Volume-7 Issue-1

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Volume-7 Issue-1, March 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Samir Khudhir Al-ani, Nada Abdulfatah Khattab

Paper Title:

Computational Optimization Aberration Coefficients of an Einzel Lens Operated Under Zero Magnification

Abstract:  In this researcher  has been studied to design an einzel  lens and  this present researcher, Which concerted about the design of electrostatic potential lens for  focused charge particle beam by using inverse method in designing to electrostatic  lens ,the paraxial  ray equation was solved using Rung – Kutta  method  ,The spherical and chromatic aberration coefficient Cs and Cc, respectively have been computed  using Simpsons rule.  The shape of the electrode of the electrostatic lens were determined by solving Laplace’s equation, in this research, the results showed low values of spherical and chromatic aberrations which are considered as good criteria for good design Electron Optics, einzel Lens.

Keywords:
Electrostatic Lens, Spherical Aberrations, Chromatic Aberrations


References:

1.       Nagy A. and Szilagyi M. (1974) ” introduction to the theory of  space charge optics  (Macmillan Press: London).
2.       Szilagyi, M. (1988), Electron and ion optics, (Plenum press: New York).

3.       Kirestein,  P.  T., Gordon,  S, K. and  Willam,  E.  W. (1967) Space- Charge Flow

4.       Hawkes P.W (2004). Recent advances  in electron optics  and electron microscopy, Annales de la Fondation Louis de Broglie, 29(1), 837-855.

5.       Sise, O.; Ulu, M.and Dogan, M. (2007), Characterization and modeling of multi-element electrostatic lens systems,

6.       P.W.  Hawkes and  A. Septier ed. Septier, Lens aberration Focusing of charged partical” ,Academic press ,New York, 1967.

7.       Polyanin, A. D. (2002). Handbook of Linear Partial Differential Equations for Engineers and Scientists. Boca Raton: Chapman & Hall/CRC Press.

8.       Hawkes P. W. and Kasper E., (1989), Principles of electron optics ,1 (Academic Press: London).

9.       Al-Meshhdany, l.  A. M. (2002) “Theoretical desigen of an electron gun lenses using numerical methods, M.Sc. Thesis College of education for women, university of Baghdad, Iraq .

10.    Munro. E. 1975.  A  Set of Computer Programs for Calculating the Properties of Electron  Lenses, Cambridge  University, Eng. Dept., Report CUED/B-ELECT/TR45.

11.    Al-Khashab, M.A. and M. T. Al-Shamma (2009) , ”Minimizing the aberration of the unipotential electrostatic lenses of multi-electrodes

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2.

Authors:

Poorva Khemaria, Shiv Kumar, Babita Pathik

Paper Title:

Implementation of Fog Computing in Cloud Enterprise for Data Security and Privacy Management

Abstract: advancement of cloud technology named as fog computing. The process of fog computing faced a problem of latency and internet connectivity. The access of data over the fog computing need some trust based authentication and authorization process. In fog computing environment two major issue one is data leakage and other is location privacy. The location privacy preserve the user access and authentication process. The location privacy in fog computing is major issue. For the location privacy used various authentication and authorization process. To address these dangers, auditable information stockpiling administration has been proposed with regards to distributed computing to secure the information. Strategies, for example, holomorphic encryption and searchable encryption are consolidated to give uprightness, confidentiality and variability for distributed storage framework to permit a customer to check its information put away on untrusted servers. In this paper used Bloom filter data structure for the location privacy in fog computing model. The fog computing model work very efficiently in terms of low latency and high speed.

Keywords:
WSN, AOI, POI, SA, BM, CEP.


References:

1.       Flavio Bonomi, Rodolfo Milito, Jiang Zhu and Sateesh“Fog Computing and Its Role in the Internet of Things”, ACM, 2012, Pp 13-16.
2.       Kirak Hong, David Lillethun, BeateOttenwälder and Boris Koldehofe “Opportunistic Spatio-temporal Event Processing for Mobile Situation Awareness”, ACM, 2013, Pp 1-12.

3.       Kirak Hong, David Lillethun, Umak is hore Ramachandran, Beate Ottenwälder and Boris Koldehofe “Mobile Fog: A Programming Model for Large–Scale Applications on the Internet of Things”, ACM, 2013, Pp 1-6.

4.       Takayuki Nishio, Ryoichi Shinkuma, Tatsuro Takahashi and Narayan B. Mandayam “Service-Oriented Heterogeneous Resource Sharing for Optimizing Service Latency in Mobile Cloud”, ACM, 2013, Pp 19-26.

5.       Beate Ottenwälder, Boris Koldehofe, Kurt Rothermel and Umakishore Ramachandran “MigCEP: Operator Migration for Mobility Driven Distributed Complex Event Processing”, ACM, 2013, Pp 1-12.

6.       Ivan Stojmenovic and Sheng Wen “The Fog Computing Paradigm: Scenarios and Security Issues”, ACSIS, 2014, Pp 1-8.

