optimal Control Problem Using Chebyshev Polynomials”, International Journal of Emerging Technology and Advanced Engineering, Vol. 5, pp. 344-351, 2015

 

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10.

Authors:

Aivars Helde

Paper Title:

Advertise with Social Discourse, as a Brand Positioning Technique: Review of Reseach with Special Reference to the Latvian Media

Abstract: This study examines the nature of the social discourse of advertising used as a brend positioning discourse. The focus is on consumer advertising, which is directed towards the promotion of some product or service to the general public. The study, however, is not meant to exhaust all the aspects of this particular discourse, or present an answer to all the problems it poses. This paper aimed at analyzing some different comercial advertisements (product/non-product ads) to investigate the intentions and techniques of consumer product companies to reach more consumers and sell more products. Norman Fairclough‟s 3-D model and Kress and van Leeuwen‟s grammar of visual design were used to analyze the data for professionals, but we are pointed on using stereotypes.Tradicionally,stereotypes are defined as patterns or shemes via which peopleorganise their behaviors and activities.Psychologists have been extremely interested in the persuasion techniques used by advertisers. The implicite question that most of such studies have entertained is whether advertising has becom aforce molding cultural mores and individual behaviors,or whether it constitutes no more than a”mirror”of deeper cultural tendencies within urbanaized contemporary society.The one thing which evryone agrees is that advertising has become one of the most recognizable and appealing forms of aocial communication to which evryone in sociecty is exposed. However, it could be understood from the results that the producers, generally tend to use their power and ideology to change people’s behaviour and thought. Some time more efficiently is used” old” stereotypes and do not try to going to change people’s behaviuor but do conversaly use their power to preserve previous behaviour try to reinforces this behaviour,shown this like some tradicional value what confirmed customers identity. When we consider gender stereotypes we look at notinos about the supposedly traditional behaviours of men and women and the characteristics and standardsof this behaviours,as grounded in our culture and society. This idea allows to producers make customer feel belonging to this society and psychologically be involved into story what is shown by advertisers. Culture covershuman values,action patterns,ideas,and material and artificial surraunding which enable interaction among people.The content of culturedetermines the particular qualities of certain groups of people,and it also determines their consumer characteristics.That is why it is essential to understand the way in witch culture reaches individuals.In today’s information area,the media are the primary means for the transmission and reproduction of cultural information.They shape the image of culture in people’s consciousness. In addition this study provides analyses of some ads, using different ways of interpretations. All materials are taken from Latvian media.

Keywords:
Social discourse analysis, stereotypes brend , customer behaviour, print advertisement, Image, Fairclough-3D, Krees and van Leeuwen’s grammer, Gestalt psychology,culture.


References:

1. Al Ries.,Jack Trout – „Positioning: the battle for Your mind”- Mc Graw-Hill ,2001.
2. D.Meerman Scott -„The new rules of marketing &Pr”-Wiley,2010.

3. Ferrell.O.C.;M.D.Hartline – „Marketing Strategy Text and Cases”South-Western Cengage Learning ,2014

4. Hohlova P.-Finansovaja reklama.Kak eta delajut v Britanii”:M,:Media Mir, 2007

5. E.A.Pesockii -”Reklamai motivācija potrebitelei”,Moskva,2009.

6. A.Lebedev-Ljubimov –„Psihologija reklami”. Piter 2003.

7. Lails E. Borns, Nensija F.Ruso-„Psiholoģija” Raka,2001.

8. Harald Business Review September 2013-„Seven steps to strategy making”

9. International Journal Of Linguistics, ISSN 1948-5425,2012,Vol4,No.4

10. Cook,.G the discurse of advertising.London and NY: Routledge,1992.

11. Wodak R. –„ Gender and discourse”Sage Publications , 1997.

12. Teun A. Van Dijk-„Discourse and manipulation”, Discurse and society Sage,2006.

13. Kelly, Aidan, Katrina Lawlor, and Stephanie O’Donohoe. “Chapter 8- Encoding Advertisements: The Creative Perspective.” The Advertising and Consumer Culture Reader. By Joseph Turow and Matthew P. McAllister. New York: Routledge, 2009.

14. Hall, Stuart. Encoding /Decoding, in Culture, Media, Language.London, Hutchinson 1980.

15. Kress G.,and Van Leeuwen T.Reding images-Victoria, Deakin Univesity Press ,1990.

16. http://www.academia.edu/2645766/Critical_Discourse_Analysis_and_Faircloughs_Model-_Mirzaee_and_Hamidi_2012_

17. http://www.uv.es/gimenez/Recursos/criticaldiscourse.pdf

 

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11.

Authors:

Anil Kumar, Vijay Mittal

Paper Title:

Recursive Approximation Method for Solving Constrained Nonlinear Optimal Control Problems Using Legendre Polynomials

Abstract: A new approach for the implementation of quality philosophy Zero Quality Defects with usage of the Poka-Yoke method in the Assembly Line has been presented. The possibility of usage of mistake proofing device is connected with monitoring and improvement of operations in the process. The Poka-Yoke method of preventing errors by putting limits on how operation can be performed in order to force the correct completion of the operation has been presented. The possibility of implementing of the Poka-Yoke method as a factor of improving operation in the process in the assembly line has been shown. The aim of method Poka-Yoke in those practical examples is to eliminate or minimizes human error in manufacturing process and management as a result of mental and physical human imperfections.

Keywords:
Lever Combination Switch; Mistake proofing; Poka Yoke ; Mindarika Company Limited


References:

1. M. Dudek-Burlikowska, D. Szewieczek, Vol-36, Issue 1, (2009), “The Poka-Yoke method as an improving quality tool of operations in the process”, Journals of achievements in Materials and manufacturing. pp 95-102
2. Arash Shahin and Maryam Ghasemaghaei, Vol. 2, No.-2; November 2010, “Service Poka Yoke”, International Journal of Marketing Studies. pp 190-201.

3. Zero Quality Control: Source Inspection and the Poka-Yoke System, Shigeo Shingo, Cambridge Productivity Press, 1986.

4. Ramin Sadri, Pouya Taheri, Pejman Azarsa and Hedayat Ghavam, vol.5 (2011), “Improving Productivity through Mistake-proofing of Construction Processes”, International Conference on Intelligent Building and Management, pp 280-284.

5. www.mindagroup.com

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12.

Authors:

Valliappan Raman, Sundresan Perumal

Paper Title:

Matlab Implementation Results: Detection and Counting of Young Larvae and Juvenile by Image Enhancement and Region Growing Segmentation Approach

Abstract: This paper describes techniques to perform efficient and accurate recognition in larvae images captured from the hatcheries for counting the live and dead larvae’s. In order to accurately model small, irregularly shaped larvae and juvenile, the larvae images are enhanced by three enhancement methods, and segmentation of larvae and juvenile is performed by orientation associated with each edge pixel of region growing segmentation method. The two vital tasks in image analysis are recognition and extraction of larvae and juvenile from an image. When these tasks are manually performed, it calls for human experts, making them more time consuming, more expensive and highly constrained. These negative factors led to the development of various computer systems performing an automatic recognition and extraction of visual information to bring consistency, efficiency and accuracy in image analysis. This main objective of this paper is to study on the various existing automated approaches for recognition and extraction of objects from an image in various scientific and engineering applications. In this study, a categorization is made based on the four principle factors (Input, Segment the larvae, Recognition, Counting) with which each approach is drive .The achieved result of recognition and classification of larvae is around 85%. All the results achieved through matlab implementation are discussed in this paper are proved to work efficiently in real environment.

Keywords:
Enhancement, Segmentation and Counting


References:

1. Official Portal: Department of Fisheries Malaysia,” Department of Fisheries Malaysia, http://www.dof.gov.my/59, 2011.
2. D.J. White, C. Svellingen, N.J.C. Strachan, Automated measurement of species and length of fish by computer vision, Fisheries Research 80 (2006) 203-210.

3. R.N.Williams, T.J.L ambert, A.F. Kelsall, T. Pauly, Detecting marine animals in underwater video: Let’s start with salmon, in: Proceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico,2006.

4. Boles, W.W,Geva, S,Busch, A, “An image processing approach for estimating the number of live prawn larvae in water” in Signal Processing and Its Applications, 1999, Brisbane, Australia.

5. D.J. Lee, R. Schoenberger, D. Shiozawa, X.Q. Xu, P.C.Zhan, Contour matching for a fish recognition and migration monitoring system, SPIE Optics East, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II 56 (2004).

6. J. Broersen, “Towards a detection and recognition system for freshwater fish,” MSc report, 2009.

7. H. de Pontuala, R. Roberta, b, P. Minerb, ” Study of bivalve larval growth using image processing” in Journal of Aquaculture Engineering, Elsevier, Vol 17, Issue 2, 1998.

8. Johnson I, Harman M, Forrow D, Norris M, “An assessment of the feasibility of using image analysis in the oyster embryo-larval development test” PubMed , Environ Toxicol, 16(1):68-77, 2001.

9. Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: ECCV (2), 2002.

10. Felzenszwalb, P.F., Huttenlocher, D.P, “Pictorial structures for object recognition”, International Journal of Computer Vision 61(1), 2005.

11. Belongie, S., Malik, J., Puzicha, J.” Shape matching and object recognition using shape contexts”, IEEE Trans. Pattern Anal. Mach. Intell. 24(4) 2002

12. J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679–698, 19.

13. D. de Ridder, P.W. Verbeek R.P.W. Duin, and L.J. van Vliet. The applicability of neural networks to non-linear image processing. Pattern Analysis and Applications, 2:111–128, 1999.

14. L.J. Latecki, R. Lakamper, and U. Eckhardt. Shape descriptors for non-rigid shapes with a single closed contour. IEEE Conf. on Computer Vision and Pattern Recognition, 5:424 429, 2000.

15. L.A. Torres-Mendez, J.C. Ruiz-Suarez, L.E. Sucar, and G. Gomez. Translation,rotation, and scale-invariant object recognition. IEEE Transactions on Systems, Man and Cybernetics, 30:125–130, February 2000.

16. Valliappan Raman, Brian Loh and Patrick Then,” First Prototype of Aquatic ToolKit: Towards Low-cost Intelligent Larval Fish Counting in Hatcheries” Proceedings of 9th International Conference on Pervasive Intelligence and Computing (PICom2011), Sydney, Australia, Pp: 193-195, Dec.12-14, 2011.

 

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13.

Authors:

Navdeep Sharma, Sameer Sharma, S.P. Guleria, N.K. Batra

Paper Title:

Mechanical Properties of Urea Formaldehyde Resin Composites Reinforced with Bamboo, Coconut and Glass Fibers

Abstract: Composite materials, plastics and ceramics have been the dominant emerging materials from last thirty years. Polymeric materials reinforced with natural and synthetic fibres such as coconut, bamboo, jute glass, carbon and aramid provide advantages of high stiffness, good thermal, acoustic insulating properties, excellent formability and strength to weight ratio as compared to conventional construction materials, i.e. wood, concrete, iron and steel. The increase interest in using natural fibres as reinforcement in plastics is to substitute the conventional synthetic fibres in some structural applications and it has become one of the main concerns to study the potential of using natural fibres as reinforcement for polymers. In this research paper, seven different fiber reinforcement polymer composite were fabricated by wet hand-lay-up method using short coconut, short bamboo and short glass fibers binded with amino resin like urea formaldehyde. The urea formaldehyde was selected due to its low cost, less weight, easier to field fabricate, long durability and high temperature withstand ability. The different mechanical properties like density, tensile strength, hardness, flexural strength and percentage elongation of specimens were calculated and were compared with the pure urea formaldehyde.

Keywords:
Composite, Polymeric materials, coconut, bamboo, glass fibers, urea formaldehyde.


References:

1. H. Hishi , M. Yoshioka , A. Yamanoi , N. Shiraishi , Mokuzai Gakkaishi, “Studies on composites from wood and polypropylenes (I)”, Vol. 34(2), 133–9, 1998.
2. F. Febrianto , M. Yoshioka , Y. Nagai , M. Mihara , N. Shiraishi , “Composites of wood and trans-1,4-isoprene rubber I: mechanical, physical, and flow behaviour”, Journal of Wood Science, Vol.45(1) , pp.38–45, 1999.

3. F. Febrianto, M. Yoshioka, Y. Nagai, M. Mihara, N. Shiraishi, “Composites for wood and trans-1,4-isoprene rubber II: processing conditions for production of the composites”, Wood Science and Technology, Vol.35, pp. 297–310, 2001.

4. M.M. Sain, B.V. Kokata, D. Maldas, “Effect of reactive additives on the performance cellulose fiber-filled polypropylene composities”, Journal of Adhesive Science and Technology, Vol.7(1) , pp.49–61, 1993.

5. S. Takase, N. Shiraishi, “Studies on composites from wood and polypropylenes (II)”, Journal of Applied Polymer Science, Vol.37, pp. 59-645, 1989.

6. S. Mishra, J.B. Naik, “Absorption of water at ambient temperature and steam in wood-polymer composites prepared from agrowaste and polystyrene”, Journal
of Applied Polymer Science, Vol.68, pp.6-681, 1998.

7. J. Simonsen , R. Jacobsen , R. Rowell , “Wood-fiber reinforcement of styrene-maleic anhydride copolymers”, Journal of Applied Polymer Science, Vol.68, pp.1567–73,1998.

8. B. Xu , J. Simonsen , W.E. Rochefort , “Creep resistance of wood-filled polystyrene/high-density polyethylene blends” Journal of Applied Polymer Science, Vol.79, pp. 418–25, 2001.

9. F. Zhang, T. Endo, W. Qiu W, L.Yang , T. Hirotsu , “Preparation and mechanical properties of composite of fibrous cellulose and maleated polyethylene”, Journal of Applied Polymer Science, Vol.84, pp.1971–80, 2002.

10. P.W. Balasuriya, L.Ye, Y.W Mai, J. Wu, “Mechanical properties of wood flake-polyethylene composites, II. Interface modification”, Journal of Applied Polymer Science, Vol.83, pp. 2505–21, 2002

11. A.Valadez-Gonzalez, J.M. Cervantes-Uc, R. Olayo, and P.J. Herrera-Franco, “Effect of fiber surface treatment on the fiber–matrix bond strength of natural fiber reinforced composites”, Composite B, Vol. 30, pp. 309-330, 1999.

12. R M Rowell, R A Young, J K Rowell, “Paper and composites from agro-based resources”. CRC Lewis Publishers, Boca Raton FL, 1997.

13. John M. J and Anandjiwala R. D, “Recent Developments in Chemical Modification and Characterization of Natural Fiber-Reinforced Composites”, Polymer Composites, 29(2), pp.187-207, 2008.

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14.

Authors:

Yousif Ismail Mohammed

Paper Title:

Virtual Solar Cell Tester System Based on Modified Interval Type-2 Fuzzy Logic Controller

Abstract: The most fundamental of solar cell characterisation techniques is the measurement of cell efficiency. Standardised testing allows the comparison of devices manufactured at different companies and laboratories with different technologies to be compared. This paper presents a new design of solar cell testers for monocrystalline, polycrystalline, cadmium telluride (CdTe), and copper indium diselenide (CIS) cells. Each cell is tested for efficiency and categorized accordingly into four groups (A to D). A Virtual Reality (VR) model was built to simulate the system, keeping in mind real world constraints. Two photoelectric sensors were used to make detections for both the testing process and the robot movement. A handling robot with vacuum end-effectors was designed based a Modified Interval Type-2 Fuzzy Logic Controller (MIT2FLC) and command line programming for construction, editing, and simulation of the MIT2FLC for control of movement for solar cell and then distributed the cells according to the categories of test for efficiency. The MIT2FLC guides the trajectory of the robot according to the results of the efficiency testing. It was seen that the system worked very well, with the testing process and the robot movement interacting smoothly. The robot trajectory was seen to be highly accurate, and the pick and place operations were done with great precision.

Keywords:
Handling robot, Solar cell tester, Virtual reality, a Modified Interval Type-2 Fuzzy Logic Controller (MIT2FLC).


References:

1. M. A. Green, K. Emery, Y. Hishikawa. Warta W. Solar cell efficiency tables (version 37), progress in photovoltaic: research and applications, 2011, 19, pp:84–92
2. S. Mohammad, C. Suria, Z. Zurita, J, J. Faridah, Kasmiran. Portable Wireless CATV Tester Unit with Solar Panel, IEEE International Conference on Space Science and Communication,2009, PP: 15-18

3. P. Hacke, K. Terwilliger, R. Smith, S. Glick, J. Pankow, M. Kempe, S Kurtz. System voltage potential-induced degradation mechanisms in PV modules and methods for test. The 37th IEEE Photovoltaic Specialists Conference (PVSC 37), 2011, pp: 1-9

4. M. Meiqin1, S. Jianhui, L. Chang, P. Kai1, Z. Guorong, Ming D. Research and development of fast field tester for characteristics of solar array. IEEE electrical and computer engineering, 2009, pp: 1055-1060

5. C. Osterwald, Terrestrial photovoltaic module accelerated test-to-failure protocol. Technical report, National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 • www.nrel.gov, March 2008, pp:1-16

6. Y. Al Mashhadany, “Design, Analysis, Simulation, and Virtual-Reality-Verified Intelligent Controller for Industrial-Application SCARA Robot”, International Journal of Advanced Computing, June, 2013

7. R. Lathrop, K. Pfluke, Novel approaches to benchmarking solar cell tabbing solder ability. Proceedings of the 26th European Union Photovoltaic Solar Energy Conference, 2011 , pp: 1-6

8. Student Guide, Robotics with the Boe-Bot, Ver. 2.2, ISBN 1-928982-03-4, 2011 , pp:1-360

9. C. Park, D. Park, H. Min. Controller design and motion simulation of solar cell substrate handling robot in vacuum environment, 11th International conference on control, automation and systems, 2011, pp: 1017-1019

10. Y. Al Mashhadany, “ Hybrid ANFIS Controller for 6-DOF Manipulator with 3D Model”, International Journal of Computers & Technology, Vol. 4, No. 2, ISSN 2277-3061, www.cirworld.com, April, 2013.

11. C. Park, D. Park, H. Min. Motion simulation model for beam type solar cell substrate transport robot, The 8th IEEE International Conference on Ubiquitous Robots and Ambient Intelligence, 2011, pp:796-799

12. X. Zhangl, H. Panland, G. Wul. Photovoltaic generation and Its applications in DC-motor, IEEE International conference on communications, circuits and
systems, 2010, pp: 609-611

13. L. Leottau, M. Melgarejo, “An Embedded Type-2 Fuzzy Controller for a Mobile Robot Application”, chapter 18, Recent Advances in Mobile Robotics, 2011, pp.365-384

14. X. Ling, Y. Zhang, “Operations on Triangle Type-2 Fuzzy Sets” Elsevier, Journal of Advanced in Control Engineering and Information Science,12, 2011, pp. 3346-3350

15. T. C. Yang, J.G. Juang, “Application of Adaptive Type-2 Fuzzy CMAC to Automatic Landing System”, 2010 IEEE International Symposium on Computational Intelligence and Design, ISCID.2010, pp. 221-224

16. M. Manceur, N. Essounbouli, A. Hamzaoui, “Robust Smooth Sliding Type-2 Interval Fuzzy Control For Uncertain System”, 978-1-4244-8126-2/10/$26.00 ©2010
IEEE

17. Y. Al Mashhadany, “Advance 6-DOF Manipulator Controller Design Using DMRAC Based ANFIS” , Wulfenia Journal, Austria, ISNN: 1561-882X, Vol 20, No. 3;Mar 2013.

18. P.A.S. Birkin, J. M. Garibaldi, “A Comparison of Type-1 and Type-2 Fuzzy Controllers in a Micro-Robot Context”, IEEE international Fuzzy conference, Korea, August 20-24, 2009, pp. 1857-1862

19. Z. Lv, H. Jin, P. Yuan, “The Theory of Triangle Type-2 Fuzzy Sets”’ IEEE Ninth International Conference on Computer and Information Technology, 2009, pp. 57-62

20. X. Du, H. Ying, “Derivation and Analysis of the Analytical Structures of the Interval Type-2 Fuzzy-PI and PD Controllers”, IEEE transactions on fuzzy system, Vol, 18, No. 4, Aug. 2010, pp. 802-814

21. Y. I. Al Mashhadany, S. Adel, A. Abdu sattar, A. Khuder, “Novel Controller for PUMA 560 Based on PIC Microcontroller”, has been accepted for publication in Wulfenia Journal, Vol. 21, Iss. 4, 2014

22. M. Biglarbegian, W. W. Melek, J. M. Mendel, “Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments”, IEEE transactions on industrial electronics, Vol. 58, No. 4, April 2011, pp. 1371-1384

23. J. T. Rickard, J. Aisbett, G. Gibbon, “Type-2 Fuzzy Conceptual Spaces”, 978-1-4244-8126-2/10/$26.00 ©2010 IEEE

24. Y. Al Mashhadany,”High-Performance of Power System Based upon ANFIS (Adaptive Neuro-Fuzzy Inference System) Controller”, Journal of Energy and Power Engineering 8 , 729-734, 2014.

25. M. Manceur, N. Essounbouli, A. Hamzaoui, “Higher Order Sliding Fuzzy Type-2 Interval Control for SISO Uncertain Nonlinear Systems”, IEEE International Conference on Fuzzy Systems, June 27-30, 2011, Taipei, Taiwan, pp. 1388-1396

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15.

Authors:

Deniz Kılınç, Fatma Bozyiğit, Alp Kut, Muhammet Kaya

Paper Title:

Overview of Source Code Plagiarism in Programming Courses

Abstract: Plagiarism of programming source codes is an undesirable situation in the many fields of software development world. Especially in educational field, it is obviously realized that plagiarism in programming courses increases consistently. The aim of this study is attempting to answer questions such as “which codes are similar?”, “what similarity ratios are?” in order to prevent plagiarism among university students who attend programming courses. While developing the proposed methodology, N-gram similarity calculation method and Vector Space Model (VSM) were considered. Information Retrieval (IR) System and Cosine Normalization (CN) methods were utilized to calculate similarity ratios. Experimental study was performed on the dataset yielded by changing source code examples in different forms. The results obtained provide convincing evidence that the study is fit the purpose.

Keywords:
Plagiarism source code, n-gram, vector space model, cosine normalization.


References:

1. Parker and J. Hamblen, “Computer algorithms for plagiarism detection”, IEEE Trans. Education, vol. 32, May 1989, pp. 94–99.
2. Intellectual and Artistic Works Law. Available: http://mevzuat.meb.gov.tr/html/7981_5846.html

3. G. Whale, “Plague: Plagiarism Detection Using Program Structure”, Dept. Comput. Sci., Univ. New South Wales, Kensington, Australia, Tech. Rep. 8805,1988.

4. G. Malpohl. JPlag: Detecting Software Plagiarism. Available: http://www.ipd.uka.de:2222/index.html

5. “YAP3: Improved detection of similarities in computer program and othertexts,” In Proc. 27th SCGCSE Tech. Symp., Philadelphia, PA, 1996, pp.130–134

6. R. Dale, H. Mois, H. Somers, “Handbook of NLP”, Marcel Dekker, 2000.

7. G. Salton, “The SMART Retrieval System – Experiments in Automatic Document Processing”, NJ, Englewood Cliffs: Prentice-Hall, 1971.

8. G. Salton, A. Wong, C.S. Yang. “A vector space model for information retrieval”, Journal of the American Society for Information Science, 1975 , 18(11):613-620.

9. C.D. Manning, P. Raghavan., H. Schütze, “An Introduction to Information Retrieval”, Cambridge University Press, 2009.

10. M. Joy and M. Luck, “Plagiarism in programming assignments,” IEEE Trans. Educ., vol. 42, no. 1, Feb. 1999, pp. 129–133.

11. E. L. Jones, “Metrics based plagiarism monitoring “, In: 6th Annual CCSC Northeastern Conference, Middlebury, Vermont, April 20-21, 2001.

12. F. Culwin, T. Lancaster, “Plagiarism issues for higher education”, VINE, Vol. 31 Iss 2, 2001, pp. 36 – 41.

13. G. Frantzeskou, E. Stamatatos, S. Gritzalis, and C. E. Chaski. “Identifying authorship by byte-level n-grams: The source code author profile (SCAP)”, Journal of Digital Evidence, 2007.

14. Bozyigit, D. Kılınç, A. Kut, M. Kaya, “Bulanık Mantık Algoritmaları Kullanarak Kaynak Kod Benzerliği Bulma”,In: AB15, 2015, submitted for publication.

15. Vector Space Model. Available: http://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html

 

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16.

Authors:

Alem Čolaković, Tarik Čaršimamović

Paper Title:

The Corresponding Options of TCP Variants for Fairness Problem in AD HOC Networks

Abstract: The ad hoc network is a continuously self-configuring and decentralized network where nodes communicate with each other without the fixed network infrastructure or centralized administration. TCP (Transmission Control Protocol) is a connection-oriented transport protocol that provides a reliable exchange of data streams. Implementation of TCP in wireless networks has many challenges such as the issues of the efficiency and TCP fairness problem. The fairness means that network nodes (users or applications) are receiving a fair share of overall resources. In this paper, we study the problem of maintaining the fairness for TCP connections in ad hoc networks. Our research has been made to present the TCP fairness problem in MANET (ad hoc mobile networks) while considering the sending and receiving of traffic. Achieving fairness in these networks is a challenge due to specific characteristics of an ad hoc environment and it is necessary to adapt TCP for ad hoc networks. The primary goal of this paper is to present fairness in ad hoc networks using combinations of different TCP variants and routing protocols. We evaluated the results of our research by using the proper simulation method. .

Keywords:
Ad hoc, MANET, VANET, TCP, fairness.


References:

1. Saylee Gharge, Ajinkya Valanjoo: “Review of different TCP variants in Ad hoc networks”, VESIT, International Technological Conference-2014 (I-TechCON), Jan. 03 – 04, 2014.
2. Hatim Mohamad Tahir, Abas Md Said, Alex W.M. Tan: „Improving TCP fairness Flows in Wireless Ad Hoc Networks“, Information Systems International Conference (ISICO), 2-4. December 2013.

3. Ahmad Al Hanbali, Eitan Altman, Philippe Nain: “A Survey of TCP over Ad hoc Networks”, Communications Surveys & Tutorials, IEEE (Volume:7 , Issue: 3 ), 2006.

4. Linfang Dong, Shang Liu: “Research on TCP Fairness Improvement over Wireless Ad Hoc Networks”, Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on 10-12 August 2010.

5. Ms.M.Santhiya ,Mr.M.Sunil karthick, Ms.M.keerthika: “Performance of various TCP in Vehicular Ad Hoc network based on timer management”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 12, December, 2013.

6. Vijendra Rai: “Simulation of Ad-hoc Networks Using DSDV, AODV And DSR Protocols And Their Performance Comparison” Proceedings of the 4th National Conference; INDIACom-2010 Computing For Nation Development, February 25 – 26, 2010.

7. Yi Lu, Yuhui Zhong, Bharat Bhargava: „Packet Loss in Mobile Ad Hoc Networks“, Center for Education and Research in Information Assurance and Security, Department of Computer Sciences, Purdue University, Wes Lafayette, USA.

8. Dimitry Kuptsov, Boris Nechaev, Andrey Lukyanenko: “A novel Demand-Aware Fairness Metric for IEEE 802.11 Wireless Networks“, Combra, Portugal, SAC’13 March 18-22, 2013.

9. Jain, R.; Chiu, D.M.; Hawe, W.: “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems”, DEC Research Report TR-30, 1984.

10. Saar Pilosof, Ramachandran Ramjee, Danny Raz, Yuval Shavitt, Prasun Sinha: „Understanding TCP fairness over Wireless LAN“, IEEE INFOCOM 2003.

11. Tan G., Guttag J.: „Time-based fairness Improves Performance in Multi-rate WLANs”. In Proc. of USENIX’04, Boston, MA, 2004.

12. G. Boggia, P. Camarda, L. A. Grieco, T. Mastrocristino, and G. Tesoriere: „A Cross-layer Approach to Enhance TCP Fairness in Wireless Ad-hoc Networks“, Wireless Communication Systems, 2005. 2nd International Symposium, Siena, September 2005.

13. Chi Ho Ng, Jack Chow, Ljiljana Trajkovic: „Performance Ecaluation of TCP over WLAN 802.11 with Snoop Performance Enhancing Proxy“, In Proceedings of OPNETWORK, School of Engineering Science Simon Fraser University, Vancouver Canada V5A 1S, 2002.

14. Awedeh R.: „Compatibility of TCP Reno and TCP Vegas in wireless ad hoc networks“, IET Communications, vol. 1, issue 6, 2007.

15. V. Jacobson, R. Braden: “TCP Extensions for Long-Delay Paths” Network Working Group, Request for Comments: 1072, 1988.

16. M. Mathis, J. Mahdavi, S. Floyd, A. Romanow: “TCP Selective Acknowledgment Options” Network Working Group, Request for Comments: 2018, 1996.

17. K. Ramakrishnan, S. Floyd, D. Black: “The Addition of Explicit Congestion Notification (ECN) to IP”, Network Working Group, Request for Comments: 3168, September 2001.

18. Forouzan Pirmohammadi, Mahmood Fathy, Hossein Ghaffarian: „TCP and UDP Fairness in Vehicular Ad hoc Networks“, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 6, June 2012.

19. Sabina Baraković, Jasmina Baraković: “Comparative Performance Evaluation of Mobile Ad HocRouting Protocols” MIPRO, Opatija, Croatia, 2010.

20. Kaixin Xu, Mario Gerla, Lantao Qi, Yantai Shu: „Enhancing TCP Fairness in Ad Hoc Wireless Networks Using Neighborhood RED“, MobiCom ’03, USA, 2003.

21. Yahia M: „Enhanced congestion control for TCP fairness in ad hoc wireless networks“, Industrial Electronics and Applications (ICIEA), 7th IEEE Conference on 18-20 July 2012.

22. Linfang Dong, Yantai Shu, Sanadidi M., Gerla M.: “A Method for Improving the TCP Fairness in Wireless Ad Hoc Networks”, Wireless Communications, Networking and Mobile Computing, 2008. WiCOM ’08. 4th International Conference on 12-14 Oct. 2008.

23. Zhifei Li, Sukumar Nandi, Anil K. Gupta: “Achieving MAC fairness in wireless ad-hoc networks using adaptive transmission contro”, Computers and Communications, 2004. Proceedings. ISCC 2004. Ninth International Symposium on July 2004.

24. Jian Liu, Singh Suresh: “ATCP: TCP for mobile ad hoc networks”, Selected Areas in Communications, IEEE Journal on (Volume:19 , Issue: 7), July 2001.

 

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17.

Authors:

Harsh Khatter, Anjali Jain, Poonam Pandey

Paper Title:

Classification and Categorization of Blood Infection Using Fuzzy Inference System

Abstract: From last few decades the human body infections and diseases are growing in exponential manner. As per the medical report, in every three months a new infection or viral comes in existence with some new explode to effect the human race. To test whether the infection is in body or not, Blood tests are the common methods. Most of the diseases are beyond the doctor’s study or some recently spread virus infected the blood or human body. In such cases doctors use to give the treatment of other disease having same symptoms or same blood test cases. In this paper we are trying to make such a system which will spread awareness among doctors about the infections. The proposed system will work on the basis of fuzzy logic and neural network with the help of inference engine and its rules. The simulation will be done using Matlab. The proposed approach of using fuzzy logic and inferences with neural networks training in blood samples on real test cases of blood report is a novel idea.

Keywords:
About four key words or phrases in alphabetical order, separated by commas.


References:

1. W.H. Chang, J.H. Wang, W.S. Ling “An integrated microfluidic system for detecting human immunodeficiency virus in blood samples”, IEEE, Taiwan, January 20-24, 2013.
2. A.P. Reddington, J.T. Tureb, A. Tuysuzoglu, G.G. Daaboul, C.A. Lopez, W.C. Karl “An Interferometric Reflectance Imaging Sensor for Point of Care Viral Diagnostic”, IEEE transaction, vol.60, no.12, December 2013.

3. W.Shitong, K.F.L. Chung, F.Duan “Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection”, Elsevier, Neurocomputing 70 (2007), pp 1348–1359, 2006.

4. W. Shitong, F. Duan, X. Min, H. Dewen “Advanced fuzzy cellular neural network: Application to CT liver images”, Elsevier, Artificial Intelligence in Medicine (2007) 39, pp 65—77, 2006.

5. D. Elsheakh, H. Elsadeki, E. Abdullah, S. Atteya. W. N. ELmazny “Novel Rapid Detection of Different Viruses in Blood Using Microimmuno-Sensor”, 7th European Conference on Antennas and Propagation, Gothenburg, 8-12 April, 2013, pp. 1128-1131.

6. D.S. Campo, Z. Dimitrova, G.L. Xia, P. Skums, L.G. Raeva, Y. Khudyakov “New Computational Methods for Assessing the Genetic Relatedness of Close Viral Variants”, IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Miami, 2-4 June 2014.

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18.

Authors:

Apeksha Rani H. M, Prathibha Kiran

Paper Title:

A Novel Method for Analysis of EEG Signals Using Brain Wave Data Analyzer

Abstract: The present day research allow us to develop a new class of bioengineering control devices and robots to provide daily life assistance to handicapped and elderly people. This proposed method describes how the brain activity is measured using mind wave EEG signal data transmission device. The brain electric signal are measured by EEG (Electroencephalograph) which shows a demand for better accuracy and stability and facilitates the graphical illustration of spatial features of electric brain activity. It provides a very promising technology for physically disabled people who are unable to access their hands and in this paper we will discuss briefly how the data acquisition can be done by using biosensor.

Keywords:
EEG, Mind wave, Biosensor.


References:

1. J.Katona, I.Farkas, T.Ujbanyi, P.Dukan, and A.Kovari,”Evaluation of the Neurosky MindFlex EEg Headset Brain Waves Data” (Ed.), 2014.
2. Chin-Teng Lin, Bor-Shyh Lin, Fu-Chang Lin, and Che-Jui Chang, “Brain Computer Interface-Based Smart Living Environmental Auto-Adjustment Contol System in UPnP Home Networking, IEEE System Journal, Vol..8, No.2, 2014

3. Giovanni, Topo Suprihadi, Kanisius Karyono,” Drowtion: Drowsiness detection software using Mind wave”, IEEE August Co., 2014

4. Gy. Buzsaki, Rhythms of the Brain, Oxford University Press, 2006

5. J. Malmivuo, R. Plonsey, Bioelectromagnetism, Principles and Applications of Bioelectric and Biomagnetic Fields, Oxford University Press, New York, 1995

6. Neurosky Inc, MindSet Communications Protocol, Neurosky Inc., 2010

7. Lebedev M.A., Nicolelis M.A., Brain-machine interfaces: past, present and future, Trends Neurosci, 29, 536-546, 2006

8. NeuroSky Inc, The brain Wave Signal (EEG), NeuroSky Inc, 2009

9. J. Katona, A. Kovari, T. Ujbanyi, Visualization of brainwaves, Dunakavics, DF Press, in press.

10. J. Katona et al. • Evaluation of The Neurosky MindFlex EEG Headset Brain Waves Data

 

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19.

Authors:

Havyas V B, Choodarathnakara A L, Thribhuvan R, Chethan K S

Paper Title:

Decision Tree Approach for Classification of Satellite Imagery

Abstract: Various practical systems capable of extracting descriptive decision making knowledge from data have been developed and evaluated. Techniques that represent knowledge about classification tasks in the form of decision trees are focused on. A sample of techniques is sketched, ranging from basic methods of constructing decision trees to ways of using them non-categorically. Some characteristics that suggest whether a particular classification task is likely to he amenable or otherwise to tree-based methods are discussed. Many urban land cover types show spectral similarity in remote sensing data. Further, the finer the spatial resolution of the data, the larger is the number of detectable subclasses within classes. This high within-class spectral variance of some classes results in multimodal distribution of spectra and may decrease their spectral separability. Hence, the existing traditional hard classification techniques which are parametric type do not perform well on high resolution data in the complex environment of the urban area as they expect datasets to be distributed normally. The aim of this paper is to investigate a non-parametric classifier as an alternative approach to classify an image data of a semi urban area

Keywords:
Remote Sensing, Image Classification, Parametric Classifier, Non-parametric and Decision Tree Classifier

References:

1. Navalgund, R. R., “Remote sensing: Basics and applications”. Resonance, 2001, 6, 51–60.
2. Patel, N. K., Medhavy, T. T., Patnaik, C. and Hussain, A., “Multi-temporal ERS-1 SAR data for identification of rice crop”. J. Indian Soc. Remote Sensing, 1995, 23, 33–39.

3. Panigrahy, S., Chakraborty, M., Sharma, S. A., Kundu, N., Ghose, S. C. and Pal, M., “Early estimation of rice acre using temporal ERS-1 synthetic aperture radar data – A case study for Howrah and Hooghly districts of West Bengal”, India. Int. J. Remote Sensing, 1997, 18, 1827–1833

4. Srivastava, H. S., Patel, P. and Navalgund, R. R., “Incorporating soil texture in soil moisture estimation from extended low-1 beammode Radarsat-SAR data”. Int. J. Remote Sensing, 2006, 27, 2587– 2598.

5. Jayaraman, V., Srivastava, S. K., Kumaran Raju, K. and Rao, U.R., “Total solution approach using IRS-1C and IRS-P3: A perspective of multi-resolution data fusion and improved vegetation indices”. IEEE Trans. Geosci. Remote Sensing, 2000, 38, 587–604

6. Navalgund, R. R., Parihar, J. S., Ajai and Nageshwar Rao, P. P., “Crop inventory using remotely sensed data”. Curr. Sci., 1991, 61, 162–171.

7. Vasudevan, B. G., Gohil, B. S. and Agarwal, V. K., “Backpropagation neural network based retrieval of atmospheric water vapour and cloud liquid water from IRS-P4 MSMR”. IEEE Trans Geosci. Remote Sensing, 2004, 42(5), 985–990.

8. C. Apte and S. Weiss, “Data mining with decision trees and decision rules,” Future Generation Computer Systems,” pp. 197-210, Nov. 1997

9. Selim Aksoy, K. Koperski, Carsten Tusk, and Giovanni Marchisio, “Interactive training of advanced classifiers for mining remote sensing image archives,” KDD’04, Seattle, Washington, USA, August 22-25,2004.

10. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, 2nd ed., Elsevier, 2006.

11. S. Rasoul Safavian and David Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. Syst. Man Cybnet., vol. 21, no. 3, pp. 660-674, May/ June 1991

12. Lior Rokach and Oded Maimon, “Top-down induction of decision trees classifiers-A survey,” IEEE Trans. Syst. Man Cybnet., part C, vol. 35, no. 4, pp. 476-487, Nov. 2005.

13. R. G. Congalton and A. Roy Mead, “A review of three discrete multivariate analysis techniques used in assessing the accuracy of remotely sensed data from error matrix,” IEEE Trans. Geosci. Remote Sensing, vol. GE 24, no. 1, pp. 169-174, Jan. 1986.

14. R. G. Congalton, “Considerations and techniques for assessing the accuracy of remotely sensed data,” Geosci. Remote Sens. Symp., vol. 3, Jul. 10-14, 1989, pp. 1847-1850.

 

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20.

Authors:

Etleva Beliu, Ermira Pajaj

Paper Title:

What Affects the Memory

Abstract: Physical exercise affects our body on multiple fronts. It increases heart rate, which means more blood pumped to the brain. It also helps the body release some hormones, that participate in aiding and providing a nourishing environment for the growth of brain cells. Physical exercise training can modify hippocampal and medial temporal lobe volumes. Both of these regions are involved in memorization. The aim of this study is to analyze the effect of physical exercise, of smoking, and using alcohol on the memorizing ability. By using the questionnaires and face-to-face interviews, data is collected from around 300 people of both genders. They have an age range of 15 – 20 years old and are from different schools in Albania ; namely in Tirana and the outskirts of Tirana, in Durres, in Shkodra, Gjirokastra and Vlora. They are asked to read 40 words in 5 minutes and then are tested to see how many of them they can memorize. The questionaire includes demographic information such as age, gender and birth city and questions to measure their lifestyle. In the lifestyle area it is asked about the smoking habbit, alcohol consumption, eating and exercise frequency. The data is analized using SPSS. A normalization of number of memorized words is done. Then, this modified variabel is analized using stepwise multiple regression. The most important independent variables of this model are exercise frequency and alcohol consumption. Exercise frequency is organized in three groups: those who train 0-2 days a week, Level Group 1 (LG1); those who train 3-4 days a week (LG2); and the ones that train more than 4 days a week (LG3). The method of MANOVA shows that there is a statistically obvious increase between two consecutive levelgroups. But there is a very significant difference between the number of memorized words of LG1 and LG2. This research provides evidence that physical exercise and alcohol consumation affect memory.

Keywords:
memory, BDNF molecule, hippocampus, exercise frequency, alcohol consumption, stepwise multiple regression, and MANOVA.


References:

1. http://www.brainfacts.org/across-the-lifespan/diet-and-exercise/articles/2013/physical-exercise-beefs-up-the-brain/
2. http://www.ncbi.nlm.nih.gov/pubmed/21722657

3. Griffin ÉW, et al. Aerobic exercise improves hippocampus function and increases BDNF in the serum of young adult males. Physiol Behav. 2011 104(5):934-41

4. Devin K. Binder and Helen E. Scharfman. Brain-derived Neurotrophic Factor Growth Factors. 2004 September; 22(3): 123–131.

5. Ka Lok Chan et al., Relationship of serum brain-derived neurotrophic factor (BDNF) and health-related lifestyle in healthy human subjects (The results of this study were approved by the Research Ethnic Committee of Hong Kong Hospital Authority and the Hong Kong Polytechnic University Human Subjects Ethnics Sub-Comittee)

6. http://www.pnas.org/content/108/7/3017.short Kirk I. Erickson et al., Exercise training increases size of hippocampus and improves memory, PNAS February 15, 2011 vol. 108 no. 7 3017–3022

7. Kirk I. Erickson et al Brain-Derived Neurotrophic Factor Is Associated with Age-Related Decline in Hippocampal Volume The Journal of Neuroscience, April 14, 2010 • 30(15):5368 –5375

8. Applied Longitudinal Analysis, G. Fitzmaurice, N. Laird and Ware, pp.490-513

9. Practical Multivariate Analysis, A.A. Afifi , S.May, V. Clarc, 2011, pp.231-264

10. An introduction to applied multivariate analysis, Tenko Raikov and George Marcoulides , 2008, pp.120-314

 

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21.

Authors:

Aditya Kumar Singh, Apurva Anand, Anindya Sundar Das

Paper Title:

First Passage Monte-Carlo Simulation for Charge Distribution and Capacitance

Abstract: A novel scheme has been studied and demonstrated for Monte Carlo simulations of diffusion-reaction processes. The new algorithm skips the traditional small diffusion hops and propagates the diffusing particles over long distances through a sequence of super-hops, one particle at a time. By partitioning the simulation space into non- overlapping protecting domains each containing only one or two particles, the algorithm factorizes the N-body problem of collisions among multiple Brownian particles into a set of much simpler single-body and two-body problems. Efficient propagation of particles inside their protective do- mains is enabled through the use of time-dependent Green’s functions (propagators) obtained as solutions for the first-passage statistics of random walks. The resulting Monte Carlo algorithm is event-driven and asynchronous; each Brownian particle propagates inside its own protective domain and on its own time clock. The algorithm reproduces the statistics of the underlying Monte-Carlo model exactly. The new algorithm is efficient at low particle densities, where other existing algorithms slow down severely. Thus we have analyzed the application of this algorithm in the charge distribution and the capacitance detection.

Keywords: Monte Carlo Simulation, Charge distribution, capacitance, Markov chain

References:

1. James A. Given, Chi-Hwang, Michel Mascagni, “First and last passage Monte Carlo algorithm for charge distribution on a conducting surface”, Phys. Rev. 66,0567042002.
2. Sydeny Redner, “A Guide to First Passage Process” Cambridge university press, 2001.

3. M.Strobel, K.-H. Heinig, and W. Moller. “Three-dimensional domain growth on the size scale of the capillary length: Effective growth exponent and comparative atomistic and mean-field simulations”. Phys. Rev. B, 64(24):245422, 2001.

4. J. S. Reese, S. Raimondeau, and D. G. Vlachos. Monte Carlo Algorithms for Complex Surface Reaction Mechanisms: Efficiency and Accuracy. J. Comp. Phys. 173(1):302–321, 2001.

5. M. Biehl. “Lattice gas models and Kinetic Monte Carlo simulations of epitaxial growth”. In Voigt, editor, Int. Series of Numerical Mathematics 149, pages 3–18. Birkhaeuser, 2005.

6. S. K. Theiss, M.-J. Caturla, M. D. Johnson, J. Zhu, T. J. Lenosky, B. Sadigh, and T. Diazde la Rubia. Atomic scale models of ion implantation and dopant diffusion in silico. Thin Solid Films, 365:219–230, 2000.

7. C. Domain, C. S. Becquart, and L. Malerba. “Simulation of radiation damage in Fe alloys: an object kinetic Monte Carlo approach. Journal of Nuclear Materials”, 335(1):121–145, 2004.

8. D. P. Tolle and N. Le Novere. “Particle-based stochastic simulation in systems biology”. Current Bioinformatics, 1(3):315–320, 2006.

9. J. S. van Zon and P. R. ten Wolde. “Green’s-function reaction dynamics: A particle-based approach for simulating biochemical networks in time and space”. J. Chem. Phys., 123(23):234910, 2005.

10. S. J. Plimpton and A. Slepoy. “Microbial cell modeling via reacting diffusive particles”. J. Phys.: Conf. Ser., 16:305–309, 2005.

11. M. H. Kalos, D. Levesque, and L. Verlet. “Helium at zero temperature with hard-sphere and other forces”. Phys. Rev. A, 9(5):2178–2195, 1974.

12. J. Dalla Torre, J.-L. Bocquet, N. V. Doan, E. Adam, and A. Barbu. “JERK an event-based Kinetic Monte Carlo model to predict microstructure evolution of materials under irradiation”. Philosophical Magazine, 85:549–558, 2005.

13. T. Oppelstrup, V. V. Bulatov, G. H. Gilmer, M. H. Kalos, and B. Sadigh. “First-Passage Monte Carlo Algorithm: Diffusion without All the Hops”. Phys. Rev. Lett., 97(23):230602, 2006.Donev.

14. T. Oppelstrup, V. V. Bulatov, G. H. Gilmer, M. H. Kalos, and B. Sadigh. “Asynchronous event-driven particle algorithms. SIMULATION”. Transactions of The Society for Modeling and Simulation International, 85(4):229–242, 2008.

15. Daniel Ben Abraham. “Computer simulation methods for diffusion-controlled reactions”. J. Chem. Phys., 88(2):941–948, 1988.

16. F. Leyvraz and S. Redner. “Spatial organization in the two-species annihilation reaction A +B → 0”. Phys. Rev. Lett., 66(16):2168–2171, 1991.

17. Y. Shafrir and D. ben Avraham. “Large scale simulations of two-species annihilation, A+B → 0, with drift”. Phys. Lett. A, 278(4):184–190, 2001.

18. M. Smith and T. Matsoukas. “Constant-number Monte Carlo simulation of population balances”. Chemical Engineering Science, 53(9):1777–1786, 1998.

19. D. Zhong, R. Dawkins, and D. Ben Abraham. “Large-scale simulations of diffusion-limited n-species annihilation”. Phys. Rev. E, 67(4):040101, 2003.

20. S. S. Andrews and D. Bray. “Stochastic simulation of chemical reactions with spatial resolution and single molecule detail”. Physical biology, 1(3):137–151, 2004.

21. D. Toussaint and F. Wilczek. “Particle–antiparticle annihilation in diffusive motion”. J. Chem. Phys., 78(5):2642–2647, 1983.

22. H. Kim and K. J. Shin. Exact Solution of the Reversible Diffusion-Influenced Reaction for an Isolated Pair in Three Dimensions. Phys. Rev. Lett., 82(7):1578–1581, 1999

 

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22.

Authors:

Merina Devi Hemam, N.V. Uma Reddy

Paper Title:

An Energy-Efficient, Delay-Aware, Lifetime-Balancing and Data Collection Protocol for Heterogeneous Wireless Sensor Networks

Abstract: The technique that is used in this paper is to make it more simpler for wireless sensor networks problem .To make the energy more efficient a protocol is used that is called EDAL. It is rebuilt from the existing system called OVR which uses NP-hard algorithm. To make more prominent a centralized heuristic is design to make the computational overhead more smaller and to detect the dead nodes. As it has some limitation distributed heuristic is design which is the best for large scale networks.

Keywords:
Power consumption, delay, energy efficient, heuristic algorithm, wireless sensor networks.


References:

1. N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A wireless sensor network for structural monitoring,” in Proc. 2nd ACM SenSys, New York, NY, USA, 2004, pp. 13–24. 1472–1483, 2009.
2. An Energy-Aware Routing Protocol in Wireless Sensor Networks Ming Liu 1, Jiannong Cao 2, Guihai Chen 3 and Xiaomin Wang Sensors 2009.

3. B. Eksioglu, A. V. Vural, and A. Reisman, “The vehicle routing problem: A taxonomic review,” Comput. Ind. Eng., vol. 57, no. 4, pp. 1472–1483, 2009.

4. O. Bräysy and M. Gendreau, “Vehicle routing problem with time windows, Part I: Route construction and local search algorithms,” Transport. Sci., vol. 39, no. 1, pp. 104–118, Feb. 2005.

5. Z. Ozyurt, D. Aksen, and N. Aras, “Open vehicle routing problem with time deadlines: Solution methods and an application,” in Operations Research Proceedings 2005, ser. Operations Research Proceedings, H.-D. Haasis, H. Kopfer, and J. Schnberger, Eds. Berlin, Germany: Springer, 2006, vol. 2005, pp. 73–78.

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23.

Authors:

Sweta, Sushmitha Reddy I, Maddipatla Mounika, Priyanka Agrawal, Pallavi G. B

Paper Title:

A Survey to Justify the Need for Carpooling

Abstract: In India people mostly prefer road transportation to move around places. The increasing number of vehicle on road lead to several issues as congestion, environmental degradation and energy problems. Research and development have been progressively done in this field to reduce the environmental degradation and for the better utilization of fossil fuels. Different approaches and techniques to solve these issues emerged which address fields of emission reduction, increase efficiency of vehicle, energy alternative, decrease the road density with care of safety and comfort, etc. In this paper survey on these emerging drifts and elaborate on one of the ways to reduce the vehicular density and emission, we have identified carpooling as one such solution to provide user, flexibility in time, enjoyable, efficient and safe ride.

Keywords:
Drivers, Efficiency, GHG emission, HOV [high occupancy vehicle], Passenger, Road density, Safety.


References:

1. Geetam Tiwari. “URBAN TRANSPORTION IN INDIAN CITIES”. London School of Economics.
2. “ D. s.Jonathan Handbook”, in www.delhi.gov.in, 2013.

3. Norman1; Heather L. MacLean; and Christopher A. Kennedy3. “Comparing high Or Low Residential Density: Life-Cycle Analysis of Energy Use and Greenhouse Gas Emission” in JOURNAL OF URBAN PLANNING AND DEVELOPMENT, MARCH 2006.

4. Hirschman, K. Zallinger, M. Fellendorf, M. Hansberger, S. “A New Method Traffic Calculate Emission with Simulated Traffic Conditions” in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on 19-22 September 2010 Funchal.

5. Vivek Tyagi, Member, IEEE, Shivkumar Kalyanarman, Fellow, IEEE and Raghuram Krishnapuram, Fellow, IEEE, IBM India Research Laboratory, India “Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics”

6. Dimitrakopoulos, G. ; Department of Digital system, University of Piraeus, Greece; Dem’estichas P. in “Intelligent Transportation Systems”, 2011.

7. Heinz Jansen, Cecile; “A welfare cost assessment of various policy measures to reduce pollutant emissions from passenger road vehicles” , Published in Transport Research, volume 4, November 1999.