7.       Stavros Salonikias, IoannisMavridis and Dimitris Gritzalis “Access Control Issues in Utilizing Fog Computing for Transport Infrastructure”, Springer, 2011, Pp 1-12.

8.       Tom H. Luan, Longxiang Gao, Zhi Li, Yang Xiang, Guiyi Weand Limin Sun “Fog Computing: Focusing on Mobile Users at the Edge”, arXiv, 2016, Pp 1-11.

9.       Salvatore J. Stolfo, Malek Ben Salem and Angelos D. Keromytis “Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud”, IEEE, 2012, Pp 125-128.

10.    Mohammad Aazam andEui-Nam Huh “Fog Computing and Smart Gateway Based Communication for Cloud of Things”, IEEE, 2014, Pp 464-470.

11.    Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu “Fog Computing: A Platform for Internet of Things and Analytics”, Springer, 2014, 2014, Pp 169-186.

12.    Luis M Vaquero and Luis. Rodero-Merino “Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing”, HPL, 2014, Pp 1-6.

13.    Flavio Bonomi, “Connected Vehicles, theInternet of Things, and Fog Computing”, VANET 2011, Pp 44-56.

14.    Behrisch, M., Bieker, L., Erdmann, J., andKrajzewicz, D. Sumo “Simulation of urban mobility-an overview”, The Third International Conference on Advances in System Simulation, 2011, Pp 55–60.

15.    Bonomi, F., Milito, R., Zhu, J., and Addepalli,S. “Fog Computing and Its Role in the Internet of Things”, ACM, 2012,Pp. 13–16.

16.    A., Lu, H., Zheng, X., Musolesi, M., Fodor, K., and Ahn, G.-S. “The rise of pe

17.    Campbell, A. T., Eisenman, S. B., Lane, N. D.,Miluzzo, E., Peterson, R. ople-centric sensing”, IEEE, 2010, Pp 12–21.

18.    Cugola, G., and Margara, A. “Tesla: a formallydefined event specification language” ACM, 2010, Pp 50–61.

19.    Cugola, G., and Margara, A. “Low latencycomplex event processing on parallel hardware”, JPDC, 2012, Pp 205–218.

20.    Hendawi, A. M., and Mokbel, M. F. “Panda: APredictive Spatio-Temporal Query Processor”. International Conference on Advances in Geographic Information Systems, 2012, Pp 13–22.

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3.

Authors:

Ratnesh Kumar Jain, Shiv Kumar, Babita Pathik

Paper Title:

An Enhancement on Block Cipher Key for Advanced Encryption Standard

Abstract:  The United State Government has standardized algorithm for encrypting and decrypting data which is known as AES (Advanced Encryption Standard). Information security is becoming very essential in data storage and transmission with the rapid growth of digital data exchange in an electronic way Cryptography play a vital role in information security system against different attacks which uses algorithms to scramble data into unreadable text which is only decrypted by those who has the associated key. It is of two types one for Symmetric and Asymmetric. Symmetric system has 288 bit block 128 bit commotional AES algorithm for 288 bit using 6×6 matrixes after implementation these points system is throughput at both sites encryption and decryption.

Keywords:
(Advanced Encryption Standard), United State, AES, Information security, Cryptography.


References:

1.       Lee, NIST Special Publication 800-21, Guideline for Implementing Cryptography in the Federal Government, National Institute of Standards and Technology, November.
2.       Advanced Encryption Standard (AES), Federal Information Processing Standards Publication 197, November 26, 2001.

3.       Amish Kumar , Mrs. Namita Tiwari,”Efficient implementation and avalanche effect of AES” International Journal of Security, Privacy and Trust Management (IJSPTM), Vol. 1, No 3/4, August 2012.

4.       Chih-Pin Su, Tsung-Fu Lin, Chih-Tsun Huang, and Cheng-Wen Wu, National Tsing Hua University,”A high throughput low cost AES processor” IEEE Communications Magazine 63-804/03 © 2003 IEEE.

5.       Chong Hee Kim,”Improved Differential Fault Analysis on AES Key Schedule” IEEE Transaction on Information Forensics and Security, Vol. 7, No. 1, Feb 2012.

6.       Diaa Salama Abdul. Elminaam, Hatem M. Abdul Kader and Mohie M. Hadhoud,” Performance Evaluation of Symmetric Encryption Algorithms on Power Consumption for Wireless Devices” International Journal of Computer Theory and Engineering, Vol. 1, No. 4, October, 2009.

7.       Irbid, Jordan, “A new approach for complex encrypting and decrypting data” International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.2, March
2013.

8.       J. Nechvatal, et. al., Report on the Development of the Advanced Encryption Standard (AES), National Institute of Standards and Technology, October 2, 2000.

9.       Mohan H.S and A Raji Reddy,”Performance analysis of AES and MARS encryption algorithm” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.

10.    Navraj Khatri, Rajeev Dhanda , Jagtar Singh ,”Comparison of power consumption and strict avalanche criteria at encryption/Decryption side of Different AES standards‟‟International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 4, August 2012.