8. Moshe Ben-Akiva Massachusetts Institute of Technology, Andre de Palma Queen’s University, Kingston, Ontario, Canada, Pavlos Kanaroglou McMaster University, Hamilton, Ontario, Canada, “Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates”.

9. Jason Hill, Erik Nelson, David Tilman, Stephen Polasky , and Douglas Tiffany “Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels”,. Published in 2006.

10. Olof Johansson and Lee Schipper “Measuring the Long-Run Fuel Demand of Cars: Separate Estimations of Vehicle Stock, Mean Fuel Intensity, and Mean
Annual Driving Distance.” in Journal of Transport Economics and Policy.

11. Sisinnio Concas, Philip L.Winters, “ Impact of Carpooling on Trip-Chaining Behavior and Emission Reductions”, published in Transportation Research Record: Journal of the Transportation Research Board, December 26, 2007.

12. Moshie Ben-Akiva and Terry J. Atherton “Methodology for Short –Range Travel Demand Prediction. An Analysis for carpooling Incentive”

13. Shrishti Garg “Carpools: Driving into greener pastures Carpooling not only helps save fuel but also contributes to the environment”, in The Business Standard, June 3, 2014.

14. Kum Kum Dewan and Israr Ahmad “Carpooling: A Step To Reduce Congestion (A Case Study of Delhi)” , in The times of India, 2014.

15. Burmeister, B. ; Daimler-Benz Res. Syst. Technol., Berlin, Germany ; Haddadi, A. ; Matylis, G. “Application of multi-agent systems in traffic and transportation” in Software Engineering. IEE Proceedings, 06 August 2002.

16. Lalos, P. Dept. of Electron., Computer., Telecomm., Univ. of Athens, Athens, Greece ; Korres, A. ; Datsikas, C.K. ; “ A Framework for Dynamic Car and Taxi Pools with the Use of Positioning Systems” in Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATION WORLD ’09.

17. Kavita Sheoran (Guide) Assistant Professor / Reader, Vaibhav Jatana, Rachit Gulati, Nikhil Ahuja and Ankit Kapoor, Student, CSE Department Maharaja Surajmal Institute of Technology “Intelligent Transportation System Architecture for a Carpool System”. Published in International Journal of Computer Applications.

18. Tejas Talele, Gauresh Pandit and Parimal Deshmukh “Dynamic ridesharing using social media”, in International Journal on AdHoc Networking Systems.

19. Swati. Tare Neha B. Khalate Ajita and A.Mahapadi Department of C.S.E J.S.P.M’s BSIOTR(W) University of Pune , “Review Paper On CarPooling Using Android Operating System-A Step Towards Green Environment”, in International Journal of Advanced Research in Computer Science and Software Engineering, April 2013.

20. Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal.

21. J. D. Hunt, J. D. P. McMillan, “Stated-Preference Examination of Attitudes Toward Carpooling to Work in Calgary”, in Transportation Research Record: Journal
of the Transportation Research Board journal, January 30, 2007.

22. Prathibha Joy “What’s stopping people in Bengaluru from carpooling?”, in The Times of India, 11 November 2014.

 

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24.

Authors:

Shubha Agarwal, Govind Narain Bajpai

Paper Title:

The Brand Extension Strategy: An Analysis

Abstract: Branding has emerged as a top management priority in the last decade due to the growing realization that brands are one of the most valuable intangible assets that firms have. This paper identifies some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand equity measurement, brand growth, and brand management. It is also discussed how branding and society affect each other. Based on the knowledge of how branding theories have been developed as dependent variables of each other and the society, we are able to form a better understanding of the past, the present, and the future of branding.

Keywords:
top management priority, most valuable intangible assets, branding area, brand positioning, brand integration, brand equity measurement, brand growth.

References:

1. Aaker, David A., Joachimsthaler, Erich (2000): Brand Leadership. The Free Press, New York
2. Burk, James (1996): Satisfied customers. Scientific American. Vol. 274 Issue 3, p116

3. Ailawadi, K. L., D. R. Lehmann, S. A. Neslin. 2001. Market response to a major policy change in the marketing mix: Learning from Procter & Gamble’s value pricing strategy. J. Marketing. 65 (January) 44-61.: Research and Best Practices, Jossey Bass, 52-82.

4. Ambler, T., C.B. Bhattacharya, J. Edell, K. L. Keller, K. N. Lemon, V. Mittal. 2002. Relating brand and customer perspectives on marketing management. J. Service Res. 5(1) 13-25.

5. Kapferer, Jean-Noël (2008): The New Strategic Brand Management – Creating and sustaining brand equity long term. London; Philadelphia, Kogan Page

6. Kim, Hong-bumm, Kim, Woo Gon, and An, Jeong A. (2003): The Effect of Consumer based Brand Equity on Firms’ Financial Performance. Journal of Consumer Marketing. 20(4), pg. 335-351

7. D., J.-P. Dubé, S. Gupta. 2005. Own-brand and cross-brand retail pass-through.Marketing Sci. 24(1) 123-137.

8. Muniz, Albert M., Jr. & O’Guinn, Thomas C. (2001): Brand Community. The Journal of Consumer Research. 27(4), pg. 412-432

 

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25.

Authors:

H. Mohssine, M. Kourchi, H. Bouhouch F. Debbagh

Paper Title:

Perturb and Observe (P&O) and Incremental Conductance (INC) MPPT Algorithms for PV Panels

Abstract: In this work we present a study on the comparison between two MPPT algorithms: Perturb and Observe (P&O) and Incremental Conductance (INC). We base our approach on the difference between computed results using an adapter bloc Buck DC-DC converter. The MPPT algorithms are combined with it to complete the PV simulation system. We show that the MPPT control with both P & O and INC keeps the system power operating point at its maximum. For this purpose the conventional P&O, the converter input reference voltage is perturbed in fixed steps until the maximum power is reached. However, depending on the step size, the system operating point will oscillate around the MPP resulting in a loss of energy.

Keywords:
Photovoltaic (PV), Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O), Incremental Conductance (INC).


References:

1. F. Ansari, A. K. Jha‘ Maximum power point tracking using perturbation and observation as well as incremental conductance algorithm’ international journal of research in engineering & applied sciences, issn: 2294-3905, PP 19-30, 2011.
2. S. Jain and V. Agarwal, “A New Algorithm for Rapid Tracking of Approximate Maximum Power Point in Photovoltaic Systems”’ IEEE Power Electronic Letter, Vol.2, pp. 16-19, Mar.2004.

3. J.A.Jiang et. Al.,”Maximum Power Tracking for Photovoltaic Power Systems,” Tamkang Journal of Science and Engineering, Vol. 8, No. 2, pp. 147-153, 2005.

4. D. Rekioua and E. Matagne, Optimization of Photovoltaic Power Systems, Modelization, Simulation and Control, Springer, 2012.

5. Y.Kuo, et. Al., “Maximum power point tracking controller for photovoltaic energy conversion system”, IEEE Trans. Ind. Electron., Vol.48, pp. 594-601, 2001.

6. Rafia Akhter and Aminul Hoque, “Analysis of a PWM Boost Inverter for Solar Home Application”, Proceedings of World Academy Of Science, Engineering And Technology Volume 17 December 2006 ISSN 1307-6884.

7. Huan-Liang Tsai, Ci-Siang Tu and Yi-Jie Su, “Development of Generalized Photovoltaic Modeling Using MATLAB/Simulink “, Proceedings of the World Congress on Engineering and Computer Science 2008 WCECS 2008, October 22-24, 2008, San Francisco, USA.

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Volume-5 Issue-3

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S. No

Volume-5 Issue-3, July 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Issa Khalil ALHasanat, Ayman A. Rahim A. Rahman

Paper Title:

The Fact of use Mobile Learners at the Arab Open University in Learn Arabic language

Abstract: This study aims to investigate the fact of the use of mobile learning at Arab Open University students in order to help them for learn Arabic, and study sample consisted of (245) students, who study decision Arabic for primary school teachers, was applied to identify prepared by the researcher on students to identify these uses, and contain this questionnaire four axes, namely: mobile phones used by students, and mobile services that benefit students to learn Arabic, and the purposes for which Students use mobile phones, and the obstacles faced by the students’ learning through mobile phones (m-phones) to learn Arabic. The study concluded with a set of recommendations to contribute to improvie the use of the Arabic language learning via mobile devices, based on what resulted from the results of the study.[1]

Keywords:
Smartphones, technology, instructional aide


References:

1. Heyasat Ahmad (2009), “Showing the third generation cellular companies”alray newspaper 3 July P 24
2. (Recycling in the Heart of Dixie: eCycle Best? s Top 5 Recyclers in Alabamaby Andrew Del Prado On Jan. 7, 2015)

3. Goh,Kinsguk, (2006)”Getting Ready for Mobile Learning-Adaptation Perspective”,JI of Educational Multimedia and Hypermadia,Vol.15.No.(2),pp.175-198

4. Yu-Shun Wang, Ming-Cheng Wu and Hisu-YuanWang. (2009).Investigating the determinants and age and gender differences in the acceptance of mobile learning .British Journal of Educational Technology,Vol (40)No(1),pp.92-118.

5. Ria (2014).” THE USE OF SMARTPHONES AMONG STUDENTS IN RELATION TO THEIR EDUCATION AND SOCIAL LIFE “Nicoletti Morphitou University of Nicosia, Greece ,icicte2014 pp73-81

6. Jessica L. Buck, Elizabeth McInnis, Casey Randolph (2013),” The New Frontier of Education: The Impact of Smartphone Technology in the Classroom”. 2013 ASEE Southeast Section Conference.

7. Attewell.J(2005) Mobile Technologies and Learning. Technology Enhanced Learning Research Center. Published By the Learning and Skills Development Agency,UK.

8. Cavus,N and Dogan ,I.(2009).”M-Learning:An experiment in using SMS to support learning new English language words”.British Journal of Educational Technology.Vol 40.No 1.pp.78-91.

9. Corbett,S.(2008).”Can the cell phone help and global poverty?”.The New York Times. April 13, Retrieved from http://www.nytimes.com/2008/04/13/magazine/13 anthropologyt.html.

10. Mohamed,A(2009).Mobile Learning Transforming the Delivery of Education and Training,Issues in Distance Education Series,Published by AU Press,Athabasca University.

11. Prensky,M.(2005).”In What Can You Learn from a Cell Phone? Almost Anything”.Journal of online education,Vol 5,No(1),pp.34-41.

12. Quinn C (2000). “mLearning:mobile ,wireless,in your-pocket learning LineZine.Fom:www.linezine.com/2.1/features /cqmmwipy.htm,

13. Shuler, C.(2009).Pockets of Potential:Using Mobile Technologies to Promote Children’s Learning,The Joan Ganz Coony Center at Sesame workshop .New York.

14. Sharples M(2000). “The design of personal mobile technologies for lifelong learning”. Computers and Education, Vol34, No(2),pp.177-193.

15. Siau,K,Lim,E.-P.&Shen,Z.(2001).”Mobile commerce: promises, challenges,and research agenda “.Journal of Database Management,Vol12,No(3),pp.4-13.

16. Rene, J. E. (2011). Handheld education: Applied mobile technology. Choice, 48(9), 1605-1608,

17. Martin, F., Pastore, R., & Snider, J. (2012). Developing mobile based instruction. TechTrends,56(5), 46-51. doi: http://dx.doi.org/10.1007/s11528-012-0598-9

18. Miangah, T.M. & Nezarat, A. (2012). Mobile-assisted language learning. International Journal of Distributed and Parallel Systems, 3(1), 309-319.

19. Begum, R. (2011). Prospect for cell phones as instructional tools in the EFL classroom: A case

20. Study of Jahangirnagar University, Bangladesh. English Language Teaching, 4(1), 105-115.

21. Gikas, J. Grant, M. M. (2013, October). Mobile computing devices in higher

22. Education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18-26.

23. Benson, V., & Morgan, S, (2013). Student experience and ubiquitous learning

24. Wankel, L. A., & Blessinger, P. (2013). Increasing student engagement and

25. Saylor. M. (2012). The mobile wave: how mobile intelligence will change everything.

26. Freeman, K. (2012). Low income students’ test scores leap 30% with smartphone use.

27. Marshable US & World. Retrieved from

28. http://mashable.com/2012/10/15/wireless-reach-

 

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2.

Authors:

Ashwini B. M, Y. P. Gowramma

Paper Title:

Implementation of Encrypted Visual Cryptographic Shares using RSA Algorithm on FPGA

Abstract: The project presents an approach for encrypting visual cryptographically generated image shares using RSA algorithm. The Visual Cryptography Scheme is a secure method that encrypts a secret document or image by breaking it into shares. A distinctive property of Visual Cryptography Scheme is that one can visually decode the secret image by superimposing shares without computation. By taking the advantage of this property, third person can easily retrieve the secret image if shares are passing in sequence over the network. RSA algorithm is used for providing the double security of secret document. The RSA is a new method to encrypt the data by using private and public keys. Thus secret share are not available in their actual form for any alteration by the adversaries who try to create fake shares. The scheme provides more secure secret shares that are robust against a number of attacks & the system provides a strong security for the handwritten text, images and printed documents over the public network. Field Programmable Gate Arrays (FPGAs) are widely used to implement special purpose processors. FPGAs are economically cheaper for low quantity production because its function can be directly reprogrammed by end users. The aim of this project is to design a hardware on which we can encrypt/decrypt a confidential data using visual cryptography and RSA algorithm. in order to reduce the hardware consumption, here I have designed the FPGA such that, we can encrypt the part of the image at a time and we are going to repeat the process until all pixels are encrypted/decrypted.

Keywords:
Visual Cryptography; Encryption; Information Security; VCshares


References:

1. Parakhand S. kak“A Recursive Threshold Visual Cryptography Scheme” .Department of Computer Science, Oklahoma State University Stillwater,OK74078.
2. Padhmavati, P. Nirmal Kumar, M. A. Dorai Rangaswamy“A Novel Scheme for Mutual Authentication and Cheating Prevention in Visual Cryptography Using Image Processing”. Department of Computer Science &Engineering, Easwari Engineering College, Chennai, DOI: 02, ACS.2010.01.264, 2010 ACEEE.

3. Chandramathi S, Ramesh Kumar R., Suresh R. and HarishS. “An overview of visual cryptography” International Journal of Computational intelligenceTechniques, ISSN: 0976–0466&E-ISSN: 0976–0474Volume 1, Issue 1, 2010, PP-32-37

4. Jenaand S. Jena “A Novel Visual Cryptography Scheme”.

5. Néelima. Guntupallieta “An Introduction to Different Types of Visual Cryptography Schemes” ,International Journal of Science and Advanced Technology (ISSN2221-8386),Volume 1No7September 2011,PP198-205.

6. M. Naorand A. Shamir “Visual Cryptography”. Advances in Cryptology EUROCRYPT ’94. Lecture Notesin Computer Science, (950):1–1, 1995.

7. M. Nakajima and Y. Yamaguchi “Extended Visual Cryptography for Natural Images” .Department of Graphic and Computer Sciences, Graduate School of Arts and sciences, the University of Tokyo 153-8902, Japan.

8. P.S.Revenkar, Anisa Anjum, W.Z.Gandhare “Survey of Visual Cryptographic Schemes”. International Journal of Security and Its ApplicationsVol.4, No.2, April, 2010.

9. Shyamalendu Kandar & Arnab Maiti “K-Secret Sharing Visual Cryptography Scheme for Color Image Using Random Number”. International Journal of Engineering Science and Technology (IJEST), ISSN0975-5462, Vol.3 No.3Mar201, PP1851-1857

10. Ujjwal Chakrabortyet al, “Design and Implementation of a (2,2)and a(2,3) Visual Cryptographic Scheme” International Conference [ACCTA-2010],Vol.1Issue2,3,4, PP128-134

11. Vaibhav Choudhary “An Improved Pixel Sieve Method for Visual Cryptography” International Journal of Computer Applications,(0975–8887)Volume12–No.9,January2011.

12. Wei-QiYan, DuoJin, Mohan S Kankanhalli “Visual Cryptography for print and scan applications “School of Computing ,National University of Singapore,Singapore117543

13. Y. Bani, Dr. B. Majhiand R.S. Mangrulkar,2008. A Novel Approach for Visual Cryptography Using a Watermarking Technique. In Proceedings of 2nd National Conference ,India Com2008.

14. Behrouz A. Forouzon, “Cryptography & Network security”4th Edition.

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3.

Authors:

Alhamzah Taher Mohammed

Paper Title:

Design and Enhancement of Space-Time Block-Code for MC-CDMA OFDM by Phase Matrix in Flat and Selective Fading Channels

Abstract: In this paper, we combine a space-time block code (STBC) with a multi-carrier code division multiple access (MC-CDMA) system. MC-CDMA is probable to be one of the most promising access methods for future wireless communication schemes. In fact, MC-CDMA achievements the benefits of both the orthogonal frequency division multiplex (OFDM) multi-carrier modulation and of the code division multiple access (CDMA) technique. A development of space-time, block-coded (STBC) multicarrier code-division multiple-access (MC-CDMA) system using phase matrix in multipath fading channel is proposed, and the performance of the system is analyzed. The bit error rates BER numerical results show that the better performance of the STBC-MC-CDMA system with phase matrix can be achieved when comparing with system without using phase matrix. As a result, it can be seen from the proposed technique that a high performance improvement was obtained over the conventional MC-CDMA, where the Bit Error Rate (BER) is mainly reduced under different channel characteristics for frequency selective fading and the AWGN channel.

Keywords:
STBC, OFDM, MC-CDMA, OFDM, IFFT, DFT, Phase matrix.


References:

1. S. Hara and R. Prasad, “Design and performance of multicarrier CDMA system in frequency selective Rayleigh fading channels,” IEEE Transactions on Vehicular Technology, vol. 48, no.5, pp.1584-1595, Sep. 1999., 1999.
2. H. H. Ajra, Md. Zahid; Islam, Md. Shohidul,, “BER Analysis of Various Channel Equalization Schemes of a QO-STBC Encoded OFDM based MIMO CDMA System,” International Journal of Computer Network & Information Security;Feb2014, Vol. 6 Issue 3, p30, 2014.

3. S.Hara and R. Prasad, “Overview of multi carrier CDMA,” IEEE Communication Magazine, vol. 35, no.12, pp. 126- 133, Dec. 1997., 1997.

4. W. A. C. F. a. M. K. W. S. Chatterjee, “Adaptive modulation based MC-CDMA systems for 4G wireless consumer applications,” IEEE Transactions on Consumer Electronics, vol.49, no.4, pp.995–1003, Nov. 2003., 2003.

5. C. S. Leandro D’Orazio, Massimo Donelli, Jérôme Louveaux and Luc Vandendorpe,, “A Near-Optimum Multiuser Receiver for STBC MC-CDMA Systems Based on Minimum Conditional BER Criterion and Genetic Algorithm-Assisted Channel Estimation,” EURASIP Journal on Wireless Communications and Networking, 2011.

6. G. Foschini and M. Gans, “On the limits of wireless communications in a fading environment when using multiple antenna, ,” Wireless Personal Commuication, vol. 6, no. 3, pp. 311-335, Mar. 1998., 1998.

7. S.M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Selected Areas in Communication, vol.16, no.8, pp. 1451-1458, Oct. 1998., 1998.

8. F. T. Inoue M, Nakagawa M,, “Space time transmit site diversity for OFDM multi- base station system. ,” International Workshop on Mobile and Wireless Communications Network, Sweden, 30-34, 2002.

9. S. K. P. Sadananda Behera, “Performance Analysis of Low-Complexity Multiuser STBC MC-CDMA System,” Intelligent Computing, Communication and Devices Advances in Intelligent Systems and Computing vol. Volume 309, , pp 223-228, 2014.

10. N. Kumaratharan, “STTC based STBC site diversity technique for MC-CDMA system,” Computing Communication and Networking Technologies (ICCCNT), 2010 International Conference on, 2010.

11. Zhi Zliang and Li Guoqing, “A Novel Decoding Algorithm of STBC for CDMA Receiver in Multipath Fading Environments,” ” IEEE Trans. on Comm., vol. 49, pp. 1956-1959, April 2001., 2001.

12. Y. J. H. Salih Mohammed Salih, Talib Mahmoud Ali,, “A Proposed Improvement Model for MC-CDMA in Selective Fading Channel,” Anbar Journal of Engineering Sciences (AJES-2009), vol. AJES, Vol. 2, No. 1, 2009.

 

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4.

Authors:

Alhamzah Taher Mohammed

Paper Title:

Zvezdan Stojanović, Dušan Savić

Abstract: new services, like IPTV, VoD, broadband access to Internet have very high demand for the bandwidth. xDSL technologies are mainly used as solution for this damand by the greatest operators in Bosnia and Herzegovina (BH Telecom, M:TEL). Thattechnologies have restriction regarding from the distance between central office (CO) where is operator’s equipment and subsribers. Solution for this problem is some form of the next generation access (NGA) technology which is used in European Union (EU). In this paper is made comparison between situation with broadband technologies in European Union and BiH with possible direction of development. It is described why broadbandtechnologies in access network is so important.

Keywords:
triple play, quadruple play, NGA, FTTx


References:

1. European Commission, Broadband markets, Digital Agenda Scoreboard for 2014.
2. European Commission, State of the telecoms services in Europe, e-Scoreboard 2013.

3. ITU-T G.984.1, Gigabit capable passive optical networks (GPON), General characteristics, 2008.

4. H.Widiger, A.Strzeletz, D. Timmermann, Evaluation of Dynamic Bandwidth Allocation Algorithms for G-PON Systems using a reconfigurable Hardware Testbed, Institute of Applied Microelectronics and Computer Engineering University of Rostock, 18051 Rostock, Germany, 2008.

5. V.A Raspopovic, G. Markovic, V.Radonjic, Pasivne optičke mreže za pristup, 25th PosTel, Beograd, pp 291-302.

6. S.Han, W. Yue and S. Smith, FTTx and xDSL: A Business Case Study of GPON versus Copper for Broadban Access Networks, Fujitsu, technical documentations, 2006.

7. CRA, The Annual Report of the Communications Regulatory Agency, Sarajevo, 2013.

8. J. Prience, “The Dynamic Effects of Triple Play Bundling in Telecommunications”, Research Program of Digital Communications, by Time Warner Cable, 2012.

9. http://www.bhtelecom.ba/

10. http://www.mtel.ba/

11. http://www.hteronet.ba/

 

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5.

Authors:

Ali Mirshahi, Hashem Mirzaei Najafi, Mohammad-R. Akbarzadeh-T, Maryam Ebrahimi Nik

Paper Title:

Automatic Quality Enhancement of Radiographic Images by Fuzzy Logic

Abstract: Although much progress has been made in X-ray imaging, conventional radiography is still used in many developing countries as well as less developed countries due to its lower cost and availability. These conventional approaches are however significantly influenced by multiple factors such as sensor and environmental noises, age of developer and fixing materials, exposure factors and the experience of the operator. The goal of this study is to apply a novel post processing technique to get digital image advantages with conventional radiographic images. Specifically, we propose a novel fuzzy system to create a standard gray scale level image. As a result, image details are clearer and can be better enhanced by morphological edge operations. This image enhancement can lead to faster and more accurate interpretation by medical professionals. A number of experiments on rats, rabbits, and birds confirm utility of the proposed approach..

Keywords:
Computer-Assisted, Fuzzy Logic, Radiography, Image Enhancement.


References:

1. L. Jian, P. Bao-Chang, H. Yong-Hui, F. Xiao-Yan, and T. Jian-Hui, “Image segmentation of bone in X-ray pictures of feet”, In: IEEE 2009 Wavelet Analysis and Pattern Recognition, 12-15 July 2009, Baoding, China. New York, NY, USA: IEEE, pp. 57-61.
2. P. Peloschek, S. Nemec, P. Widhalm, R. Donner, Birngruber E, Thodberg HH, Kainberger F, Langs G. “Computational radiology in skeletal radiography,”. Eur J. Radiol 2009, pp. 252-257.

3. T-R. Song, and W. Zheng, “The research of X-ray bone fracture image enhancement algorithms,” In: IEEE 2010 Computer, Mechatronics, Control and Electronic Engineering (CMCE), 24-26 August 2010, Changchun, China. New York, NY, USA: IEEE, pp. 384-387.

4. Frosio, M. Lucchese, F. Lissandrello, and NA. Borghese, “Automatic contrast enhancement in digital radiography,” In: IEEE 2008 Nuclear Science Symposium Conference Record, 2008 NSS ’08 IEEE; 19-25 October 2008, Dresden, Germany. New York, NY, USA: IEEE, pp. 4331-4334.

5. BS. Sharif, SA. Zaroug, EG. Chester, JP. Owen, and EJ. Lee, “Bone edge detection in hand radiographic images,” In: IEEE 1994 Engineering in Medicine and Biology Society, 1994 Engineering Advances: New Opportunities for Biomedical Engineers Proceedings of the 16th Annual International Conference of the IEEE, 3-6 November 1994, Baltimore, MD, USA. New York, NY, USA: IEEE, pp. 514-515.

6. MS. Dinesh, Bhanuprakash, and R. Ashok, “Vision system for bone measurement from digital hand radiograph,” In: IEEE 1995 Engineering in Medicine and Biology Society, 1995 and 14th Conference of the Biomedical Engineering Society of India An International Meeting, Proceedings of the First Regional Conference, IEEE, 15-18 February 1995, New Delhi, India. New York, NY, USA: IEEE, pp. SPC9-SP10.

7. RS. Gaborski, and BK. Jang, “Enhancement for computed radiographic images,” In: IEEE 1995 Computer-Based Medical Systems, 1995, Proceedings of the Eighth IEEE Symposium on, 9-10 June 1995, Lubbock, TX, USA. New York, NY, USA: IEEE, pp. 27-34.

8. Y. Lujun, J.K Chang, and A. Basu, “Synthesis-based scalable image enhancement for digital radiography,” In: IEEE 2002 Image Processing 2002 Proceedings 2002 International Conference on, 22-25 September 2002, Rochester, NY, USA. New York, NY, USA: IEEE, pp. II-973-II-976.

9. H. Mirzaei, M. Jafari, and A. Mirshahi, “Considering the effect of using JPEG images on accuracy results of radiology images and application programs,” IJSCE 2013, pp. 489-492.

10. S. Soroori, F. Hosseini, A. Zamani-Moghaddam, M. Hosseininejad, I. Karimi, M. Masoudifard, and MM. Dehghan, “Assessment of avian osteoporosis by a quantitative radiographic method,” IRAN J VET RES 2012, pp. 317-322.

11. WN. Brown, “Bone density computing machine,” Proceedings of the National Electronics Conference, 26-28 September 1949, Chicago, USA. pp. 64-71.

12. GC. Henny, “Roentgenographic estimation of the mineral content of bone,” Radiology 1950, pp. 202-210.

13. HE. Meema, CK. Harris, and RE. Porrett, “A method for determination of bone-salt content of cortical bone,” Radiology 1964, pp. 986-997.

14. AH. Mark, YS. Hazel, Y S, and AF. John, “Computerized methods for X-ray-based small bone densitometry,” Comput Meth Prog Bio 2004, 35-42.

15. RC. Gonzalez, and RE. Woods, Digital image processing. Upper Saddle River, NJ, USA: Prentice Hall, 2008.

16. J-SR. Jang, C-T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Upper Saddle River, NJ, USA: Prentice Hall, 1997.

 

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6.

Authors:

Fraol Bekana, J. Mehedi

Paper Title:

A Review of Clustering Schemes for Mobile Ad Hoc Networks

Abstract: Due to its vast application, maintaining the connectivity and forwarding the information in mobile ad hoc network (MANET) is very crucial to increase the efficiency as well as the performance of the system. One way of guarantying this performance to a large and dynamic network is through clustering. A number of researchers came up with a variety of approaches and performance metrics for ad hoc clustering. In this paper, we have presented a comprehensive review of various proposed clustering schemes for MANET. The classification and analysis of these schemes are done depending on their cluster formation. Descriptions of their approaches, evaluations of their performance, discussions of their advantages and disadvantages of each clustering schemes are presented. We believe that this paper will enable readers to get more understanding of ad hoc clustering and indicate research trends in the area.

Keywords:
Clustering, CDS, Mobile ad hoc Networks


References:

1. Sze-Yao Ni, Yu-Chee Tseng, Y.S. Chen, and J.P. Sheu, The broadcast storm problem in a mobile ad hoc network, In Proc. of the 5th ACM/IEEE Int. Conference on Mobile Computing and Networking (MobiCom ’99), pages 151–162, New York, NY, USA, 1999.
2. C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks,” IEEE JSAC, vol. 15, Sept. 1997, pp. 1265–75.

3. C.R. Lin and M. Gerla, “A Distributed Control Scheme in Multi-hop Packet Radio Networks for Voice/Data Traffic Support,” Proc. IEEE ICC’95, pp. 1238–42, June 1995.

4. T.C. Hou and T.J. Tsai, “An Access-Based Clustering Protocol for Multihop Wireless Ad Hoc Networks,” IEEE JSAC, vol. 19, no. 7, pp. 1201–10, July 2001.

5. Yu .J.P and Chong P.H.J, A Survey of Clustering Schemes for Mobile Ad Hoc Networks, IEEE Communications Surveys and Tutorials, Vol. 7, No. 1, pp. 32-48, 2005.

6. J. Wu, Ming Gaol, and Ivan Stojmenovic, “On Calculating Power-Aware Connected Dominating Sets for Efficient Routing in Ad Hoc Wireless Networks,” J. Commun. and Networks, vol. 4, no. 1, pp. 59–70, Mar. 2002.

7. J.H. Ryu, S. Song, and D.H. Cho, “New Clustering Schemes for Energy Conservation in Two-Tiered Mobile Ad-Hoc Networks,” in Proc. IEEE ICC’01, vo1. 3, pp. 862–66, June 2001.

8. M. Chatterjee, S.K. Das, and D. Turgut, “An On-Demand Weighted Clustering Algorithm (WCA) for Ad hoc Networks,” in Proc. IEEE Globecom’00, pp. 1697–701, 2000.

9. A.B. McDonald and T.F. Znati, “Design and Performance of a Distributed Dynamic Clustering Algorithm for Ad-Hoc Networks,” in Proc. 34th Annual Simulation Symp., pp. 27–35, Apr. 2001.

10. T. Ohta, S. Inoue, and Y. Kakuda, “An Adaptive Multihop Clustering Scheme for Highly Mobile Ad Hoc Networks,” in Proc. 6th ISADS’03, Apr. 2003.

11. D.J. Baker and A. Ephremides, A distributed algorithm for organizing mobile radio telecommunication networks, in: Proceedings of the 2nd International Conference on Distributed Computer Systems, pp. 476–483, April 1981.

12. Ephremides, J.E. Wieselthier and D.J. Baker, A design concept for reliable mobile radio networks with frequency hopping signaling, in: Proceedings of IEEE, Vol. 75(1) (1987) 56–73.

13. M. Gerla and J.T. Tsai, “Multiuser, Mobile, Multimedia Radio Network,” Wireless Networks, vol. 1, pp. 255–65, Oct. 1995.

14. D. Gavalas, G. Pantziou, C. Konstantopoulos, Basilis Mamalis, Stable and Energy Efficient Clustering of Wireless Ad-Hoc Networks with LIDAR Algorithm, P. Cuenca and L. Orozco-Barbosa (Eds.): PWC 2006, LNCS 4217, pp. 100–110, 2006.

15. CH.V. Raghavendran, G. Naga Satish, P. Suresh Varma, I.R. Krishnam Raju , Enhancing the Performance of Routing in Mobile Ad Hoc Networks using Connected Dominating Sets, Special Issue of International Journal of Computer Applications, pp. 0975 – 8887, 2012.

16. D.J. Baker and A. Ephremides, The architectural organization of a mobile radio network via a distributed algorithm, IEEE Transactions on Communications COM-29 11 (1981) 1694–1701.

17. C.S. Victor and G. Amalanathan, Construction of Strategic Connected Dominating Set For Mobile Ad Hoc Networks, Journal of Computer Science 10 (2): 285-295, 2014.

18. Yongsheng Fu, Xinyu Wang, Shanping Li, Construction K-Dominating Set with Multiple Relaying technique in Wireless Mobile Ad Hoc Networks, IEEE Computer Society; International Conference on Communications and Mobile Computing, 2009.

19. Ling Ding et al, Distributed Construction of Connected Dominating Sets with Minimum Routing Cost in Wireless Networks, IEEE Computer Society;International Conference on Distributed Computing Systems, 2010.

20. Kazuya Sakai, Scott C.H. Huang, Wei-Shinn Ku, Min-Te Sun, and Xiuzhen Cheng, Timer-Based CDS Construction in Wireless Ad Hoc Networks, IEEE Transactions on Mobile Computing, Vol. 10, No. 10, October 2011.

21. R. Zheng and R. Kravets, “On-demand Power Management for Ad Hoc Networks,” in Proc. IEEE Infocom’03, vol. 1, pp. 481–91, Mar.–Apr. 2003.

22. J. Gomez et al., “PARO: Supporting Dynamic Power Controlled Routing in Wireless Ad Hoc Networks,” Wireless Networks, vol. 9, pp. 443–60, 2003.

23. J. Wu and H. Li. On calculating connected dominating set for efficient routing in ad hoc wireless networks. Proc. of the 3rd Int ’1 Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, pages 7-14, 1999.

24. Naixue Xiong, Xingbo Huang, Hongju Cheng, and Zheng Wan, “Energy-Efficient Algorithm for Broadcasting in Ad Hoc Wireless Sensor Networks” Sensors 2013, 13, 4922-4946; doi: 10.3390/s130404922.

25. Mritunjay Rai, Shekhar Verma, Shashikala Tapaswi, A Power Aware Minimum Connected Dominating Set for Wireless Sensor Networks, Journal of Networks, Vol. 4, No. 6, August 2009.

26. P. Basu, N. Khan, and T.D.C. Little, “A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks,” in Proc. IEEE ICDC-SW’01, pp. 413–18, Apr. 2001.

27. C.C. Chiang, H.K. Wu, W. Liu, M. Gerla, “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel,” Proceedings of SICON 1997.

28. Yan Zhang, Jim Mee Ng, Chor Ping Low, A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks, Elsevier, Computer Communications, Vol. 32, pp. 189–202, 2009.

29. M. Chatterjee, S. Dasand D. Turgut, “WCA: a weighted clustering algorithm for mobile ad hoc networks,” Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), 5, pp.193-204, 2002.

30. Mohamed Aissa, Abdelfettah Belghithb and Khalil Drira, New strategies and extensions in weighted clustering algorithms for mobile Ad Hoc networks, Procedia Computer Science 19 ( 2013 ) 297 – 304.

31. M. Amine Abid, Abdelfettah Belghith, Stability routing with constrained path length for improved routability in dynamic MANETs, The International Journal of Personnal and Ubiquitous Computing, Springer; Volume 15, Issue 8 (2011).

32. S.K.B. Rathika and J. Bhavithra, An Efficient Fault Tolerance Quality of Service in Wireless Networks Using Weighted Clustering Algorithm; Bonfring International Journal of Research in Communication Engineering; Vol. 2, Special Issue 1, Part 4; February 2012.

33. W. Jin,et al. A load-balancing and energy-aware clustering algorithm in wireless ad-hoc networks; Embedded and Ubiquitous Computing EUC 2005 Workshops; Lecture Notes in Computer Science Volume 3823; pp 1108-1117; 2005.

34. Xi’an Jiaotong, et al. WACHM: Weight Based Adaptive Clustering for Large Scale Heterogeneous MANET; Communications and Information Technologies; ISCIT ’07; 2007.

35. Hui Cheng, Jiannong Cao, Xingwei Wang, Sajal K. Das, Shengxiang Yang, Stability-aware Multi-Metric Clustering In Mobile Ad Hoc Networks With Group Mobility, Wireless Communications and Mobile Computing; 9:759 771, Published online 21 April 2008 in Wiley InterScience.

36. Christian Bettstetter, The Cluster Density of a Distributed Clustering Algorithm in Ad Hoc Networks, IEEE International Conference Communications; Page(s): 4336 – 4340 Vol.7; 2004.

37. Wei-dong Yang, Guang-zhao Zhang, A Weight-Based Clustering Algorithm for mobile Ad Hoc network, IEEE Computer Society, 2007.

38. Yang Wei-dong, Weight-Based Clustering Algorithm for Mobile Ad Hoc Network, IEEE: Cross Strait Quad-Regional Radio Science and Wireless Technology Conference; July 26-30, 2011.

39. Seiven Leu, Ruay-Shiung Chang, A weight-value Algorithm for Finding Connected Dominating Sets in a MANET, Journal of Network and Computer Applications, Vol. 35, pages 1615-19, March 2012.

 

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7.

Authors:

Majzoob Kamal Aldein Omer, Mohmed Sirelkhtem Adrees, Osama E. Sheta

Paper Title:

Alternative Central Mobile Application Strategy to Deaf and Dumb Education in Third World Countries

Abstract: The study aims to apply the strategy to help deaf students and dumb in academic achievement by using mobile learning technology application , This sample of the students have a high potential for the use of mobile applications and has a capacity of great learning via mobile. Smart mobile phones have the ability to create a good educational content of images, shapes, graphics and illustrations appropriate signs to the Deaf and Dumb students and the production of educational content suitable for individual differences in education between them and meets their needs mental and their interests that are different from ordinary students in Education. The paper focuses on the educational content of the component images, graphs, and illustrations appropriate signs to the Deaf and Dumb students because it is not easy to understand by a normal listener on the opposite and to make things worse. In fact the technology is used to achieve the interaction between deaf and dump children with others.

Keywords:
Educational Content, Deaf and Dumb Student, Scalability, Integration, home user, institute user, provider user


References:

1. N.Hema , Ms. P. Thamarai , Dr. T.V.U. KiranKumar ( 2013 ), Handheld Deaf and Dumb Communication Device based on Gesture to Voice and Speech to Image/Word Translation with SMS Sending and Language Teaching Ability .
2. R. E. Mitchell and M. A. Karchmer. Chasing the mythical ten percent: Parental hearing status of deaf and hard of hearing students in the United States. Sign Language Studies, 4(2):138{163, 2004.

3. Morford, Jill Patterson, and MacFarlane, James, Winter 2003,”Frequency Characteristics of AmericanSign Language”.Sign Language Studies, Volume 3, Number 2, pp. 213-225

4. S. Zhao, M. Wang, Z. Wei, 2013 “ A New Type of Deaf-Mute Sign Language Recognition System Based on the Mobile Communication Platform and Terminal Equipment”

5. Dalia Nashat, Abeer Shoker, Fowzyah Al-Swatand Reem Al-Ebailan , (2014) , AN ANDROID APPLICATION TO AIDUNEDUCATED DEAF-DUMB PEOPLE

6. Kimberly A. Weaver, Kimberly A. (2012), Mobile Sign Language Learning Outside the Classroom.

7. ITU release 2014 ICT – Geneva, 5 May 2014

 

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8.

Authors:

Abhishek M. Kinhekar, Parmalik Kumar

Paper Title:

Router Node Placement in Distributed Sensor Networks: A Review of Optimized Methods

Abstract: designing a distributed wireless sensor network can be an arduous task if not done with simulation tools. Simulation with a tool should provide a robust and efficient solution of the problem with quick response time but available simulation tools for designing these wireless sensor networks are very limited. Pugelli, Mozumdar, avagno and Sangiovanni-Vicentelli[1] proposed an interactive design tool that can assist rapid design of sensor network. This tool synthesizes networks using Dijkstra’s algorithm but its execution time is very high when the network size is relatively large (n>=50). Moreover, it produces sub-optimal solution with large number of router nodes. In this paper, we present efficient and robust synthesis algorithm that exclusively reduce running time.

Keywords:
wireless sensor network; router placement; synthesis algorithm; Simulation Tools


References:

1. Mohammad Mozumdar, Arun Ganesan, Alireza Ameri Daragheh, “Optimizing router node placement for desining Distributed Sensor networks” 2014 IEEE International Conference on Distributed Computing in Sensor Systems
2. Puggelli, M. Mozumdar, L. Lavagno, A. L. Sangiovanni-Vincentelli, “Routing-Aware Design of Indoor Wireless Sensor Network Using an Interactive Tool”, IEEE Systems Journal, Volume:PP, Issue:99, 2013, pp. 1-14.
3. Chih-Yung Chang, Jang-Ping Sheu, Senior Member, IEEE, Yu-Chieh Chen, and Sheng-Wen Chang, “An Obstacle-Free and Power-Efficient Deployment algorithm for wireless sensor networks”, IEEE Transactions on Systems, Man, and Cybernetics—part a: Systems and Humans, Vol. 39, No. 4, July 2009, 795
4. M. Gibney, M. Klepal, J. T. ODonnell, “Design of Underlying Network Infrastructure of Smart Building, Intelligent Environments”, 2008 IET 4th International Conference.
5. Y. Wang, C. Hu, Y. Tseng, “Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks”, in Proceedings of First International Conference on Wireless Internet
6. M. Mozumdar, F. Gregoretti, L. Lavagno, L. Vanzago, andS. Olivieri, “A Framework for Modeling, Simulation and Automatic Code Generation of Sensor Network Application”, Proc. of SECON, 2008, pp. 515–522.
7. M. Mozumdar, L. Lavagno, L. Vanzago, and Alberto L.Sangiovanni-Vincentelli. HILAC: A framework for Hardware In the Loop simulation and multi-platform Automatic Code Generation of WSN Applications”, In Proc. of SIES, pages 88-97, Italy, 2010.
8. Pavlos Papageorgiou, “Literature Survey on Wireless Sensor Networks”, pavlos@eng.umd.edu, July 16, 2003
9. Xuesong Liu, Burcu Akinci, and James H. Garrett, Ömer Akin, “Requirements for a computerized approach to plan sensor placement in the HVAC systems”, Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor)
10. Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit, “Harmony Search-based K-Coverage Enhancement in Wireless Sensor Networks”, World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering Vol:9, No:1, 2015
11. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli, “Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.
12. M. Gibney, M. Klepal, and J. T. O’Donnell, “Design of underlying networkinfrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.
13. Y. Wang, C. Hu, and Y. Tseng, “Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks,” in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.
14. A. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .

 

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9.

Authors:

Abhishek M. Kinhekar, Parmalik Kumar

Paper Title:

Router Nodes Placement Optimization for Designing a Distributed Sensor Network

Abstract: The recent advancements in Distributed Wireless Sensor Network has stimulated the need for the newer and enhanced version of algorithms, which will not only reduce the delay in the processing but also consumes much less power. Distributed Sensor networks are most employed and have much scope for their optimization in working. In this paper we explore to find and compare about wireless sensor network, router placement, synthesis algorithm and simulation tools of DWSN.

Keywords:
wireless sensor network; router placement; synthesis algorithm; Simulation Tools


References:

1. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli. “Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.
2. M. Gibney, M. Klepal, and J. T. O’Donnell. “Design of underlying network infrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.

3. Y. Wang, C. Hu, and Y. Tseng, “Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks,” in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.

4. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .

5. J. Chang, P. Hsiu, and T. Kuo. “Search-oriented deployment strategies for wireless sensor networks,” in 10th IEEE Int. Symp. on Object and Component-Oriented Real- Time Distributed Computing, 2007,(ISORC ‘07), 2007, PP. 164-171.

6. N. Akshay, M. P. Kumar, and B. Harish. “An efficient approach for sensor deployments in wireless sensor networks,” in Int. Conf. on Emerging Trends in Robotics and Communication Technologies, 2010, PP. 350-355.

7. X. Bai, S. Kumar, D. Xuan, Z. Yun, and T. H. Lai. “Deploying wireless sensors to achieve both coverage and connectivity,” in Proc. 7th ACM Int. Symp. On Mobile ad hoc Networking and Computing, 2006, PP. 131-142.

8. Y. Wang, C. Hu, and Y. Tseng. “Efficient deployment algorithms for ensuring coverage and connectivity of wireless Sensor networks,” in Int. Conf. on Emerging Trends in Robotics and Communication Technologies (INTERACT), 2010, PP. 114- 121.

9. T. Clouqueur, V. Phipatanasuphom, P. Ramanathan, and K. K. Saluja. “Sensor deployment strategy for target detection,” in Proc. 1st ACM Int. Workshop on Wireless Sensor Networks and Applications, 2002, PP. 42-48.

10. Elysium Ltd. “JPEG”. Internet: http://www.ipeg.org/ 2007[0ct. 2, 2013].

11. Mohammad Mozumdar, Arun Ganesan, Alireza Ameri Daragheh, “Optimizing router node placement for desining Distributed Sensor networks” 2014 IEEE International Conference on Distributed Computing in Sensor Systems
12. Puggelli, M. Mozumdar, L. Lavagno, A. L. Sangiovanni-Vincentelli: Routing-Aware Design of Indoor Wireless Sensor Network Using an Interactive Tool, IEEE Systems Journal, 2013 (Volume:PP, Issue:99), pp. 1-14.
13. Chih-Yung Chang, Jang-Ping Sheu, Senior Member, IEEE, Yu-Chieh Chen, and Sheng-Wen Chang, “An Obstacle-Free and Power-Efficient Deployment algorithm for wireless sensor networks”, ieee transactions on systems, man, and cybernetics—part a: systems and humans, vol. 39, no. 4, july 2009 795

14. M. Gibney, M. Klepal, J. T. ODonnell: Design of Underlying Network Infrastructure of Smart Building, Intelligent Environments,2008 IET 4th International Conference.

15. Y. Wang, C. Hu, Y. Tseng: Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks,in Proceedings. First International Conference on Wireless Internet

16. M. Mozumdar, F. Gregoretti, L. Lavagno, L. Vanzago, andS. Olivieri, A Framework for Modeling, Simulation and Automatic Code Generation of Sensor Network Application, Proc. of SECON ’08, pp. 515–522.

17. M. Mozumdar, L. Lavagno, L. Vanzago, and Alberto L.Sangiovanni-Vincentelli. HILAC: A framework for Hardware In the Loop simulation and multi-platform Automatic Code Generation of WSN Applications. In Proc. of SIES, pages 88-97, Italy, 2010.

18. Pavlos Papageorgiou, “Literature Survey on Wireless Sensor Networks”, pavlos@eng.umd.edu, July 16, 2003

19. Xuesong Liu, Burcu Akinci, and James H. Garrett, Ömer Akin, “Requirements for a computerized approach to plan sensor placement in the HVAC systems” © Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor)

20. Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit, “Harmony Search-based K-Coverage Enhancement in Wireless Sensor Networks” World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering Vol:9, No:1, 2015

21. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli.“Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.

22. M. Gibney, M. Klepal, and J. T. O’Donnell. “Design of underlying networkinfrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.

23. Y. Wang, C. Hu, and Y. Tseng, “Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks,” in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.

24. A. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .

 

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10.

Authors:

Hafiz Jabr Younis, Alaa Al Halees, Mohammed Radi

Paper Title:

Hybrid Load Balancing Algorithm in Heterogeneous Cloud Environment

Abstract: Cloud computing is a heterogeneous environment offers a rapidly and on-demand wide range of services to the end users.It’s a new solution and strategy for high performance computing where, it achieve high availability, flexibility, cost reduced and on demand scalability. The need to efficient and powerful load balancing algorithms is one of the most important issues in cloud computing to improve the performance. This paper proposed a hybrid load balancing algorithm to improve the performance and efficiency in heterogeneous cloud environment. The algorithm considers the current resource information and the CPU capacity factor and takes advantages of both random and greedy algorithms. The hybrid algorithm has been evaluated and compared with other algorithms using cloud Analyst simulator. The experiment results show that the proposed algorithm improves the average response time and average processing time compared with other algorithms.

Keywords:
Cloud Computing, Cloud Analyst, Scheduling algorithm, Virtual Machine Load Balancing.


References:

1. Florence, A.P. and V. Shanthi, Intelligent Dynamic Load Balancing Approach for Computational Cloud. International Journal of Computer Applications, 2013: p. 15-18.
2. Sharma, T. and V.K. Banga, Efficient and Enhanced Algorithm in Cloud Computing. International Journal of Soft Computing and Engineering (IJSCE), March 2013. 3 (1).

3. Zhang, Q., L. Cheng, and R. Boutaba, Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 2010. 1(1): p. 7-18.

4. Khatib, V. and E. Khatibi, Issues on Cloud Computing : A Systematic Review, in International Conference on Computational Techniques and Mobile Computing. 2012: Singapore.

5. Sareen, P., Cloud Computing: Types, Architecture, Applications, Concerns, Virtualization and Role of IT Governance in Cloud. International Journal of Advanced Research in Computer Science and Software Engineering, 2013. 3(3): p. 533-538.

6. O., K.S., I. F., and A. O., Cloud Computing Security Issues and Challenges. International Journal of Computer Networks (IJCN), 2011. 3(5): p. 247-255.

7. Sajid, M. and Z. Raza, Cloud Computing: Issues & Challenges, in International Conference on Cloud. 2013. p. 35-41.

8. Mohapatra, S., K.S. Rekha, and S. Mohanty, A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing. International
Journal of Computer Applications, 2013. 68.

9. Ray, S. and A. De Sarkar, Execution Analysis Of Load Balancing Algorithms In Cloud Computing Environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 2012. 2(5): p. 1-13.

10. Yao, J.H., Ju-hou, Load Balancing Strategy Of Cloud Computing Based On Artificial Bee Algorithm in Computing Technology and Information Management (ICCM). 2012, IEEE: Seoul. p. 185 – 189.

11. Shameem, P.M. and R.S. Shaji, A Methodological Survey on Load Balancing Techniques in Cloud Computing. International Journal of Engineering and Technology (IJET), 2013. 4(5): p. 3801-3812.

12. Behal, V. and A. Kumar, Cloud Computing: Performance Analysis Of Load Balancing Algorithms In Cloud Heterogeneous Environment, in Confluence The Next
Generation Information Technology Summit (Confluence). 2014, IEEE: Noida. p. 200 – 205.

13. Kaushik, V.K., H.K. Sharma, and D. Gopalani, Load Balancing In Cloud Computing Using High Level Fragmentation Of Dataset, in International Conference on Cloud, Big Data and Trust. 2013. p. 118-126.

14. Mehta, R., P. Yask, and T. Harshal, Architecture For Distributing Load Dynamically In Cloud Using Server Performance Analysis Under Bursty Workloads. 2012. 1(9).

15. Tiwari, M., K. Gautam, and K. Katare, Analysis of Public Cloud Load Balancing using Partitioning Method and Game Theory. International Journal of Advanced Research in Computer Science and Software Engineering, 2014. 4(2): p. 807-812.

16. Deepika, D. Wadhwa, and N. Kumar, Performance Analysis of Load Balancing Algorithms in Distributed System. Advance in Electronic and Electric Engineering, 2014. 4(1): p. 59-66.

17. Ratan, M. and J. Anant, Ant colony Optimization: A Solution of Load Balancing in Cloud. International Journal of Web & Semantic Technology (IJWesT), 2012. III.

18. Sethi, S., S. Anupama, and K. Jena, S, Efficient load Balancing in Cloud Computing using Fuzzy Logic. IOSR Journal of Engineering (IOSRJEN), 2012. 2(7): p. PP 65-71.

19. Hu, J., et al., A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment, in 3rd International Symposium on Parallel Architectures, Algorithms and Programming. 2010, IEEE. p. 89-96.

20. Sharma, T. and V.K. Banga, Proposed Efficient and Enhanced Algorithm in Cloud Computing. International Journal of Engineering Research & Technology (IJERT), 2013. 2(2).

21. Singh, A., R. Bedi, and S. Gupta, Design and implementation of an Efficient Scheduling algorithm for load balancing in Cloud Computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2014. 3(1).

22. cloudsim. cloudbus; Available from: http://www.cloudbus.org/cloudsim/.

23. Pakize, S.R., S.M. Khademi, and A. Gandomi, Comparison Of CloudSim, CloudAnalyst And CloudReports Simulator in Cloud Computing. International Journal of Computer Science And Network Solutions, 2014. 2: p. 19-27.

 

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11.

Authors:

Sanket Panda, Shaurya Nigam, Rohit Kumar, Mamatha HR

Paper Title:

A Performance Study of SIFT, SIFT-PCA and SIFT-LDA for Face Recognition

Abstract: Humans have the ability to identify faces instantly with minimum effort and inspired by this, Face Recognition (FR) tries to imitate this ability by using numerous effective algorithms and has been extensively developed in the last decade. FR has received a lot of attention because of its wide range of its applications. Since Humans store and retrieve images instantly when needed, FR imitates this procedure by holding images in a database and trains them to recognize faces. Although many impactful algorithms have been developed, they are not entirely effective in unconstrained settings. Hence, we thoroughly compare the SIFT method and its two variations SIFT-PCA and SIFT-LDA to prove that the variations are better alternatives to regular SIFT.

Keywords:
Face Recognition; SIFT; PCA; LDA.


References:

1. D.G. Lowe. Distinctive image features from scale-invariant key-points. International Journal of Computer Vision, 60(2):91–110, 2004.
2. AT&TDataset:http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

3. GrimaceDataset: http://cmp.felk.cvut.cz/~spacelib/faces/grimace.html

4. Isra’a Abdul, Ameer Abdul Jabbar, Jieang Tan “Adaptive PCA-SIFT matching approach for Face recognition application”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol I, pp 1-5, IMECS 2014.

5. Face 95: http://cmp.felk.cvut.cz/~spacelib/faces/faces95.html

6. Y. Ke and R. Sukthankar. Pca-sift: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, pages 506–513, 2004.

7. Alsaqre, Falah E., and Saja Al-Rawi. “Symmetry Based 2D Singular Value Decomposition for Face Recognition.” In Digital Information Processing and Communications, pp. 486-495. Springer Berlin Heidelberg, 2011

8. LINEAR DISCRIMINANT ANALYSIS – A BRIEF TUTORIAL S. Balakrishnama, A. Ganapathiraju.

9. L-F Chen, H-Y Mark Liao, M-T Ko, J-C Lin, and G-J Yu, “A new LDA-based face recognition system which can solve the small sample size problem”, Pattern Recognition, vol. 33, pp. 1713–1726, 2000.

10. De Carrera, Proyecto Fin. “Face recognition algorithms.” Master’s thesis in Computer Science, Universidad Euskal Herriko (2010).1

11. Wold, Svante, Kim Esbensen, and Paul Geladi. “Principal component analysis.” Chemometrics and intelligent laboratory systems 2, no. 1 (1987): 37-52.

12. Zhao, Wenyi, et al. “Face recognition: A literature survey.” Acm Computing Surveys (CSUR) 35.4 (2003): 399-458.

13. Bicego, Manuele, Andrea Lagorio, Enrico Grosso, and Massimo Tistarelli. “On the use of SIFT features for face authentication.” In Computer Vision and Pattern Recognition Workshop, 2006. CVPRW’06. Conference on, pp. 35-35. IEEE, 2006.

14. Križaj, Janez, Vitomir Štruc, and Nikola Pavešić. “Adaptation of SIFT features for robust face recognition.” In Image Analysis and Recognition, pp. 394-404. Springer Berlin Heidelberg, 2010.

15. M. Martinez, A. C. Kak, “PCA versus LDA”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, 2001, pp.228-233

16. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification. Wiley, New York (2001)

17. Face 96: http://cmp.felk.cvut.cz/~spacelib/faces/faces96.html

18. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proceedings of ComputerVision and Pattern Recognition, June 2003.

 

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12.

Authors:

Shalini Dutta, Sudhakar S. Jadhav

Paper Title:

Image Fusion Conspectus

Abstract: Technological advancements have brought extensive research in the field of Image Fusion. Image fusion is the process of amalgamation of relevant information from a set of input images into a single image which in turn is better informative, complete and accurate. This paper presents an overview of Image Fusion. The silhouette of the paper is anticipated to cover Image fusion right from its inception till the future research prospects. This covers the various fusion systems and techniques of image fusion such as Spatial Domain methods like Weighted Pixel Averaging, Select Maximum/Minimum, Principal Component Analysis (PCA), Frequency/Transform Domain methods like Pyramid Decomposition (Laplacian, FSD, Ratio, Gradient, Morphological), Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) based image fusion. A comparative study of various image fusion techniques and their analyzed results are enlisted. Vivacious applications of image fusion also are highlighted as well. The compendium is concluded with the analysis of better approach as a result of the comparative study and the future scope of research perseveres.

Keywords:
Image Fusion, Discrete Wavelet Transform (DWT), Weighted Pixel Averaging, Select Maximum/Minimum, Principal Component Analysis (PCA), Pyramid Methods, Artificial Neural Network (ANN)


References:

1. Dr. S.S.Bedi and Rati Khandelwal, “Comprehensive and comparative study of image fusion techniques”, International Journal of Soft Computing and Engineering (IJSCE), IISN: 2231-2307,Volume-3,Issue-1,March 2013
2. Deepak Kumar Sahu and M.P.Parsai, “Different image fusion techniques-A critical review”, International Journal of Modern Engineering Research (IJMER), Vol. 2, Issue. 5, Sep.-Oct. 2012, pp-4298-4301

3. Qiguang Miao, “New Advances in Image Fusion”, ISBN 978-953-51-1206-8, Published: November 20, 2013 under CC BY 3.0 license. © The Author(s).

4. http://www.ece.lehigh.edu/SPCRL/IF/image_fusion.htm.

5. P.J. Burt, “The pyramid as a structure for efficient computation, in: A. Rosenfeld (Ed.), Multiresolution Image Processing and Analysis”, Springer-Verlag, Berlin, 1984, pp. 6–35.

6. E.H. Adelson, “Depth-of-Focus Imaging Process Method, United States” Patent 4,661,986 (1987).

7. Toet, “Image fusion by a ratio of low-pass pyramid, Pattern Recognition Letters” (1989) 245–253

8. R.D. Lillquist, “Composite visible/thermal-infrared imaging apparatus. United States” Patent 4,751,571 (1988).

9. P. Ajjimarangsee, T.L. Huntsberger, “Neural network model for fusion of visible and infrared sensor outputs, in: P.S. Schenker (Ed.), Sensor Fusion, Spatial Reasoning and Scene Interpretation”, The International Society for Optical Engineering, 1003, SPIE, Bellingham, USA, 1988, pp. 153–160

10. N. Nandhakumar, J.K. Aggarwal, “Integrated analysis of thermal and visual images for scene interpretation”, IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (4) (1988) 469–481

11. S.K. Rogers, C.W. Tong, M. Kabrisky, J.P. Mills, “Multisensor fusion of ladar and passive infrared imagery for target segmentation”, Optical Engineering 28 (8) (1989) 881–886

12. L.J. Chipman, Y.M. Orr, L.N. Graham, “Wavelets and image fusion”, in: Proceedings of the International Conference on Image Processing, Washington, USA, 1995, pp. 248–251

13. Koren, I., Laine, A., Taylor, F,”Image fusion using steerable dyadic wavelet”. In: Proceedings of the International Conference on Image Processing, Washington, USA, 1995. pp. 232–235

14. Deepali A.Godse, Dattatraya S. Bormane (2011) “Wavelet based image fusion using pixel based maximum selection rule” International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 7 July 2011, ISSN : 0975-5462

15. Susmitha Vekkot, and Pancham Shukla “A Novel Architecture for Wavelet based Image Fusion”. World Academy of Science, Engineering and Technology 57 2009

16. Shih-Gu Huang, “Wavelet for Image Fusion”

17. Yufeng Zheng, Edward A. Essock and Bruce C. Hansen, “An Advanced Image Fusion Algorithm Based on Wavelet Transform – Incorporation with PCA and Morphological Processing”

18. Gonzalo Pajares, Jesus Manuel de la Cruz “A wavelet-based image fusion tutorial” 2004 Pattern Recognition Society.

19. Chetan K. Solanki Narendra M. Patel, “Pixel based and Wavelet based Image fusion Methods with their Comparative Study”. National Conference on Recent Trends in Engineering & Technology. 13-14 May 2011.

20. M .Chandana,S. Amutha, and Naveen Kumar, “A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform”. International Journal of Research and Reviews in Computer Science (IJRRCS) Vol. 2, No. 4, August 2011, ISSN: 2079-2557

21. V.P.S. Naidu and J.R. Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis”. Defence Science Journal, Vol. 58, No. 3, May 2008, pp. 338-352 Ó 2008, DESIDOC

 

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13.

Authors:

Adnan Hussein Ali

Paper Title:

Performance Evaluation of Wi-Fi Physical Layer Based QoS Systems on Fiber Using OPNET Modeler

Abstract: Wireless Fidelity (WiFi) network is based on the IEEE 802.11 standard. WiFi units are used to provide a connection of local devices within homes or businesses. In this paper, OPNET Modeler is used to module and simulate a WiFi networks in fixed local area networks to estimate their performance based on End to End Delay and WiFi voice-packet delay for both WiFi base line and WiFi base fiber. Simulation results indicate that base line has delay larger than base fiber.

Keywords:
Wireless LAN, Wi-Fi, End to End delay, OPNET.


References:

1. Zainab T. Alisa, “Evaluating the Performance of Wireless Network using OPNET Modeler”, International Journal of Computer Applications, Volume 62– No.13, January 2013.
2. P.Trimintzios1 and G. Georgiou, “WiFi and WiMAX Secure Deployments” Journal of Computer Systems, Networks, and Communications Volume 2010, Article ID
423281.

3. L.M.S.C. of the IEEE Computer Society, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Higher-Speed Physical ayer Extension in the 2.4 GHz Band,” ANSI/IEEE Standard 802.11-1999TM.

4. Kritika, N., Namarta. “Performance Evaluation of 802.11 WLAN Scenarios in OPNET Modeler” International Journal of Computer Applications, May 2011.

5. H. Zhu, M. Li, I. Chlamtac, B. Prabhakaran, “A survey of quality of service in IEEE 802.11 networks”, IEEE Wireless Communications, 2004.

6. Garima Malik *, Ajit Singh, “Performance Evaluation of WiFi and WiMax Using Opnet “, International Journal of Advanced Research in Computer Science and Software, Volume 3, Issue 6, June 2013.

7. L. Das Dhomeja, Shazia Abbasi, Asad Ali Shaikh1, Y. A. Malkani, “PERFORMANCE ANALYSIS OF WLAN STANDARDS FOR VIDEO CONFERENCING APPLICATIONS”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 3, No. 6, December 2011

8. S. Banerji, R. Singha Chowdhury, “On IEEE 802.11: Wireless LAN Technology “International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol. 3, Issue. 4, 2013.

9. Eldad Perahia, Michelle X. Gong, “Gigabit Wireless LANs: an overview of IEEE 802.11ac and 802.11ad “, Intel Corporation

10. OPNET Technologies, OPNET WORK 2007 proceedings (online). Available: http://www.opnet.com/opnetwork2007.

 

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14.

Authors:

Aktham Hasan Ali

Paper Title:

Design and Performance of Code Division Multiple Access Physical Layer Transceivers in Flat and Selective Fading Channels

Abstract: Code Division Multiple Access (CDMA) is the technology used in all third generation cellular communications networks, and it is a promising candidate for the definition of fourth generation standards. The wireless mobile channel is typically frequency-selective causing interference among the users in one CDMA cell. In this work, CDMA Transceivers block has been studied widely, and an analysis of proposed model based on Orthogonal frequency-division multiplexing OFDM based Fourier transform on in Flat and Selective Fading Channels

Keywords:
CDMA, OFDM, IFFT, FFT, Flat Fading, Selective Fading, Channels .


References:

1. D. V. Ageev, “Bases of the Theory of Linear Selection. Code Demultiplexing,” Proceedings of the Leningrad Experimental Institute of Communication:, pp. 3–35, 1935.
2. P. C. Lajos L. Hanzo, Jurgen Streit,, “Wireless Video communcation ” ISBN: 978-0-7803-6032-7 1092 pages February 2001, Wiley-IEEE Press, 2001.

3. L. K. Haohong Wang, Ajay Luthra, Song Ci,, 4G Wireless Video Communications, 2009.

4. Hui Liu and Huiun Yin, “Receiver Design in Multi -carrier Direct Sequence CDMA Communications ” IEEE Trans. on Comm, vol. 49, 2001.

5. V. Khairnar, Bhopal, M. P., India ; Mathur, Jitendra ; Singh, Hema,, “Design and performance analysis of DS-CDMA rake receivier for wireless communication,” Electronics and Communication Systems (ICECS), 2014 International Conference on, 2014.

6. K.Wirisal. (2002). OFDM Air Interface Design for Multimedia Communication.

7. S. H. a. S. Prasad, “Overview of Multi-carrier CDMA ” IEEE Communication Magazine vol. 35, pp. 126-133, 1997.

8. A.Persson et al, “A Unified Analysis for Direct-Sequence CDMA in the Down Link of Systems ” IEEE Transactions on Communication \, 2003.

9. e. a. Sadayuki Abeta, “Performance of Coherent Multi-carrier /DS-CDMA and MC-CDMA for broadband Packet Wireless Access,” IEICE, Trans. on Comm, , vol. E84-B, 2001.

10. e. a. M. J. Juntti, “Genetic algoriths for multi-user detectioin in syncronous CDMA,” IEEE Int .Sym on information theory ISIT97, p. 492, 1997.

11. K. Y. a. L.Hanzo, “Hybrid Genetic Algorith based Multi-user Detection Schemes for Synchronous CDMA systems ” pp. 1400-1404, 2000.

12. Hany Farid and Eero P. Simoncelli, “Differentiation of Discrete Multidimensional Signals,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 4, APRIL 2004, vol. 13, 2004.

13. X. W. a. W. Yang, “Performance of Space-Time Block-Coded Multicarrier DS-CDMA System in aMultipath Fading Channel,” internatioal Symposium on Mcrowave Anteena propagation and EMC Technology for Wireless Communication Proceedings, pp. 1551-1555, 2005.

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15.

Authors:

Fatima Faydhe AL-Azzawi, Saleim Hachem Farhan, Maher Ibraheem Gamaj

Paper Title:

M-FSK in Multi Coding and Channel Environments

Abstract: Frequency-shift keying (FSK) is a frequency modulation scheme in which digital information is transmitted through discrete frequency changes of a carrier wave currently used by manufacturers of low power low data rate data transmission equipment. The power efficiency of this modulation increases as the signal alphabet increases at the expense of increased complexity and reduced bandwidth efficiency. Most early telephone-line modems used frequency-shift keying (FSK) to send and receive data at rates up to about 1200 bits per second. In this paper M-FSK have been tested under multi-channel environments AWGN, Rayliegh fading and Risian fading channels in term of BER with coherent and non- coherent demodulation and deferent Size of modulation constellation, Improving techniques used to enhanced the performance of the system under AWGN where convolutional code with hard and soft decision, extended Golay code and Reed-Solomon code, the ratio of energy in the specular component to the energy in the diffuse component (linear scale) and diversity used to improve the performance under Rayliegh and Risian fading channels.

Keywords:
M-FSK, FSK with matlab, M-FSK coding, multi-channel.


References:

1. Proakis, J. G., Digital Communications, 5th Ed., McGraw-Hill, 2008.
2. Simon, M. K., and Alouini, M. S., Digital Communication over Fading Channels –A Unified Approach to Performance Analysis, 1st Ed., Wiley,2000.

3. Simon, M. K ,Hinedi, S. M., and Lindsey, W. C., Digital CommunicationTechniques – Signal Design and Detection, Prentice-Hall, 1995.

4. Odenwalder, J. P., Error Control Coding Handbook (Final report), Linkabit Corp., 15 July 1976.

5. Sklar, B., Digital Communications, 2nd Ed., Prentice-Hall, 2001.

6. Gulliver, T. A., “Matching Q-ary Reed-Solomon codes with M -ary modulation,” IEEE Trans. Commun., vol. 45, no. 11, Nov. 1997, pp. 1349-1353.

7. Dimpal joshi, Kapil gupta,” SEP performance of MFSK in Rician fading channel based on MGF method”, IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 897-899.

8. HONGFEI WANG ; MEMORIAL UNIV., ST. JOHN”S, NL, CANADA ; DOBRE, O.A. ; CHENG LI ; INKOL, R.,”M-FSK SIGNAL RECOGNITION IN FADING CHANNELS FOR COGNITIVE RADIO”,IEEE, RADIO AND WIRELESS SYMPOSIUM (RWS), 2012.

 

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Authors:

Kanwaljeet Singh, Avtar Singh Buttar

Paper Title:

Study of Spectrum Sensing Techniques in Cognitive Radio: A Survey

Abstract: the wireless traffic is increasing in an unparalleled way, which causes radio spectrum shortage. The fixed spectrum assignment policy makes this problem more critical. Cognitive radio is one answer to spectrum scarcity problem. In Cognitive radio, the licensed bands are opportunistically accessed when primary user is absent. The first step to a cognitive radio network is the spectrum sensing. An efficient and fast spectrum sensing can make cognitive radio more useful practically. In this paper we discuss several spectrum sensing techniques used in cognitive radio. The vacant frequency spectrum is first sensed by the cognitive radio users, for this purpose several spectrum sensing techniques are used. Spectrum sensing is one of the features of cognitive radio which tells us the availability of vacant bands (also called spectrum holes). In this survey, we analyze the non-cooperative, cooperative and interference based spectrum sensing techniques in cognitive radio. Also in the last, an introduction of some miscellaneous techniques has been given.

Keywords:
Cognitive Radio (CR), spectrum sensing, Primary User (PU), fusion center, multi-taper spectrum estimation, Power Spectral Density (PSD).


References:

1. J. mitola III, ”Cognitive radio: an integrated agent architecture for software defined radio,” Ph.D Thesis, KTH Royal Institute of Technology, Sweden, 2000.
2. FCC, ET Docket No 02-135 Spectrum Policy Task Force (SPTF) Report, Nov. 2002.

3. Ian F. Akyildiz, Brandon F. Lo, Ravikumar (2011), ”Cooperative Spectrum Sensing in Cognitive Radio networks: A Survey, Physical Communication”, pp:40-62.

4. Shahzad A. et. Al. (2010), “Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems,” Australian
Journal of Basic and Applied Sciences.

5. Zi- Long Jiang, “Wavelet Packet Entropy Based Spectrum Sensing in Cognitive Radio” IEEE international conference Xi’an, 2011.

6. Ekram Hossain, Dusit Niyato, Zhu Han (2009), “Dynamic Spectrum Access and Management in Cognitive Radio Networks”, Cambridge University Press.

7. Z. Tian and G. B. Giannakis, “Compressed sensing for wideband cognitive radios”.

8. Jun Ma, Y.L. Geoffrey,B.H. Juang, 2009, “Signal Processing in Cognitive Radio”, Proceeding of the IEEE, vol.97, No.5

9. B. Farhang-Boroujeny, “Filter Banks Spectrum Sensing for Cognitive Radios”, IEEE Transaction on Signal Processing, Vol. 56, pp. 1801-1811, May 2008.

10. Qing Zhao, Brian M. Sadler,” A Survey of Dynamic Spectrum Access”, IEEE Signal Processing magazine, May, 2007, pp.79-89.

11. C. Clancy, -Formalising the interference temperature model, J. Wireless Communication Mobile Computing, 2007.

 

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17.

Authors:

Milad Ghanbari, Abozar Godarzi Mehr, Hamid Nehzat

Paper Title:

Introducing an Intelligent Transportation System Decision Support Model for the Highways in Iran Based on Fuzzy Logic

Abstract: The significance of inner and inter-city highways in terms of security, environmental pollution, and the capacity and density of the lanes has led to implementation of intelligent transportation infrastructure. The use of Intelligent Transportation Systems (ITS) economizes on costs and time. ITS enjoying high technology in information processing, communications, electronic controll establish a proper and safe relationship between man, vehicles, and roads. This paper aimed to introduce a Decision Support System (DSS) in order to select the kind of intelligent transportation system for the highways in Iran. The research taking advantage of the ideas of some experts in the field of traffic and transportation performed fuzzy logic (FL) model in MATLAB software. The validity of the model was studied and confirmed in a case study of two highways.

Keywords:
fuzzy logic, traffic engineering, intelligent transportation system, highway capacity, decision support system.


References:

1. Young, R. (2008). Transportation Infrastructure: An Overview of Highway Systems and South Carolina’s Position and Status. Institute for Public Service and Policy Research, University of South Carolina.
2. Van der Kroon, P., Camolino, R., & Jandrisits, M. (2009). The E-Safety Forum Intelligent Infrastructure Working Group–Identifying the Expectations Towards the Road Infrastructure Side of Cooperative Systems. In 16th ITS World Congress and Exhibition on Intelligent Transport Systems and Services.

3. Dabahde, V. V., & Kshirsagar, R. V. FPGA-Based Intelligent Traffic Light Controller System Design.

4. Farzaneh, K. (2009). A Comprehensive Survey to Identify System Concepts & ICT Requirements of IRAN Intelligent Transportation System (IRAN ITS).

5. Koonce.P.,.Bertini.R.L,.Monsere.C.M (2005) . benefits of intelligent transportation system technologies in urban area: a literature review. Portland state university.

6. Martin.A, Marini.H, Tosunoglu.S (2004) .Intelligent Highway/ Vehicle System : survey. Miami, Florida international univ. 33199.

7. Yoon, S. W., Velasquez, J. D., Partridge, B. K., & Nof, S. Y. (2008). Transportation security decision support system for emergency response: A training prototype. Decision Support Systems, 46(1), 139-148.

8. Fierbinteanu, C. (1999). A decision support systems generator for transportation demand forecasting implemented by constraint logic programming. Decision Support Systems, 26(3), 179-194.‏

9. Sprenger, R., & Mönch, L. (2014). A decision support system for cooperative transportation planning: Design, implementation, and performance assessment.Expert Systems with Applications, 41(11), 5125-5138.‏‏

10. Ülengin, F., Önsel, Ş., Ilker Topçu, Y., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.‏

11. Nwagboso, C., Georgakis, P., & Dyke, D. (2004). Time compression design with decision support for intelligent transport systems deployment. Computers in Industry, 54(3), 291-306.‏

12. Crainic, T. G., Gendreau, M., & Potvin, J. Y. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies, 17(6), 541-557.

13. Ying, X., Mengxin, L., & YangXue, Z. J. Traffic Flow Forecasting Algorithm based on Spatio-temporal Relationship.

14. Borne, P., Fayech, B., Hammadi, S., & Maouche, S. (2003). Decision support system for urban transportation networks. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 33(1), 67-77.

15. Vannieuwenhuyse, B., Gelders, L., & Pintelon, L. (2003). An online decision support system for transportation mode choice. Logistics Information Management, 16(2), 125-133.

16. Ülengin, F., Önsel, Ş., Topçu, Y. I., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.

17. Arampatzis, G., Kiranoudis, C. T., Scaloubacas, P., & Assimacopoulos, D. (2004). A GIS-based decision support system for planning urban transportation policies. European Journal of Operational Research, 152(2), 465-475.

18. Painho, M., Oliveira, T., & Henriques, R. (2011). A Decision Support System for the Public Transportation Sector: The SIGGESC Project. Proceedings of the 11st CAPSI, October 19th–21st Lisbon, Portugal.

19. Alipour, B. (2011). Intelligent transportation systems: Past, present and look to future by using Grid technology. In 5th Symposium on Advances in Science and Technology May.

20. Hegyi, A., De Schutter, B., Hoogendoorn, S., Babuska, R., van Zuylen, H., & Schuurman, H. (2001). A fuzzy decision support system for traffic control centers. In Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE(pp. 358-363). IEEE.

21. Almejalli, K., Dahal, K., & Hossain, M. A. (2007). Intelligent traffic control decision support system. In Applications of Evolutionary Computing (pp. 688-701). Springer Berlin Heidelberg.

22. Dos Santos Soares, M., Vrancken, J., & Wang, Y. (2010, April). Architecture-based development of road traffic management systems. In Networking, Sensing and Control (ICNSC), 2010 International Conference on (pp. 26-31). IEEE.

23. Liu, H., Zhang, K., Wang, X., Qi, T., & Wang, C. (2005). Effective and sustainable development of chinese national intelligent transportation system architecture. Transportation Research Record: Journal of the Transportation Research Board, (1910), 46-56.

24. Mihyeon Jeon, C., & Amekudzi, A. (2005). Addressing sustainability in transportation systems: definitions, indicators, and metrics. Journal of infrastructure systems, 11(1), 31-50.

 

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18.

Authors:

Uwa C. U, Nwafor J. C

Paper Title:

Relevance of Science and Technology on Environmental Commons: The Nigerian Experience

Abstract: A journey through Nigeria, either by road, air, or rail shows a scintillating environment fully endowed with abundant of resources, from a rich ecosystem through rich mangrove and rain forests to plateau, mountain vegetation interspersed with rivers, lakes in different climatic regions and in different stages of utilization and management. All these influence man’s existence but the extent of their influence on him depends on his capabilities to transform the applicable environment. Man applies science and technology in his quest to satisfy his day to day needs. Man will necessarily succumb to the dictates of environmental fallouts, if man is ill-equipped. With the necessary skills and knowledge and right application of the tools. It is on this premise that this study examines man and his physical environment, his application of science and technology to transform this environment to meet his immediate needs, the impact on it’s environment and it’s influence on man setting useful environmental laws and strategies for the way forward are then discussed. This study also deals with the laws, which helps to manage the environment for better wage and improve the living condition of man and nature via the waste management methods.

Keywords:
scintillating environment fully endowed utilization and management, laws and strategies.


References:

1. Agwu, E. I. C; Ezedike, C. E., and Egbu, A. U. Eds (2000). The Concept and Procedure of Environmental Impact Assessment (EIA), Crystal Publishers, Okigwe, Nigeria.
2. Baba, J. M. (2004): Sustainable Development and the Nigerian Environment. The Nigerian Geographical Journal, New Series Vol. 1:1-4.

3. Castells, M. (2000): European Cities. The Information and Society and the Global Economy. Oxford, Blackwell.

4. Castells, M. (2004): The Information Age. Economy, Society and Culture. Oxford Blackwell.

5. Ezenwa, E. (1993): Man and His Environment: An Introduction, Enugu: New Generation Books.

6. Fajuyigbe, M. O. (2007): Art and Aesthetics of the Built and Natural Environment. An Appraisal of Obafemi Awolowo University Community Proceeding on towards a Sustainable Built and Natural Environment Awolowo – University Ile-Ife, 223-224.

7. FEPA (1998): Guidelines and Standards for Environmental Pollution Control in Nigeria.

8. Folorunsho, D. O. (2005): The Challenges of Environment, Energy and Safety to Manufacturing. African Journal of Arts and Ideas 4:207-212.

9. Harvey, S. A. (2005): Reformation of Geographical Landscape. Journal of Environmental Sciences Vol. 10:4-12.

10. Mabogunje, A. I. (1978): Towards an Urban Policy in Urbanization Process and Problems in Nigeria. Soda P. S. et al. (eds) Ibadan University Press 7-20pp.

11. NEST (2004): Nigeria’s Threatened Environment: A National Profit Ibadan: NEST.

12. Oruwari, T. (2000): Open Space Design and Health Implications in Nigeria in A. O. Bayo et al. (eds) Effective Housing in the 21st Century Environ. Forum, Federal University of Technology, Akure, 89 – 95pp.

13. The Nigerian Environment: A Quarterly Newsletter of the Federal Environmental Protection Agency (FEPA), Vol. 8 No. 1 & 2.

14. Udoessien, E. I. (2003): Basic Principles of Environmental Science. Etiliew International Publishers, Uyo, 25 – 66pp.

15. Udoh, F. D., Mahadev, S. and Varma, S. M. (2002): Developing an Emission Inventory for Akwa-Ibom State, Nigeria Air Quality proposal prepared by Fadeux International to the Ministry of Environment Uyo, Akwa-Ibom State, Nigeria.

16. United Nations Environment Programme (1972): Geo, (2000) New York.

 

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19.

Authors:

Uwa Clementina Ukamaka, Nwafor J. C

Paper Title:

Climate Change Effects on Environmental Flora in the Nigerian Terrain: Health Implications on Mankind

Abstract: Environmental conditions play a key role in defining the function and distribution of flora, in combination with other factors. Changes in long term environmental conditions that can be collectively coined climate change are known to have had enormous impacts on flora diversity pattern in the past and are seen as having significant current impacts. Researchers predict that climate change will remain one of the biodiversity patterns in the future. Adopting the survey method of research, this study investigates the importance of Juglans regia (walnuts) commonly known as walnut, in the areas of food and medicine in Nigeria. Some factors that are responsible for biodiversity depletion in environmental flora forms a major focus of this work. The concept of ecosystem or biosphere as a circle of life receives highlight. This work also details the purposes, significance, educational implication as well as policy implication of the concept of biodiversity loss.

Keywords:
Climate change, environmental flora, biodiversity, ecosystem and Juglans regia


References:

1. Barrat, S. C. H. and Joshna, R. K (1991): Genetic and Conservation or Rare Plants. New York; Oxford University Press.
2. Biodiversity Monitoring and Assessment Project. htpp://www.envroliteracy.org/ category.Php/3.htm (assessed 25th June 2012).

3. Carthew, S.M (1993): Patterns of Flowering and Fruit Production in a Natural Production of Banksia Spinulosa. Australia Journal of Botany 41: 468-480.

4. Daniel, B.B and Edward, A.K. (1998): Environment Science Earth as a Living Plant. New York: John Willey and Sons: 102-104Pp.

5. Davidson, O. Halsnaes, K. Hug, S, Kok, M., Metz, B. S. Okona, Y. and J. Verhagen (2003): The Development and Climate Nexus: the Case of Sub-saharal Africa, Climate Policy, 351, 597-5113.

6. “Ecosystems”. Think Quest http://library.thinkquest.org/11353//Ecosystems. hta (Accessed 25th July 2012).

7. Environmental Science, Earth as a Living Planet. New York John Willey and Sons 102 – 104pp.

8. IPCC (2001): Climate Change 2001: Impacts Adaptation and Vulnerability: IPCC Working Group II Third Assessment Report. Mc Carthy, J. J., O. J. Canizian, N. A. Leary, D. J. Pokten, and K. S. White (Eds) Cambridge University Press.

9. Lynch, E. and Lande, I. (1993): Effects of Climate Change on Biodiversity. New York, Oxford University Press, 2pp.

10. Margaret, B. (1982): Major Requirements Environmental Education Journal on Environmental Conservation No. 2 (9): 136.

11. Midegley, G. F. L. Hainah, D. Millar, W. Tiller, Aid A. Booth, (2003): “Developing Regional Aid Species – Level Assessment of Climate Change Impacts on Biodiversity in the Cape Floristic Region”. Biological Conservation 112 91 – 2); 87 – 97.

12. Okoro, O. (1986): Biology aspects of Seed Production by Pinus caribaea Morelet variety Handurensis Barret and Golfari in Nigeria. A Ph.D Thesis of the Department of Forest Resources Management, University of Ibadan, Ibadan.

13. Oni, O. (1989): Fruit Abortion in a West African Hardwood, Terminalia Ivorensis Journal of Tropical Forest Science 2(4): 280 – 285.

14. Petanidou, T. Ellis, W. N.; Margaris, N. S. and Vokou, D. (1997): Constraints on Flowering Phenology in Phyganic (East Mediterranean Shrub) Community. American Journal of Botany 82 (5): 607 -620.

15. Peter, J. B. (2002): Biodiversity and Conservation. www.workbank.org/htm/ schools/glossarythm; 44-48pp.

16. Pickering, C. M. (1995): Variation in Flowering Parameters within and among Five Species of Australian Ranunculus. Aust, J. Bot 43: 103 – 112.

17. Reid, H., Pisupati, B. and H. Baulch (2004): How Biodiversity and Climate Change Interact Scider. Net. Biodiversity Dossier Policy Brief.

18. Rosakar, R. (2003): Phonological Pattern of Terrestrial Plants. Ann Rev. Ecol. Syst. 16: 1769 – 214.

19. Thomas, C. D.; Jones, B. A. and Adams, E. C. (2004): Extinction Risk from Climate Change. Nature Vol. 427: 145 – 148.

 

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20.

Authors:

Abdullah M. Alnajim

Paper Title:

An Automated Analyzer for Users’ Anti-Phishing Behaviour within a LAN

Abstract: Phishing is a security attack that seeks to trick people into revealing sensitive information about themselves and their Internet accounts. This paper proposes a novel anti-phishing approach that is deployed within a Local Area Network (LAN). The approach is a model that automatically perform ongoing analysis for users behaviours against phishing attacks and then based on the results it decides whether to train them or not against phishing. The aim is to enhance the phishing countermeasures applied on a LAN by making users aware of phishing attacks. A prototype proof of concept implementation is presented in this paper in order to test the approach’s applicability. The prototype of the new model shows that the approach model runs and performs the concept.

Keywords:
Modeling, Analyzer, Blacklists, LAN, e-Commerce Security, Network, Proxy, Online Banking Security, Phishing, Pharming.


References:

1. The National Consumers League Projects (2015). Phishing. Available: http://www.fraud.org/scams/internet-fraud/phishing, last access on 15/5/2015.
2. G. K. Tak, N. Badge, P. Manwatkar, A. Ranganathan, S. Tapaswi, “Asynchronous Anti Phishing Image Captcha approach towards Phishing”. Proc. the 2nd International Future Computer and Communication (ICFCC), Wuhan, IEEE Press, pp. V3-694 – V3-698.

3. Alnajim, and M. Munro “An Approach to the Implementation of the Anti-Phishing Tool for Phishing Websites Detection”. Proc. International Conference on Intelligent Networking and Collaborative Systems (INCoS 2009). Barcelona, Spain, IEEE Press, 2009, pp. 105 – 112.

4. J. S. Downs, M. B. Holbrook and L. F. Cranor, “Decision strategies and susceptibility to phishing”. Proc. the 2nd symposium on usable privacy and security. New York, USA, ACM Press, 2006, pp. 79 – 90.

5. Anti-Phishing Working Group APWG. (2015). Phishing Activity Trends Report, 4th Quarter 2014. Available: http://docs.apwg.org/reports/apwg_trends_report_q4_2014.pdf, last access on 26 June 2015.

6. S. A. Robila and J. W. Ragucci, “Don’t be a Phish: Steps in User Education”. Proc. 11th annual SIGCSE conference on innovation and technology in computer science education. New York, ACM Press, 2006, pp. 237 – 241.

7. Symantec. (2004). Mitigating Online Fraud: Customer Confidence, Brand Protection, and Loss Minimization. Available: http://www.antiphishing.org/sponsors_technical_papers/symantec_online_fraud.pdf, last access on 21/3/2007.

8. Alnajim and M. Munro, “Effects of Technical Abilities and Phishing Knowledge on Phishing Websites Detection”. Proc. the IASTED International Conference on Software Engineering (SE 2009), Innsbruck, Austria, ACTA Press, 2009, pp. 120-125.

9. Y. Zhang, J. I. Hong and L. F. Cranor, “Cantina: a content-based approach to detecting phishing web sites”. Proc. 16th international conference on WWW. New York, ACM Press, 2007, pp. 639 – 648.

10. G. Xiang, J. Hong, C. P. Rose, L. Cranor, “CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites”. ACM Transactions on Information and System Security (TISSEC), 2011 , Volume 14 Issue 2, New York, ACM Press, Article No. 21 .

11. H. Bo, W. Wei, W. Liming, G. Guanggang, X. Yali, L. Xiaodong, M. Wei, “A Hybrid System to Find&Fight Phishing Attacks Actively”. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Lyon, IEEE Computer Society, 2011, pp. 506-509

12. Alnajim and M. Munro, “An Evaluation of Users’ Tips Effectiveness for Phishing Websites Detection”. Proc. 3rd IEEE International Conference on Digital Information Management ICDIM, London, IEEE Press, 2008, pp. 63-68.

13. Microsoft Corporation. (2007). Microsoft Security for Home Computer Users Newsletter. Available: http://www.microsoft.com/protect/secnews/default.mspx, last access on 16 March 2007.

14. S. Sheng, B. Magnien, P. Kumaraguru, A. Acquisti, L. F. Cranor, J. Hong and E. Nunge, “Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phish”. Proc. 3rd symposium on usable privacy and security SOUPS. New York, ACM Press, 2007, pp. 88 – 99.

15. P. Kumaraguru, Y. Rhee, A. Acquisti, L. F. Cranor, J. Hong and E. Nunge,”Protecting people from phishing: the design and evaluation of an embedded training email system”. Proc. the SIGCHI conference on Human factors in computing systems. New York, USA, ACM Press, 2007, 905 – 914.

16. Alnajim and M. Munro, “An Anti-Phishing Approach that Uses Training Intervention for Phishing Websites Detection”. Proc. 6th IEEE International Conference on Information Technology – New Generations (ITNG). Las Vegas, IEEE Computer Society, 2009, pp. 405-410.

17. Alnajim, “High Level Anti-Phishing Countermeasure: A Case Study”. Proc. The The World Congress on Internet Security (WorldCIS-2011), London, UK, IEEE Press, 2011, pp. 139 – 144.

18. Alnajim, 2015. “A Country Based Model Towards Phishing Detection Enhancement”. International Journal of Innovative Technology and Exploring Engineering
(IJITEE), 2015, Volume 5 Issue 1, pp. 52 – 57.

19. W. Jia, and W. Zhou, “Distributed Network Systems: From Concepts to Implementations”. New York: Springer, 2004.

20. M. P. Singh, “the Practical Handbook of Internet Computing”. USA: Chapman & Hall/CRC Publisher, 2005.

21. Y. Xiao and H. Chen, “Mobile Telemedicine: A Computing and Networking Perspective”. USA: Auerbach Publications, 2008.

 

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21.

Authors:

Adhvik Shetty, Subham Chatterjee, Parimala R

Paper Title:

Predicting Behaviors of Stock Market

Abstract: Prices of stock depend on a variety of factors. Predicting and building a model is a daunting task to any analyst. To predict the behavior of stock market, one goes through the company news, economic and political news and global sentiments. Considering the large number of news articles, there are some which can be missed out. Also it is impossible to focus on each and every news article as soon as it is published on the internet. In this paper, we analyze the sentiment generated by news articles and correlate the sentiment with the actual change in stock market prices. This gives a deeper insight into the correlation and tells us how much news articles influence the stock market. After extensive research we have decided to use a hybrid technique involving machine learning and natural language processing concepts. We have used n–gram as the feature creation, chi square as the feature selection and support vector machines as the classification technique. Improving the accuracy of predicting stock market trends, we hope to aid investors in better decision making based on real time sentiment of news articles.

Keywords:
PRICES, CLASSIFICATION, ARTICLES, NEWS, POLITICAL


References:

1. An extensive empirical study of feature selection metrics for text classification by George Gorman, Journal of Machine Learning Research 3(2003)
2. Preprocessing the Informal Text for efficient Sentiment Analysis by I Hemalatha, Dr. GP Saradhi Verma, and Dr A Govardhan, Internal Journal of Emerging Trends and Technology in Computer Science

3. Xiaodong Li, Haoran Xie, Li Chen, Jianping Wang and Xiaotie Deng. “News impact on stock price return via sentiment analysis.” Knowledge-Based Systems(2014).

4. Gonçalves, Pollyanna, Matheus Araújo, Fabrício Benevenuto, and Meeyoung Cha. “Comparing and Combining Sentiment Analysis Methods.” ACM(2013).

5. Butler, M., Keselj, V. 2009. “Financial Forecasting using Character N-Gram Analysis and Readability Scores of Annual Reports”, Advances in AI

6. Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: a case study. In Proceeding of the intl. conference on recent advances in natural language processing. Borovets, BG.

7. S. Das and M. Chen. Yahoo! for anazon: Extracting market sentiment from stock message boards. In Proc. of the 8th APFA, 2001.

8. M. Thelwall. Heart and soul: Sentiment strength detection in the social web with sentistrength. http://sentistrength.wlv.ac.uk/documentation/ SentiStrengthChapter.pdf

9. B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In ACL Conference on Empirical Methods in Natural Language Processing, pages 79–86, 2002.

10. Antweiler, W., Frank, M.Z. 2004. “Is all that talk just noise? The information content of internet stock message boards”, The Journal of Finance, Volume 59, Number 3, June 2004, pp. 1259-1294

11. Gidofalvi, G. & Elkan, C. 2003. “Using News Articles to Predict Stock Price Movements. Technical Report”, Department of Computer Science and Engineering, University of California, San Diego

12. Joachims, T., 1998. “Text categorization with support vector machines: Learning with many relevant features”, Proceedings of the European Conference on Machine Learning, Springer-Verlag.

13. Schumaker, R.P., Chen, H. “Textual analysis of stock market prediction using breaking financial news: the AZFin Text System”, ACM Transactions on Information Systems 27 (2) (2009).

 

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22.

Authors:

Ghazy Al- Hamed

Paper Title:

Effect of Turnover on Jordanian Health Care Organizations

Abstract: Productivity is very important issue for any Health Care organization. There are several factors on which productivity of an organization mostly depends upon. Employee’s turnover is one of them which is considered to be one of the challenging issues in business nowadays. The impact of turnover has received considerable attention by senior management, human resources professionals and industrial psychologists. It has proven to be one of the most costly and seemingly intractable human resource challenges confronting by several organizations globally. The purpose of this research is therefore, to find out the actual reasons behind turnover and its damaging effects on the performance of different Jordanian Health Care Organizations.The objectives of the study is to ascertain the cause of Employees turnover , To determine the effect of employee turnover , To measure the satisfaction level of employees in the health organizations, and finally to build model to reduce turnover in health organizations. This study focused on the effect of employee turnover on Health Care Organizations with reference to the Jordan Health Care organizations (JHCOS). High employee turnover rates affect efforts to attain organizational objectives. In addition, when the Health Care Organizations loses a critical employee, the effects on innovation, consistency in providing service to patients and timely delivery of services to patients may be negatively affected. The research design used in this study was the quantitative approach, which allowed the researcher to use structured questionnaires in collecting data. The simple random sampling technique was used to select four hundred respondents from all levels of management in the Jordan Health Care organizations. The total number of population that the questionnaires were administered was four hundred (400), of which three hundred and seventy four (374) was retrieved shaped, (, .93% of total population. Analytical statistics was used to analyze and test hypothesis ,(SPSS) was used for that. The study found positive turnover and negative turnover effect the performance of Jordanian Health Care Organizations, The study show also that Gender and Age not affect the Health Care Organizations Turnover causes, but Educational background, Status of respondents, and Work experience have an effect on Health Care Organizations Turnover causes. The study illustrate that adopted mode suggested effect on reducing turnover in Jordanian Health Care Organizations , the Model include ( improve work environment ,build trust ,recognize good performance, develop of employee, adopt good benefits and incentives system) on reducing Health Care organization Turnover, Turnover, however, had dual effects on the health organization; positive and negative effects. Whiles employee turnover introduced new ideas and skill into the health organization, it’s also lead to difficulties in attracting new staff. To reduce the rate of turnover, management should be assure that the environmental condition of employees is convenient.

Keywords:
Whiles employee turnover introduced new ideas and skill into the health organization, it’s also lead to difficulties in attracting new staff. To reduce the rate of turnover, management should be assure


References:

1. Allen, D., 2000. Managing employee turnover: Myths to dispel and strategies for effective management. Journal of Management, 6(2): 122-123.
2. Armstrong, M., 2011. Handbook of human resource management practices. 12th Edn., New York: Prentice-Hall.

3. Durbin, H., 2000. Applying psychology: Individual and organizational. London, England: Prentice Hall.

4. Grobler, P.A., S. Warnich, M.R. Carrel, N.F. Elbert and R.D. Hatfield, 2006. Human resource management in South Africa. London: Thompson.

5. Hom, P.W. and R.W. Griffeth, 2001. Retaining valued employees. Ontario, Canada: South- Western Publishing.
6. Kreitner, R., 2003. Human resources management. Toronto: Arizona State University: Houghton Mifflin Company.

7. Kreitner, R. and A.J. Kinicki, 2007. Organizational behavior. 7th Edn., New York: Irwin McGraw Hill.

8. Meyer, J.P., 2001. Organizational commitment: Personnel psychology and HRM. London, England: Wiley.

9. Nugent, A., 2009. Using voluntary benefits strategically can help employer’s goals of retaining employees and controlling cost. Benefits Quarterly Reviews, 25(2): 7-100

10. Glisson, C., & Durick, M. (1988). Predictors of job satisfaction and organizational commitment in human service organizations. Administrative Science Quarterly, 33, 61-81.

11. Mor Barak, M. E.,Nissly, J. A., & Levin, A. (2001). Antecedents to retention and turnover among child welfare, social work, and other human service employees: What can we learn from past research? A review and meta analysis. Social Service Review, 75(3), 625-661.

12. Landsman, M. J. (2001). Commitment in public child welfare. Social Service Review, 75(3), 387-419.

13. Krueger, M. (1996). Job satisfaction for child and youth care workers (3rd ed.). Washington, DC: Child Welfare League of America Press.

14. Weiner, N. (1980). Determinants and behavioral consequences of pay satisfaction: A comparison of two models. Personnel Psychology, 33(4), 741-757.

15. Poulin, J. E. (1994). Job task and organizational predictors of social worker job satisfaction change: A panel study. Administration in Social Work, 18(1), 21-38.

16. Poulin, J. E., & Walter, C. (1992). Retention plans and job satisfaction of gerontological social workers. Journal of Gerontological Social Work, 19, 99-114.

17. Sze, W. C., & Ivker, B. (1986). Stress in social workers: The impact of setting and role. 17-The Journal of Contemporary Social Work, 67(3), 141-148.

18. Jayaratne, S., & Chess, W. A. (1991). Job satisfaction and burnout: Is there a difference? Journal of Applied Social Sciences, 15, 245-262.

19. Rycraft, J. R. (1994). The party isn’t over: The agency role in the retention of public child welfare caseworkers. Social Work, 39(1), 75-80.

20. Dressel, P. L. (1982). Policy sources of worker dissatisfactions: The case of human services in aging. Social Service Review, 56(3), 406-423.

21. Tracy, E. M., Bean, N., Gwatkin, S., & Hill, B. (1992). Family preservation workers: Sources of job satisfaction and job stress. Research on Social Work Practice, 2(4), 465-478.

22. McLean, J., & Andrew, T. (2000). Commitment, satisfaction, stress and control among social service managers and social workers in the UK. Administration in Social Work, 23(3/4), 93-117.

23. Glisson, C., & Hemmelgarn, A. (1998). The effects of organizational climate and interorganizational coordination on the quality and outcomes of children’s service systems. Child Abuse and Neglect, 22(5), 401-421.

24. Ellett, A. J. (2001).Organizational culture and intent to remain employed in child welfare: A two-state study. Dallas, TX: Council on Social Work Education Annual Program Meeting.

25. Poulin, J. E., & Walter, C. (1992). Retention plans and job satisfaction of gerontological social workers. Journal of Gerontological Social Work, 19, 99-114.

26. Boswell, W, E., Bourdrean, J.W., & Dunford, B.B ( 2004 ). The outcomes and correlates of job search objectives.

27. Burton. M.D., & Beckman, C.M. ( 2007 ) leaving legacy : Position imprints and successor turnover in young firms. American sociological Review, 72, 239 266.

28. Cotton , J., & Tuttle , J.M ( 1986 ). Employee Turnover: A Meta analysis and Review with implications for Research, Academy of Management Review, 11, 55 – 70. 29- Employee Turnover in Organization by Lisa Magloff, Demand Media.

29. Williams, L.J., Hazer, J.T. (1986), “Antecedents and consequences of satisfaction and commitment in turnover models: a re-analysis using latent variable structural equation methods”, Journal of Applied Psychology, Vol. 72 No.1, pp.219-31.

30. Price, J. & Muller, C. (1981). A casual model of turnover of nurses. Academy of Management Journal, 24(3), 543-565.

 

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23.

Authors:

Battu Deepa, M. Hemalatha

Paper Title:

Solar Energy Tracking System Using At89s52 Microcontroller and L293d Motor Driver Circuit


Abstract:
With the increasing demand of energy and the diminution of the fossil fuels, with increase in pollution level and depletion of the ozone layer the demand for the natural and renewable sources of energy is the need of the hour and this has been the point of discussion all over the world with many organizations working for the utilization of these resources and its promotion and United Nations allocating huge amount of funds for its promotion we have also made an effort to contribute a bit in the same direction. It is a known fact that the most unutilized source of energy is solar energy. This paper deals with a microcontroller based solar panel tracking system. Solar tracking enables more energy to be generated because the solar panel is always able to maintain a perpendicular profile to the sun’s rays. Development of solar panel tracking systems has been ongoing for several years now. As the sun moves across the sky during the day, it is advantageous to have the solar panels track the location of the sun, such that the panels are always perpendicular to the solar energy radiated by the sun A solar energy tracker is a device used for orienting a solar photovoltaic panel or lens towards the sun. Hence the sun tracking system can collect more energy.


Keywords:
Solar system, solar panel, microcontroller AT89S52, LCD HITACHI 44780, L293D MOTOR DRIVER CIRCUIT.


References:

1. B.Suchitha Samuel, J.Mrudula, ―Design of Intelligent Solar Tracker Robot for Surveillance,‖ International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 10, October 2013.
2. Bhavesh Pandey1, Anita Agrawal, ―Automatic Sun Tracking System Using PSoC,‖ International Journal of Innovative Research in Science, Engineering and Technology, Vol. 1, Issue 1, November 2012.

3. NANOCO group PLC, http://www.nanocotechnologies.com/content/CommercialApplicatio ns/Solar.aspx

4. J. Rizk, and Y. Chaiko, “Solar Tracking System: More Efficient Use of Solar Panels”, World Academy of Science, Engineering and Technology, 41, 2008.

5. Solar Power http://www.solar-power-answers.co.uk/basics.php

6. SolarGreenhttp://www.solargreen.net.au/solarcell.php

7. MacsLabhttp://www.macslab.com/optsolar.html

8. International Energy Agency,World energy outlook 2012, 2012.

9. R. Eke and A. Senturk, “Performance Comparison of a Double-axis Sun Tracking Versus Fixed PV System,” Solar Energy, Vol. 86, No. 9, 2012,pp.2665-2672. doi:10.1016/j.solener.2012.06.006

10. P. Roth, A. Georgiev and H. Boudinov, “Design and Construction of a System for Sun-tracking,” Renewable Energy, 2004, Vol. 29, No. 3, pp. 393-402. doi:10.1016/S0960-1481(03)00196-4

11. F. R. Rubio, M. G. Otega, F. Gordillo and M. Lopez-Martinez, “Application of New Control Strategy for Sun Tracking,” Energy Conversion and Management, 2007, Vol. 48, No. 7, pp. 2174-2184. doi:10.1016/j.enconman.2006.12.020

12. C. X. Du, P. Wang and C. F. Ma, “A High Accuracy Algorithm for the Calculation of Solar Position,” Energy engineering,Vol.2,2010,pp.40-48. http://www.cnki.net/KCMS/detail/detail.aspx

13. J. A. Beltran, J. L. S. Gonzalez Rubio, C.D. Garcia-Beltran: Design, Manufacturing and Performance Test of a Solar Tracker Made by an Embedded Control, CERMA 2007, Mexico

14. O. Stalter, B. Burger, S. Bacha, D. Roye: Integrated Solar Tracker Positioning Unit in Distributed Grid-Feeding Inverters for CPV Power Plants, ICIT 2009, Australia

15. M. A. Panait, T. Tudorache: A Simple Neural Network Solar Tracker for Optimizing Conversion Efficiency in Off-Grid Solar Generators, ICREPQ 2008, Spain

16. M. Morega, J. C. Ordonez, P. A. Negoias, R. Hovsapian: Spherical Photovoltaic Cells – A Constructal Approach to Their Optimization, OPTIM 2006, Romania

17. M. Morega, A. Bejan: A Constructal Approach to the Optimal Design of Photovoltaic Cells, Int. Journal of Green Energy, pp. 233-242, 2005

18. J. Horzel, K. De Clerq: Advantages of a New Metallization Structure for the Front Side of Solar Cells, 13th EC Photovoltaic Solar Energy Conference, France, 1995

 

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Volume-5 Issue-4

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Volume-5 Issue-4, September 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Amira A. El Tayeb, Vikas Pareek, Abdelaziz Araar

Paper Title:

Applying Association Rules Mining Algorithms for Traffic Accidents in Dubai

Abstract: Association rule mining algorithms are widely used to find all rules in the database satisfying some minimum support and minimum confidence constraints. In order to decrease the number of generated rules, the adaptation of the association rule mining algorithm to mine only a particular subset of association rules where the classification class attribute is assigned to the right-hand-side was investigated in past research. In this research, a dataset about traffic accidents was collected from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori association rules algorithms were applied to the dataset in order to explore the link between recorded accidents’ factors to accident severity in Dubai. Two sets of class association rules were generated using the two algorithms and summarized to get the most interesting rules using technical measures. Empirical results showed that the class association rules generated by Apriori algorithm were more effective than those generated by Predictive Apriori algorithm. More associations between accident factors and accident severity level were explored when applying Apriori algorithm.

Keywords:
Association Rule Mining, Apriori, Predictive Apriori, Dubai Traffic Accidents


References:

1. Adeyemi Adejuwon, Amir Mosavi, “Domain Driven Data Mining- Application to Business”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No.2, July 2010, pp. 41-44.
2. Qiankun Zhao, Sourav S. Bhowmick, “Association Rule Mining: A Survey”, Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003116, 2003.

3. Bing Liu, Wynne Hsu, Yiming Ma, “Integrating Classification and Association Rule Mining”, KDD-98, New York, Aug 27-31, 1998.

4. Paresh Tanna, Dr. Yogesh Ghodasara, “Using Apriori with WEKA for Frequent Pattern Mining”, International Journal of Engineering Trends and Technology (IJETT),
Volume 12 Number 3, Jun 2014, pp. 127-131.

5. Divya Bansal, Lekha Bhambhu, “Execution of APRIORI Algorithm of Data Mining Directed Towards Tumultuous Crimes Concerning Women”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 9, September 2013, pp. 54-62.

6. Zhou Aoying, Wei Li, YU Fang, “Effective Discovery of Exception Class Association Rules”, Journal of Computer Science and Technology, Volume 17, Issue 3, May 2002,
pp. 304-313.

7. Saddys Segrera, Maria N. Moreno, “Classification Based on Association Rules for Adaptive Web Systems”, Advances in Soft Computing, Volume 44, 2007, pp. 446-453.

8. David Halbert (October 20, 2008), “The World’s Worst Drivers – Car Accident Statistics From Around the World”, [Online]. Available at URL http://www.articlesbase.com/cars-articles/the-worlds-worst-drivers-car-accident-statistics-from-around-the-world-609862.html.

9. Ronald V Miller ( September 8, 2010), “Car Accident Statistics from the CDC”, [online]. Available at URL: http://www.marylandinjurylawyerblog.com/2010/09/car_accident_statistics_from_t.html.

10. Bener, D. Crundall, “Road traffic accidents in the United Arab Emirates compared to Western countries”, Advances in Transportation Studies an international Journal Section A6, 2005, pp. 5-12.

11. Abdelaziz Araar, Amira A. El Tayeb,“Mining Road Traffic Accident Data to Improve Safety in Dubai”, Journal of Theoretical and Applied Information Technology, Vol. 47 No.3, 31st January 2013, pp. 911-925.

12. Ossenbruggen, P. J., J. Pendharkar, et al. “Roadway safety in rural and small urbanized areas.” Accidents Analysis and Prevention 33(4): 485-498, 2001.

13. Sohn, S. and S. Hyungwon. “Pattern recognition for a road traffic accident severity in Korea”, Ergonomics 44(1): 101-117, 2001.

14. Sohn, S. and S. Lee, “Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea.” Safety Science 41(1): 1-14, 2002.

15. Ng, K. S., W. T. Hung, et al., “An algorithm for assessing the risk of traffic accidents.” Journal of Safety Research 33: 387-410, 2002.

16. Bedard, M., Guyatt, G. H., Stones, M. J., & Hireds, J. P., “The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities, Accident analysis and Prevention, Vol. 34, pp. 717-727, 2002.

17. Miao M. Chong, Ajith Abraham, Marcin Paprzycki, “Traffic Accident Analysis Using Decision Trees and Neural Networks”, IADIS International Conference on Applied Computing, Portugal, IADIS Press, Pedro Isaias et al. (Eds.), Volume 2, pp. 39-42 2004.

18. Chang, L. and W. Chen “Data mining of treebased models to analyze freeway accident frequency”, Journal of Safety Research 36: 365- 375, 2005.

19. T. Beshah, “Application of data mining technology to support RTA severity analysis at Addis Ababa traffic office”, Addis Ababa, Addis Ababa University, 2005.

20. Chang, L. and H. Wang, “Analysis of traffic injury severity: An application of non-parametric classification tree techniques Accident analysis and prevention”, Accident analysis and prevention 38(5): 1019-1027, 2006.

21. Srisuriyachai, S., “Analysis of road traffic accidents in Nakhon Pathom province of Bangkok using data mining”, Graduate Studies, Bangkok, Mahidol University, 2007.

22. Wong, J. and Y. Chung,”Comparison of Methodology Approach to Identify Causal Factors of Accident Severity.” Transportation Research Record 2083: 190-198, 2008.

23. Zelalem, R., “Determining the degree of driver’s responsibility for car accident: the case of Addis Ababa traffic office”, Addis Ababa, Addis Ababa University, 2009.
24. Getnet, M., “ Applying data mining with decision tree and rule induction techniques to identify determinant factors of drivers and vehicles in support of reducing and controlling road traffic accidents: the case of Addis Ababa city, ”Addis Ababa, Addis Ababa University, 2009.
25. Sami Ayramo, Pasi Pirtala, Janne Kauttonen, Kashif Naveed, Tommi Karkkainen, “Mining road traffic accidents”, University of Jyvaskyla, Finland, 2009.

26. T. Beshah and S. Hill, “Mining Road Traffic Accident Data to Improve Safety: Role of Road-related Factors on Accident Severity in Ethiopia”, Proceedings of AAAI Artificial Intelligence for Development (AI-D’10), 2010.

27. Galvão ND, de Fátima Marin H, “Traffic accident in Cuiabá-MT: an analysis through the data mining technology”, Federal University of Mato Grosso-UFMT, Brazil, 2010.

28. Amirhossein Ehsaei, Harry Evdorides,“Temporal Variation of Road Accident Data caused by Road Infrastructure”, 3rd International Conference of Road Safety and Simulation, September 14-16, Indianapolis, USA, 2011.

29. S.Krishnaveni, Dr.M.Hemalatha, “A Perspective Analysis of Traffic Accident using Data Mining Techniques”, International Journal of Computer Applications,Volum 23- No. 7, pp. 40-48, June 2011.

30. S.Krishnaveni,Dr.M.Hemalatha,“Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms”, International Journal of Computer Applications, Volume 35– No.12, December 2011, pp. 30-37.

31. Beshah, T.; Ejigu, D.; Abraham, A.; Snasel, V.; Kromer, P., “Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety”, World Congress on Information and Communication Technologies (WICT), December 2011, pp. 1241 – 1246.

32. Vandana Munde, Sachin Deshpande, S.K.Shinde,“Data Mining for Traffic Accident Analysis”, International Conference on Advances in Computing and Management, 2012.

33. Olutayo V.A, Eludire A.A, “Traffic Accident Analysis Using Decision Trees and Neural Networks”, I.J. Information Technology and Computer Science, 2014, 02, 22-28.

34. Rajdeep Kaur Aulakh, “Association Rules Mining Using Effective Algorithm: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 3, March 2015, pp. 831-835.

35. Amit Mittal, Ashutosh Nagar, Kartik Gupta, Rishi Nahar, “Comparative Study of Various Frequent Pattern Mining Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 4, April 2015, pp. 550-553.

36. Dr. S. Vijayarani, Ms. R. Prasannalakshmi, “Comparative Analysis of Association Rule Generation Algorithms in Data Streams”, International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015, pp. 15-25.

37. Sunita B Aher, Mr. LOBO L.M.R.J., “Data Mining in Educational System using WEKA”, International Conference on Emerging Technology Trends (ICETT), 2011, pp. 20-25.

38. Sunita B Aher and Lobo L.M.R.J, “A Comparative Study of Association Rule Algorithms for Course Recommender System in E-Learning”, International Journal of Computer Applications, Volume 39 – No. 1, February 2012, pp. 48-52.

39. “Stratified Random Sampling”, [online]. Available at URL: http://www.stat.ualberta.ca/~prasad/361/STRATIFIED%20RANDOM%20SAMPLING.pdf.

40. Carlos Ordonez, Norberto Ezquerra, Cesar A. Santana, “Constraining and Summarizing Association Rules in Medical Data”, Knowledge and Information Systems, Volume 9, Issue 3, 2006, pp.. 259 – 283.

 

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2.

Authors:

Sadik Khan, Yashpal Singh, Ajay Kumar Sachan

Paper Title:

Web Mining in Search Engines for Improving Page Rank

Abstract: An application of web mining can be seen in the case of search engines. Most of the search engines are ranking their search results in response to users’ queries to make their search navigation easier. In this research, a survey of page ranking algorithms and comparison of some important algorithms in context of performance has been carried out.So this kind of problem is actual need of this proposed research work. One of the major problems for automatically constructed portals and information discovery systems is how to assign proper order to unvisited Web pages. Topic-specific crawlers and information seeking agents should try not to traverse the off-topic areas and concentrate on links that lead to documents of interest. In this chapter, we propose an effective approach based on the relevancy context graph to solve this problem.Some commonly used link algorithms are page rank, HITS and Weighted Page Content Rank. Most of the search engines are ranking their search results in response to user’s queries to make their search navigations easier. In this paper we give a study of page ranking algorithms and description about Pagerank , HITS, based on web content mining and structure mining that shows the relevancy of the pages to a given query is better determined, as compared to the Page Rank and HITS.

Keywords:
Web Mining ,Data mining, HITS, Search Engines, web content, Page rank, Web Logs, web structure mining, web content mining.


References:

1. M Eirinaki, M Vazirgiannis, Web Mining for Web Personalization, in ACM Transactions on Internet Technology (TOIT), 3(1), February (2003).
2. 1.M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM, 46(5):604-632, September (1999).809

3. S. Chakrabarti, B. Dom, D. Gibson, 1. Kleinberg, R Kumar, P. Raghavan, S. Rajagopalan, A Tomkins, Mining the Link Structure of the World Wide Web, IEEE Computer (1999) Vol.32 No.6.

4. S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks, 30(1 7): 107-117, 1998, Proceedings of the 7th International World Wide Web Conference(WWW7).

5. 1.M. Kleinberg, Hubs, Authorities, and Communities, ACM Computing Surveys, 31 (4), December (1999).

6. D7. Gibson, J. Kleinberg, P. Raghavan, Inferring Web Communities from Link Topology, in the Proceedings of the 9th ACM Conference on Hypertext and Hypermedia, (1998).

7. R Kumar, P. Raghavan, S. Rajagopalan, A Tomkins, Trawling the Web for Emerging Cyber-Communities, in Proceedings of the 8th WWW Conference (WWW8), (1999).

8. Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, SIGKDD Explorations, January 2000Nol. 1, Issue 2, pp. 12-23.

9. M. Rajman, M. Vesely, From Text to Knowledge: Document Processing and Visualization: a Text Mining Approach, in Proceedings of the NEMIS Launch Conference, International Workshop on Text Mining & its Applications, Patras, Greece, April(2003).

10. N. Oikonomakou, MVazirgiannis, A Review of Web Document Clustering approaches, in Proceedings of the NEMIS Launch Conference, International Workshop on Text Mining & its Applications, Patras, Greece, April (2003).

11. G. Pinski, F. Narin, Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics, in Information Processing and Management. 12, (1976).

12. S. Shearin and H. Liebermann, Intelligent Profiling by Example, Proc. of Intern. Conf. of Intelligent User Interfaces (IUI2001), p. 145-152, Santa Fe, NM, Jan. 14-17, 2001.
13. WEB MINING: A ROADMAP, Magdalini Eirinaki, Dept. of Informatics Athens University of Economics and Business.
14. Evaluating the datamining techniques and their roles in increasing the search speed data in web, Ayatollah Amoli Branch, Comput. Dept., Islamic Azad Univ., Amol, Iran , DOI: 10.1109/ICCSIT.2010.5563818 Conference: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, Volume: 9

 

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3.

Authors:

Md. Osman Goni Nayeem, Maung Ning Wan, Md. Kamrul Hasan

Paper Title:

Prediction of Disease Level Using Multilayer Perceptron of Artificial Neural Network for Patient Monitoring

Abstract: ANN has been proved as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. In our research, ANN has been used for predicting three different diseases (heart disease, liver disorder, lung cancer). Feed-forward back propagation neural network algorithm with Multi-Layer Perceptron is used as a classifier to distinguish between infected or non-infected person. The results of applying the ANNs methodology to diagnosis of thesedisease based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In our proposed work, Multi-Layer Perceptron with having 2 hidden layer is used to predict medical diseases. Here in case of liver disorder prediction patients are classified into four categories: normal condition, abnormal condition (initial), abnormal condition and severe condition. This neural network model shows good performance in predicting disease with less error.

Keywords:
Artificial Neural Network (ANN), Multilayer Perceptron, Heart Diseases, Liver Disorder, Lung Cancer.


References:

1. J. Dayhoff and J. DeLeo, “Artificial neural networks opening the black box,” Cancer Supplement, vol. 91, No. 8, pp. 16151635, 2001.
2. Dimitrios H. Mantzaris, George C. Anastassopoulos and Dimitrios K. Lymberopoulos,“Medical Disease Prediction Using Artificial Neural Networks” vol.91, NO.82,pp, 16253765 , July 5, 2008, IEEE.

3. V. Piuri and F. Scotti, “Morphological classification of blood leucocytes by microscope images,” in 2004 IEEE Int. Conf. Computational Intelligence for Measurement Systems and Applications, pp. 103108.

4. D. S. Huang and S. D. Ma, “Linear and nonlinear feedforward neural network classifiers: A comprehensive understanding,” J. Intelligent Syst., vol. 9, pp. 1–38, 1999.

5. Irfan Y. Khan, P.H. Zope, S.R. Suralka, “Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease” Volume 2, Issue 2, March 2013, International Journal of Engineering Science and Innovative Technology (IJESIT).

6. Kadhim Al-Shayea, “Artificial neural network in medical diagnosis”, International journal of computer science Vol.8, Issue 2, March 2011.Qeethara

7. Qeethara Kadhim Al-Shayea and Itedal S.H. Bahia, “Urinary system Dieseases Diagnosis Using Artificial neural networks”, IJCSNS, Vol.10, No.7, July 2010.

8. S. Moein, S.A. Monadjemi, and P. Moallem, “A Novel Fuzzy Neural Based Medical Diagnosis system”, International Journal of biological and Life Science 4:3, 2008.

9. Abdel- Badeeh M. Salem, “Case Base Reasoning Technology for Medical Diagnosis”, World Academic of Science, Engineering and Technology 7, 2007.

10. Ashish Dehariya, Ilyas Khan,Vijay K. Chaudhari and Saurabh Karsoliya, “ A Novel flow Reasoning of Medical Diagnostic system using Artificial Feed Forward Neural Networks”, IJCSE, Vol.3, No-3, March 2011.

11. Zhi- Hua Zhou, Yoan Jiang, Yu- Bin Yang & Shi- Fu Chen, “ Lung Cancer Cell Identification Based On Artificial Neural Network ensembles”, Artificial Intelligence in Medicine Vol24, No-1, 2002.

12. Xin Yao Senior Member, IEEE and Yong Liu, “A new evolutionary system for evolving Artificial Neural Networks”, IEEE Transaction on Neural Network Vol.8, No-1, May 1997 Transactions on information Technology in Biomedicine Vol 11, No.3, May- 2012.

13. Baker J A, Kornguth PJ, Lo JY, Williford ME, Floyd CE Jr(1995) : “Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon”. Radiology, 1995; 196(3): 817-22

14. P. S. Heckerling, G. Canaris, S. D. Flach, T. G. Tape, R. S. Wigton and B. S. Gerber, Predictors of urinary tract infection based on artificial neural networks and genetic algorithms, International Journal of Medical Informatics; April, 2007, Vol. 76 Issue 4, pp. 289-296.

15. S. A. Monadjemi and P. Moallem, Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach, International Review on Computers & Software, Jul., 2008, Vol. 3 Issue 4, pp. 406-411

16. R. Suganya, and S. Rajaram, “Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach”, American Journal of Applied Sciences 9 (6), 2009, pp. 938-945.

17. S. Babaei, A. Geranmayeh, “Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals”, Computers in Biology and Medicine (39), 2009, pp. 8–15.

18. Zhi-Hua Zhou, Member IEEE and Yuan Jiang, “Medical Diagnosis with C4.5 Rule Preceded By Artificial Neural Network Ensemble”, IEEE Transactions on information Technology in Biomedicine Vol-7, No-1, March 2003.

19. D. Gil, M. Johnsson, J. M. Garicia Chemizo, A. S. Paya and D. R. Fernandez, Application of Artificial Neural Networks in the Diagnosis of Urological Dysfunctions, Expert Systems with Applications, April, 2009, Vol. 36 Issue 3, pp. 5754-5760.

20. W. G. Baxt, “Application of artificial neural networks to clinical medicine,” Lancet, vol. 346, no. 113, pp. 58, 1995.

21. J. S. Chiu, Y. C. Li, F. C. Yu, and Y. F. Wang, “Applying an artificial neural network to predict osteoporosis in the elderly,” Studies in Health Technology and Informatics, vol. 124, pp. 609614, 2006.

22. G. Zhang and V. Berardi, “An investigation of neural networks in thyroid function diagnosis,” Health Care Management Science, vol.1, pp. 29–37, 1998.

 

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4.

Authors:

Alireza Noroziroshan, Shaghayegh Habibi

Paper Title:

A Performance Analysis of Memetic Algorithm, Genetic Algorithm and Simulated Annealing in Production System Optimization

Abstract: Researchers laid the foundation of evolutionary algorithms in the late 60s and since then, heuristic algorithms have been widely applied to several complex scheduling and sequencing problems during the recent studies. In this paper, memetic algorithm (MA), genetic algorithm (GA) and simulated annealing (SA) are applied to a complex sequencing problem. The problem under study concerns about sequencing problem in mixed-shop floor environment. The main objective is to minimize the overall make-span of multiple mixed-model assembly lines by finding the best job sequence and allocation. The superiority of MA’s performance is proved by evaluating standard deviation, optimal solution and mean value of obtained solutions.

Keywords:
Genetic Algorithm, Make-span, Memetic Algorithm, Simulated Annealing.


References:

1. M. P. Groover, Automation, production systems, and computer-integrated manufacturing, p.^pp. 212-215, Upper Saddle River, N.J.: London : Prentice Hall ; Prentice-Hall International, 2001.
2. D. R. Sule, Industrial scheduling, 20 Park Plaza,Co Boston: PWS Publishing Company. , 1997.

3. R. S. Russell, and B. W. Taylor, Operations management: quality and competitiveness in a global environment: Wiley, 2006.

4. V. Sekar, “Minimizing the make-span in a high-product mix shop-floor using integer programming,” M.S., State University of New York at Binghamton, United States — New York, 2007.

5. R. Brahim, “Assembly line design: The balancing of mixed-model hybrid assembly lines with genetic algorithms,(Series in advanced manufacturing),” 2006, 2006.

6. E. Eiben, and J. E. Smith, Introduction to evolutionary computing, Verlag Berlin Heidelberg New York: Springer, 2003.

7. Naderi, M. Zandieh, A. Khaleghi Ghoshe Balagh et al., “An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness,” Expert Systems With Applications, 2008.

8. M. Zandieh, S. M. T. Fatemi Ghomi, and S. M. Moattar Husseini, “An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times,” Applied Mathematics and Computation, vol. 180, no. 1, pp. 111-127, 2006.

9. B. Wang, Y. Rao, X. Shao et al., “Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm.” p. 387.

10. S. Karabati, and S. Sayin, “Assembly line balancing in a mixed-model sequencing environment with synchronous transfers,” European Journal of Operational Research, vol. 149, no. 2, pp. 417-429, 2003.

11. P. R. McMullen, and G. V. Frazier, “A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line,” IIE Transactions, vol. 32, no. 8, pp. 679-686, 2000.

12. W. L. Winston, Introduction to mathematical programming: applications and algorithms, Fourth Edition ed., CA93950, USA: Duxbury Resource Center, 2003.

13. M. Vazquez, and D. Whitley, “A hybrid genetic algorithm for the quadratic assignment problem.” pp. 135–142.

14. L. Wang, and D.-Z. Zheng, “An effective hybrid optimization strategy for job-shop scheduling problems,” Computers & Operations Research, vol. 28, no. 6, pp. 585-596, 2001.

15. M. Yazdani, M. Gholami, M. Zandieh et al., “A simulated Annealing Algorithm for Flexible Job Shop Scheduling Problem,” Journal of Applied Sciences, pp. 1-9, 2009.

16. M. Negnevitsky, Artificial intelligence: a guide to intelligent systems: Addison-Wesley, 2005.

17. Y. K. Kim, C. J. Hyun, and Y. Kim, “Sequencing in mixed model assembly lines: A genetic algorithm approach,” Computers & Operations Research, vol. 23, no. 12, pp. 1131-1145, 1996.

18. Z. Michalewicz, Genetic algorithms+ data structures= evolution programs, Charlotte, USA: Springer, 1996.

19. J. J. Grefenstette, “Optimization of control parameters for genetic algorithms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 16, no. 1, pp. 122-128, 1986.

20. Y. Y. Leu, L. A. Matheson, and L. P. Rees, “Assembly Line Balancing Using Genetic Algorithms with Heuristic-Generated Initial Populations and Multiple Evaluation Criteria*,” Decision Sciences, vol. 25, no. 4, pp. 581-606, 1994.

21. C. Oysu, and Z. Bingul, “Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining,” Engineering Applications of Artificial Intelligence, vol. 22, no. 3, pp. 389-396, 2009.

22. Fogel, L. J., Owens, A. J., and Walsh, M. J. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.

23. Moscato, P. On genetic crossover operators for relative order preservation.C3P Report 778, California Institute of Technology, Pasadena,CA 91125, 1989.

24. Stalk, George. “Time–the next source of competitive advantage.” (1988): 41-51.

 

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5.

Authors:

Ibrahim F. Alshammari, Haider A. Abdulkarim, Ali Abdulraheem Alwan

Paper Title:

CW Laser Combined with LED to Reduce the FWM in SAC-OCDMA Network

Abstract: A new technique based on LED combined with CW lasers in Spectral Amplitude Coding (SAC) Optical Code Division Multiple Access (OCDMA) networks, which allow reduction of the four-wave mixing (FWM) effect. In this paper, SAC-OCDMA networks have been developed and analyzed based on Multi Diagonal (MD) and Zero Cross Correlation (ZCC) codes. We simulate and investigate of three users design and concluded that the FWM can be reducing by using the LED source combined with CW lasers for each user in the code’s design. Our results show that the MD code gives better performance than the ZCC code by using our technique. In other words, the ratio of reducing the FWM power in the MD code is approximately -20 dBm, while in ZCC is -10 dBm..

Keywords:
Optical code division multiple access (OCDMA), Spectral amplitude coding (SAC); Multi diagonal (MD);Zero-Cross Correlation (ZCC); Four-Wave Mixing (FWM; Light Emitting Diode (LED).


References:
1. Abtin Keshavarzian, J. A. S. “Optical Orthogonal Code Acquisition in Fiber-Optic CDMA Systems via the Simple Serial-Search Method.” IEEE Transactions on Communication Vol. 50, No. 3 (2002).
2. Indu Bala, V. R. “Performance analysis of SAC based non-coherent optical CDMA system for OOC with variable data rates under noisy environment.” Indian Journal of Science and Technology Vol.2 No. 8: 49-52(2009)

3. Fuad A. Hatim, F. N. H., Sahbudin Shaari. “Effects of Nonlinear Stimulated Brillouin Scattering on Performance Analysis of an Optical CDMA Transmission System.” Journal of Optical Communications 30: 104-108(2009)

4. Osamu Aso, M. T., Shu Namiki. “Four-Wave Mixing in Optical Fibers and Its Applications.” Furukawa Review 19: 63-68(2000)

5. K.P. Lor, K. S. C.. “Theory of nondegenerate four-wave mixing in a birefringent optical fibre.” Optics Communications 152: 26-30(1998)

6. Abd, T. H., S. A. Aljunid, et al. “Development of a new code family based on SAC-OCDMA system with large cardinality for OCDMA network.” Optical Fiber Technology 17(4): 273-280

7. Hamza M. R. Al-Khafaji, S. A. Aljunid., Hilal A. Fadhil. Spectral Efficiency of Unipolar SAC–OCDMA System Considering Noise Effects. IEEE Symposium on Indestrial Electronics and Applications (ISIEA 2011). Langkawi, Malaysia, IEEE explore: 218-222(2011)

8. S. P. Singh, N. S.. “Nonlinear Effects In Optical Fibers: Origin, Managment And Applications.” Progress In Electromagnetics Research, PIER Vol. 73: 249–275(2007)

9. S.V. Kartalopoulos, Introduction to DWDM Technology — Data in a Rainbow, John Wiley & Sons, 2000

10. Anuar, M. S., S. A. Aljunid, et al. (2007). “New design of spectral amplitude coding in OCDMA with zero cross-correlation.” Optics Communications 282(14): 2659-2664(2007)

 

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6.

Authors:

K. Uma Devi, B. Lalitha

Paper Title:

Optimizing Service Selection in Combinatorial Auction by Resolving Non-Linear Programming Constraints

Abstract: The selection of services with the aim to fulfill the quality constraints became critical and challenging research aspect in the field of service computing to promote automated service selection in service-based systems (SBSs), especially when the quality constraints are stringent. However, none of the existing approaches for quality-aware service composition has sufficiently considered QoS parameters to determine the best service. This paper proposes an optimization model for SBS to automate the process of quality aware service selection. Furthermore, this paper presents a compositional quality model to analyze and optimize the quality constraints that play a vital role in Winner Determination Problem (WDP)

Keywords:
critical and challenging research aspect, computing to promote automated service selection, QoS parameters, optimization model for SBS, Winner Determination Problem (WDP).


References:

1. Qiang He, Jun Yan,” Quality-Aware Service Selection for Service-Based Systems Based on Iterative Multi-Attribute Combinatorial Auction”, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 40, NO. 2, FEBRUARY 2014, pp: 192-215.
2. M.R. Andersson, T. Sandholm, “Time-quality tradeoffs in reallocative negotiation with combinatorial contract types”, Proc. American Association for Artificial Intelligence-99, Orlando, FL, 1999, pp. 3-10.

3. Federal Communications Commission.

4. http://wireless.fcc.gov/auctions/31/, April 2000.

5. S.J. Rassenti, V.L. Smith, R.L. Buffin, “A combinatorial auction mechanism for airport time slot allocation”, Bell Journal of Economics, vol. 13, 1982, pp. 402 – 417.

6. F.Kelly and R.Steinberg, “A combinatorial auction with multiple winners for universal service”, Management Science, vol. 46, 2000, pp. 586 – 596.

7. T. Sandholm and S. Suri, “BOB: Improved winner determination in combinatorial auctions and generalizations”, Artificial Intelligence, vol. 145, 2003, pp. 33 – 58.

8. G. Canfora, M.D. Penta, R. Esposito, F. Perfetto, and M.L. Villani, “Service Composition (Re)Binding Driven by Application-Specific QoS,” Proc. Fourth Int’l Conf. Service-Oriented Computing (ICSOC ’06), pp. 141-152, 2006.

9. Y. Li, J. Huai, T. Deng, H. Sun, H. Guo, and Z. Du, “QoS-Aware Service Composition in Service Overlay Networks,” Proc. IEEE Int’l Conf. Web Services (ICWS ’07), pp. 703-710, 2007.

10. D. Ardagna and B. Pernici, “Adaptive Service Composition in Flexible Processes,” IEEE Transactions on Software Engineering, vol. 33, pp. 369-384, 2007

11. Object Management Group. (2011). Business Process Model And Notation (BPMN) Version 2.0. Available: http://www.omg.org/spec/BPMN/2.0/PDF/

12. OASIS. (2007). Web Services Business Process Execution Language Version 2.0. Available: http://docs.oasis-open.org/wsbpel/2.0/wsbpelv2.0. pdf

13. Q. He, J. Han, Y. Yang, J. Grundy, and H. Jin, “QoS-Driven Service Selection for Multi-tenant SaaS,” Proc. 2012 IEEE Fifth International Conference on Cloud Computing, Honolulu, HI, USA, 2012, pp. 566- 573.

14. L. Zeng, B. Benatallah, A. H. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, “QoS-Aware Middleware for Web Service Composition,” IEEE Transactions on Software Engineering, vol. 30, pp. 311-327, 2004.

 

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7.

Authors:

Ruby Singh, Chiranjit Dutta, Ranjeet Singh

Paper Title:

Increasing Efficiency & Detailing in Analysis of Market Trends using SAS

Abstract: In the fast moving world and changing scenario of market (Business) there is need for improving and updating at every point of time, in order to obtain maximum and exact output companies need detailed data to work on hence this paper involves researching on increasing the efficiency so as to obtain better and exact prediction for the product to be used. The SAS System of software provides a wide variety of tools for analyzing market research data. Everything from simple summary analysis to advanced statistical and graphical techniques is available. Users holding different levels of expertise in both software and market research methodologies benefit from these tools. This project briefly discusses some of the methods available in the SAS System and will examine a case study of a current SAS software user, see how they have implemented their market research applications and increase the efficiency in prediction of aspects related to products. SAS ®is widely accepted as the gold standard for determining safety and efficacy for clinical trials, and it provides the primary mechanism for preparing data for traditional clinical research analysis activities. However, most SAS users in the biopharmaceutical industry are unaware of the broad range of SAS analytics that are widely applied in other industries. This paper discusses and describes how SAS business and advanced analytics can be used to design Better trials, forecast patient-based activities, and optimize other operational processes. Applying business and advanced analytics to clinical trial operations represents a new and improved approach to reducing the cost and time associated with managing clinical research projects. As a result, the roles of SAS experts in the biopharmaceutical industry are expanded.

Keywords:
SAS, BI-Tools, Market-Research


References:

1. Kuhfeld, Warren F. (1993), Marketing Research Methods in the SAEfPSystem, A Collection of Papers and Handouts.Latour, Kristin (1994), “Market Research Methods in the SASe System,” CSMA Conference, Orlando, FL.
2. Roeder, Kelly (2014), “Giving Customers What They Want; SAS Communicetions”, 20, 14-16.

3. SAS Institute Inc. (2014), Introduction to Marlcet Research Using the SAS” System, Cary, NC; SAS Institute Inc.

4. SAS Institute Inc. (2013), SAEfPTechnical Report R-109, Conjoint Analysis Examples, Cary, NC: SAS Institute Inc.

5. Shorland, Michael and Zodrow, Michael (2013), “BearCreek Builds In-house Gold Mine,” Direct Marlceting,35-40.

6. Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications By Kattamuri S. Sarma

7. SAS | Business Analytics and Business Intelligence www.sas.com/

 

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8.

Authors:

Hindayati Mustafidah, Suwarsito

Paper Title:

Error Rate Testing of Training Algorithm in Back Propagation Network

Abstract: Artificial Neural Network (ANN), especially back propagation method has been widely applied to help solve problems in many areas of life, eg for the purposes of forecasting, diagnostics, and pattern recognition. An important part at ANN in determining the performance of the network is training algorithm used. Because there are 12 training algorithms that can be used at back propagation method, of course, it’s needed to be selected the most optimal algorithm in order to obtain the best results. Training algorithm performance is said optimal in providing solutions can be seen from the error generated. The smaller the error is generated, the more optimal performance of the algorithm. In this study, testing to get the training algorithm has the smallest error rate of 12 existing algorithms. Testing begins with the preparation of a computer program modules using MATLAB programming language to get the error value of the network output for each training algorithm. Each program for each training algorithm executed 20 times. Furthermore, the error of the network output was tested using analysis of variance with an alpha level of 5% to get a training algorithm which has the smallest error rate. The conclusion of the test results is that the training algorithm “trainlm” has the smallest error with the network parameters for the target error = 0.001 (10-3), the maximum epoch = 10000, learning rate (lr) = 0.01, and 5 neuron input data with 1 neuron output.

Keywords:
error rate, training algorithm, back propagation, network parameters


References:

1. J.J. Siang, 2009, Neural networks and programming Using MATLAB, ANDI, Yogyakarta.
2. M.T. Jones, 2008, Artificial IntelligenceA Systems Approach. Infinity Science Press LLC, New Delhi.

3. S. Kusumadewi , S. Hartati, 2006, Integration of Fuzzy Systems and Neural Networks, Graha Ilmu, Yogyakarta.

4. S. Kusumadewi, 2004, Develop Artificial Neural Networks Using MATLAB and EXCEL LINK, Graha Ilmu, Yogyakarta.

5. Harjono, D. Aryanto, 2009, “Application of Artificial Neural Networks to Predict Student Achievement Study”, SAINTEK ISSN1411-2558, Vol. 5 No. 2.

6. H. Mustafidah, D. Aryanto, D.K. Hakim, 2013, “Optimization Test of Algorithm Conjugate Gradient Training on Artificial Neural Networks”, Proceeding SENATEK, ISBN: 978-602-14355-0-2 pp. B.9-1. 21st September 2013.

7. H. Mustafidah, D.K. Hakim, S. Sugiyanto, 2013, “Optimization Level of Training Algorithm on Artificial Neural Networks (Case Study: Student Achievement Prediction)”, JUITA ISSN: 2086-9398 Vol. II No. 3, May 2013, pp. 159 – 166.

8. F. Wibowo, S. Sugiyanto, H. Mustafidah, 2013, “Data Pattern Recognition Accuracy Level on Neural Network Improved Training Algorithm Method in Batch Mode”, JUITA, ISSN 2086-9398, Vol. II No. 4, November 2013, pp. 259 – 264.

9. H. Mustafidah, S. Hartati, R. Wardoyo, A. Harjoko, 2013, “Prediction of Test Items Validity Using Artificial Neural Network”, Proceeding International Conference on Education, Technology, and Science (NETS) 2013, “Improving The Quality Of Education To Face The Impact Of Technology”. December 28th, 2013. University Muhammadiyah of Purwokerto.

10. T. Taniredja, H. Mustafidah, 2011, “Quantitative Research (an Introduction)”, Alfabeta, Bandung.

 

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9.

Authors:

Md. Kamrul Hasan, Md. Osman Goni Nayeem, Md. Asif Ahamed, Maung Ning Wan, Mohiuddin Ahmad

Paper Title:

Randomness Prediction of Brain Tumor by Analyzing EEG Signal Using Approximate Entropy and Regression Analysis

Abstract: Brain activity commonly known as the Electroencephalographic (EEG) signal is the measure of the brain state either normal or abnormal condition of the human brain. The brain contains about 10 Billion or more working brain cells. Brain tumor is life frightening disease of human brain. The brain tumor is the disease which neutralize the neuron day by day on the brain. The detection of brain tumor is one of the major problem by analyzing the brain signal (EEG Signal). The more the age of the tumor in the brain indicates the more randomness that is more unpredictable. In our research, we tried to find out the solution for the detection of tumor level that exist in the human brain. To complete this research, EEG data of the tumor patients having different age of tumor growth is analyzed and regression equation is determined for the prediction of the randomness. By using this regression equation, clinical person may provide the treatment for the tumor affected persons.

Keywords:
EEG Signal, Approximate Entropy (ApEn), Brain Tumor, Regression Analysis


References:

1. K. Lehnertz,F. Mormann, T. Kreuz, R. Andrzejak,C. Rieke, P. David , andC. Elger. Seizure prediction by nonlinear EEG analysis. IEEE Engineering in Medicine and Biology Magazine. 2003.
2. P. K. Nayak, and N. U.Cholayya. Independent componentanalysis of Electroencephalogram. IEE Japan Papers of Technical Meeting on Medical and Biological Engineering. 2006.

3. National Brain Tumor Society (April 2010). Symptoms & Diagnosis. April 2010. [Online]

4. http://www.braintumor.org/patientsfamily-friends/about-brain-tumors/symptoms-and-diagnosis/.

5. J. D.Bronzino. Biomedical Engineering Handbook. New York: CRC Press LLC. Vol. I. 2nd edition. 2000.

6. American Brain Tumor Association. Brain Tumor Primer – A Comprehensive Introduction to Brain Tumor. ABT Press. 9th edition. 2012.

7. M. Murugesan, R. Sukanesh. Automated Detection of Brain Tumor in EEG Signals Using Artificial Neural Networks. International Conference on Advances in Computing, Control, & Telecommunication Technologies. 2009..

8. The Musella Foundation for Brain Tumor Research &Information,Brain Tumor Symptoms Survery Results, April 2010.[Online]. http://www.virtualtrials.com/braintumorsymptomssurvey/.

9. M. K. Rosenblum. The 2007 WHO Classification of Nervous System Tumors: Newly Recognized Members of the Mixed Glioneuronal Group. International Society of Neuropathology. 2007.

10. L. S. Yew and G. A. Leng. The Diagnosis of brain tumor. Singapore Medical Journal. Vol 9. 1968.

11. L. R.Schad, R.Boesecke, W. Schlegel, G. H. Hartmann, V. Sturm, L. G. Strauss, W. J. Lorenz. Three dimensional image correlation of CT, MR, and PET studies in radiotherapy treatment planning of brain tumors. J Comput Assist Tomogr. 1987.

12. Boutros, Nashaat .Standard EEG: A Research Roadmap for Neuropsychiatry.

13. H.Ocak. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Elsevier journal on Expert Systems with
Applications. March 2009.

14. C.Cobelli, E Carson. Introduction to Modeling in Physiology and Medicine. Elsevier. 2007.

15. M. L.Steyn-Ross, D. A. Steyn-Ross, J. W. Sleigh, D. T. J.Liley. Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: Evidence for a general anesthetic-induced phase transition. Phys Rev E Stat Phys Plasmas Fluids RelatInterdiscip Topics.1999.

16. Pincus, M. Steven, I. M. Gladstone, and A. R.Ehrenkranz. A regularity statistic for medical data analysis. Journal of Clinical Monitoring. October 1991.

17. M. A.Riheen, M. W. Rahman, A. B. M. A. Hossain. Selection of Proper Frequency Band and Compatible Features for Left and Right Hand Movement from EEG Signal Analysis. 16th International Conference on Computer and Information Technology (ICCIT). 2013. DOI: 10.1109/ICCITechn.2014.6997366.

18. Measurement of error. Available at: www.socialresearchmethods.net/kb/measerr.php

19. H. T.Ocbagabir, K. A. I.Aboalayon, M.Faezipour. Efficient EEG analysis for seizure monitoring in epileptic patients. Systems, Applications and Technology Conference (LISAT). 2013.DOI: 10.1109/LISAT.2013.6578218

20. Regression analysis. Available at:

21. en.wikipedia.org/wiki/Regression_analysis

22. D. A. Freedman. Statistical Models: Theory and Practice. Cambridge University Press. 2005.

23. EEG Recording. Available at:

24. http://www.aha.ru/~geivanit/EEGmanual/Recording.htm

 

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10.

Authors:

Cyrus Babu Ong`ondo, Githae Wanyona, Abednego Gwaya

Paper Title:

An Investigation into the Factors that Influence Project Control Process in the Implementation of Construction Projects in Kenya

Abstract: The aim of project control process is to ensure projects are delivered on-time, within-budget, desired quality amongst other performance measures (Jackson, 2004). In the construction industry of Kenya, many technological developments have occurred just like other developing countries leading to emergency of project control techniques to aid on the effectiveness of the control process, they include Gantt, Bar charts, Program evaluation and review techniques (PERT) and critical path method (CPM).In addition, many software packages have also been developed to support these techniques. Further, the Government of Kenya (GoK) uses the Building organization and operations manual (BOOM) as an official document to guide implementation of projects (Munano, 2012). Despite the wide use of these control methods and techniques, many projects still fail during implementation. Pointing to a potential gap on what influences the effectiveness of the control process in management of construction projects. This study therefore sought to investigate factors influencing project control process in an effort to enhance effectiveness in project controls. This cross-sectional research adopted a mixed-method design consisting of analysis of a questionnaire survey administered to active 67No. (NCA1, NCA2, NCA3 and NCA4) contractors selected by way of stratified random sampling. A similar approach was also used to select 53No.Consultants with a response rate of 78% and 81% respectively. Data analysis techniques employed include descriptive statistics and thematic analysis. The study established thirty six (36No.) factors that influence project control process. These factors were clustered into seven (7No.) groups. They include; Pre-construction planning (RII=0.786), Project communication (RII=0.801), Commitment to project (RII=0.763), Project administration (RII=0.817) and factors related to Monitoring & Evaluation (RII=0.785).It’s recommended that project managers should enhance their pre-construction planning strategies and establish a good enabling environment for the execution of construction projects by constituting a competent project team, clearly defining the performance benchmarks, outlining the project scope, establish a sound communication plan for the project and receive commitment from all the project participants.

Keywords:
Project control, construction industry, Project control factors, Kenya


References:

1. Abd El-Razek, M. (2008). Causes of Delay in Building Construction Projects In Egypt. Journal of Construction Engineering & Management, , 134(11),831-841.
2. Akintoye, A. (2007). Collaborative relationships in construction-The UK contractor`s perception. Engineering,Construction and Architectural Management.

3. Atkinson, R. (1999). Project management: Cost,time and quality,two best guesses and a phenomena.its time to accept other success criteria. International Journal of Project Management, Vol 17,Issue 6 December,1999,Pages 337-342..

4. Chandara, P. (2002). Projects Planning,Financing,Implementation and Review. Tata: McGraw-Hill Publishing Company.

5. Chitkara, K. (2002). Construction Project Management Planning,Scheduling and Control. Hill Publishing Company Ltd.

6. Christenson, D. (2008). Using vision as a critical Success element in Project Management. International Journal of Project Management.

7. [Cooke, B. W. (2004). Construction Planning Programming and Control. Oxford: Blackwell Publishing.

8. Cooke-Davies, T. (2002). “The real success factors on projects. International Journal of Project Management.

9. Egbu, C. (1998). “Planning and Control processes and techinques for refurbishment management.”. Construction Management and Economics, 16(3),315-325.

10. Fena-Mora, F. (2001). Dynamic Planning and control methodologyfor design/build fast-track construction projects. Journal of Construction Engineering and Management, 127(1),1-17.

11. Floyd, L. (2004). ” Application of appropriate control tools for contract type”. Cost Engineering, 46(2),25-30.

12. Forsythe, P. (2008). Modelling customer perceived quality in housing. International journal of project management,Elsevier Science Ltd and IPMA.

13. Fortune, J. (2006). Framing of critical success factors by a systems model. International Journal of Project Management,Elsevier Science Ltd and IPMA.

14. Frimpong, Y. (2003). Delay and cost overruns in Construction of Ground water Projects in developing countries. International Journal of Project Management, 21,321-326.

15. George, R. (2008). Critical activities in front End planning process. Journal of Management of Engineering.

16. Gichunge, H. (2000). Risk management in the Building Industry in Kenya. Unpublished PHD.Thesis.University of Nairobi.

17. Goodman, L. (1988). Project Planning and Management-an integrated system for improving productivity. New York: Van Nostrand Reinhold Company Inc.

18. Greer, M. (1999). Handbook of Human performance Technology. San Francisco: Jossey-Bas.

19. Gwaya, A. (2014). Development of appropriate project management factors for the construction industry in Kenya. International Journal of Soft Computing and Engineering (IJSCE), ISSN:2231-2307,Vol 4,Issue 1.

20. Hendrickson. (1999). Causes of Delay in Construction. Journal of Construction Engineering and Management, Vol 134,issue 11,p831.

21. Hillebrandt, P. (2000). Economic theory and the construction Industry.3rd Edition. London: Macmillan.

22. Iyer, K. J. (2005). Factors affecting cost performance evidence from indian construction projects. International journal of project management,, 23 (4),283-295.

23. Jackson, B. (2004). Construction Management Jump Start. CA: Sybex Incorporated Alaneda.

24. Johnson, G. (2006). Exploring Corporate Strategy 7th Edition. London: Pearson Education.

25. Kagiri, N. (2005). Time and Cost overrun in Power projects in Kenya: A case study of Kenya Electricity Generating Company Ltd. Unpublished MBA Thesis.University of Nairobi.

26. Kaming, P. (1997). Factors Influencing Construction Time and Cost Overruns on High-Rise Projects in Indonesia. Journal of Construction Management and Economics, 7,83-94.

27. Kenny, C. (2007). Construction,Corruption and developing countries. World Bank policy Research working paper.

28. Kerzner, H. (2006). Project Management: A systems Approach to Planning,Scheduling and Controlling 9th Edition. John Wiley & Sons publications.

29. Kongere, N. S. (2010). Project Management,From Design to Implementation. Nairobi: Richmond Designers and Printers.

30. Lester, A. (2000). Project Planning and Control. Oxford: Butterworth Heinemann.

31. Lindahl, G. (2007). Client`s goals and the Construction Project Management Process. Journal of Construction Management and Economics.

32. Ling, F. (2009). How Project Managers can better control the performance of design build projects. International Journal of Project Management, 22(6),477-488.

33. Masu, S. (2006). An investigation into the causes and impact of resource mix practices in the performance of construction firms in Kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

34. Morris, S. (1990). Cost and Time Overruns in Public Sector Projects.

35. Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

36. Munano, A. (2012). Pre-constrcution Planning: Exploring the factors that influence timelines of project completion for public sectors buildings in Kenya.
Unpublished Master of Construction Management Thesis.Jomo Kenyatta University.

37. Musa, G. (1999). Determination of Factors Influencing Projects Delays in Water Projects in Kenya: The case of Government Funded Projects. Nairobi: Unpublished MBA Thesis University of Nairobi.

38. Mwandali, D. (1996). Analysis of Major Factors that affect Projects Management: A Case of Kenya Railways Projects. Nairobi: Unpublished MBA Thesis,University of Nairobi.

39. Nguyen, A. (2004). A study on Project success factors in large construction projects in Vietnam.

40. Nicholas, J. (2001). Project Management for Business and Technology. New Jersey: Prentice Hall.

41. Olawale, Y. a. (2010). “Cost and time control of construction projects: Inhibitng factors and mitigating measures in practice”. Construction Management and Economics, 28 (5),509-526.

42. Pellicer, E. (2005). Cost control in Consulting engineering firms. Journal of Management in Engineering, 21 (4),189-192.

43. Project Management Institute. (2013). PMBOK: A guide to the Project Management Book of Knowledge. Project Management Institute.

44. Rozenes, S. (2006). “Project Control: Literature review”. Project Management Journal, 37(4) 4-14.

45. Samuelson, W. (2006). Managerial Economics.5th Edition. New Jersey: John Wiley & Sons.

46. Talukhaba, A. (1998). Time and Cost Performance of Construction Projects. Nairobi: Unpublished M.A.Thesis,University of Nairobi.

47. Tucker, L. A. (1987). Is Construction Project planning really doing its job?.A critical focus,role and progress in the construction management economic. Vol 5,243-266.

48. Wanyona, G. (2005). Risk Managment in the cost planning and control of building projects.The case of quantity Surveying profession in Kenya. Unpublished PhD Thesis.University of Cape Town.

49. White, D. F. (2002). Current practice in project management-An Emperical study. International Journal of Project Management, 20(2),1-11.

50. Yakubu, O. a. (2009). Cost and time control of construction projects: A survey of Contractors and Consultants. Construction Information Quarterly, , 11(2),53-59.

51. Zhen Yu, Z. (2010). Application of innovative Critical Chain Method for project planning and control. Journal of Construction Engineering and Management.

 

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11.

Authors:

Peter Mwangi Njogu, Alkizim Ahmad, Abednego Gwaya

Paper Title:

Identifying Key Risk Influencing Project Delivery in Kenya from Contractors’ Perspective

Abstract: The construction industry is crucial in the country’s economy growth. The Kenyan construction industry has been contributing immensely towards the Gross Domestic Product (GDP). The statistics by the Kenya Bureau Statistics (Republic of Kenya, 2014), indicate that the industry contributed 4.2%, 4.1%, 4.2%, and 4.4% towards the Gross Domestic Product (GDP) for the years 2010, 2011, 2012 and 2013 respectively. Despite this praise, studies in recent years have shown poor delivery of construction projects in relation to project objectives. This has been attributed to the many risks inherent in the industry (Ehsan et al., 2010). This has provoked an increased interest into the need for risk management in the industry. The main objective of this study was to determine the key construction risk which affects construction project delivery in Kenya in terms of cost, time, quality, environmental sustainability, and health and safety from contractors’ perspective. Response measures to these risks are believed shall enhance project delivery among contractors.This study was conducted through a review of existing literature and through self-administered questionnaires. The study targeted contractors registered in Kenya by the National Construction Authority (NCA). A sample of 190 respondents was selected through stratified random sampling to participate in this study. Sixteen (16) of the respondents were from class NCA 1, 12 from class NCA 2, 22 from class NCA 3, 74 from class NCA 4 and 66 from class NCA 5. Senior managers, project managers, technical managers, architects, quantity surveyors and engineers working with the contractors constituted the sample units for this study. Ninety eight (98) valid questionnaires were returned.The study assessed the likelihood of occurrence of risks and their impact on project objectives in terms of cost, time, quality, environment and health and safety; ranked the risks depending on their significance index score thus determined the key risks. Statistical package for social science (SPSS) analysis software was used to analyze data collected for the purpose of interpretation and conclusions. Descriptive statistic was applied where some measures of distribution, central tendency and dispersion were used. Findings were presented using descriptive statistical tools like tables and radar diagram. Based on a comprehensive assessment of risk probability and impact on the project objectives, 26 key risk factors were identified and ranked. Project time and cost were found to be the project objectives most vulnerable to construction risk. “Delay in payments” had the highest level of impact on both time and cost having a Risk Significance Index Score (RSIS) of 0.5849 and 0.5514 respectively. The second ranked risk was “excessive approval procedures in administrative government departments” The risk had a major impact on both time and cost at RSIS of 0.5641 and 0.5000 respectively. “Information unavailability-details, drawings, sketches” is the third ranked risk. Revised Version Manuscript Received on August 12, 2015. Njogu Peter Mwangi, Masters Student- Construction Project Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya. Ahmad Alkizim, Senior Lecturer- Construction Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya Gwaya Abednego, Lecturer- Construction Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya. The risk has a significant impact on project quality having RSIS of 0.5188 and its highest impact on project time having RSIS of 0.5527. “Design variations required by clients” was found to have high impact on both time and cost having RSIS of 0.5474 and 0.5322 respectively. The findings of this study shall be useful not only to contractors but also consultants and policy makers in the construction industry in managing construction risks thereby improving project delivery in Kenya.

Keywords:
risk, risk management, construction projects objectives, contractors’ perspective


References:

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Authors:

Abubakar S. Umar, Muntaqa D. Alhassan, Kabiru Aminu, Salahuddeen G. Ahmad

Paper Title:

Modelling and Control of Dynamical Systems Using Neural Network – A Review

Abstract: This paper presents a brief review on how artificial neural networks can be used in modelling and control of dynamical systems. The paper is broadly categorized into two; the first part is a short overview on artificial neural networks, particularly its generalization property, as applied to systems identification. The subsequent part contains a review onsome of the typical approaches used in the control of dynamical systems using neural networks which includes model predictive control, NARMA-L2 Control and model reference control. Finally, a comparative conclusion was made to distinguish the performances of the different control methods presented in this paper.

Keywords:
Neural Network Controllers; Generalization; Systems Modelling; Control Systems


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Authors:

Harpreet Singh

Paper Title:

Robust Video Watermarking Algorithm Using K-Harries Feature Point Detection