11.    Xinmiao Zhang and Keshab K. Parhi,”Implementation approaches for the advanced encryption standard algorithm”, IEEE Transactions 1531-636X/12©2002IEEE.

 

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4.

Authors:

Vikash Kumar, Sanjay Sharma

Paper Title:

Lossless Image Compression through Huffman Coding Technique and Its Application in Image Processing using MATLAB

Abstract: Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. . In this paper we try to answer the following question. Which entropy coding, Huffman, is more suitable compared to other from the compression ratio, performance, and implementation points of view? We have implemented and tested Huffman algorithms. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio.

Keywords:
Lossless Compression, PSNR, Compression-Ratio, Encoding Technique, Huffman Coding, JPEG2000, JPEG-LS, JPEG


References:

1.    N. Parvatham and Seetharaman Gopalakrishnan, 2012 Third International Conference on Intelligent Systems Modelling and Simulation “A Novel Architecture for an Efficient Implementation of Image compression using 2D-DWT”
2.    Giridhar Mandyam, Nasir Ahmed, Neeraj Magotra, “Lossless Image compression using Discrete Cosine Transform”, Journal of Visual Communication and Image Representation ,Vol.8, No.1, March, pp.21-26, 1997, Article no. VC970323.

3.    Donapati, S. Yagain “A Comparative Study of Effects of CSC on Image Compression Ratios While Using JPEG-XR”, Year of Publication (2013), pp. 158-161.

4.    J. Wang, “Shot Cut Detection Based On The Statistical Parameter Histogram With The Discrete Walsh Transform”, Second International Conference on MultiMedia and Information Technology, (2010).

5.    J. Ziv and A. Lempel, “A Universal Algorithm for Sequential Data Compression”, IEEE Transactions on Information Theory, May 1977

6.    Dr. T. Bhaskara Reddy, Miss.Hema suresh yaragunti, Dr.S.kiran, Mrs.T.Anuradha “ A novel approach of lossless image compression using hashing and Huffman coding “,International Journal of Engineering research and technology ,vol.2 issue 3,march-2013.

7.    G.C Chang Y.D Lin (2010) “An Efficient Lossless ECG Compression Method Using Delta Coding and Optimal Selective Huffman Coding” IFMBE proceedings 2010, Volume 31, Part 6, 1327-1330, DOI: 10.1007/978-3-642-14515-5_338.

 

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5.

Authors:

Kanos Matyokurehwa, Nehemiah Mavetera, Osden Jokonya

Paper Title:

Requirements Engineering Techniques: A Systematic Literature Review

Abstract:  Requirements engineering is a torrid task to requirements engineers because requirements keep changing and this affect the project’s delivery schedule and cost. Although various authors proposed numerous techniques to be used in requirements engineering, software projects still fail. The issue now lies on which technique to use to minimize project failures. The aim of the study was to identify gaps in requirements engineering techniques used. The paper used a systematic literature review of requirements engineering techniques used from January 2000 to July 2016. The study found out that a lot of techniques are used in requirements engineering and some of the techniques used are not adequately addressing the problem space but the solution space. The study identified some gaps in requirements engineering techniques that need further research in order to solve those gaps.

Keywords:
Requirements Engineering, Project Failure, Techniques, Changing Requirements, Technique limitations.


References:

1.       Aguilar Calderón, J.A., Garrigós Fernández, I. and Mazón López, J.N., 2016. Requirements Engineering in the Development Process of Web Systems: A Systematic Literature Review.
2.       Hull, E., Jackson, K. and Dick, J., 2010. Requirements engineering. Springer Science & Business Media.

3.       Wang, X., Bettini, C., Brodsky, A., Jajoida, S.: Logical Design for Temporal Databases with Olaronke, G.E., Olaleke, J.O. and Olajide, M.S., 2010. A Survey on Requirement Analysis in the Nigerian Context.

4.       Batra, M and Bhatnagar, A, 2015, Descriptive Literature Review of Requirements Engineering Models. International Journal of advanced Research in Computer Science and Software Engi-neering (Volume 5, Issue 2, pp. 289-293).

5.       Clancy, T., 2014. The Standish Group CHAOS Report. Project Smart.

6.       Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J. and Linkman, S., 2009. Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1), pp.7-15.

7.       Jiang, L., Eberlein, A., Far, B.H. and Mousavi, M., 2008. A methodology for the selection of requirements engineering techniques. Software & Systems Modeling, 7(3), pp.303-328.

8.       Nuseibeh, B. and Easterbrook, S., 2000, May. Requirements engineering: a roadmap. In Pro-ceedings of the Conference on the Future of Software Engineering (pp. 35-46). ACM.

9.       Neill, C.J. and Laplante, P.A., 2003. Requirements engineering: the state of the practice. IEEE software, 20(6), p.40.

10.    Paetsch, F., Eberlein, A. and Maurer, F., 2003, June. Requirements Engineering and Agile Software Development. In WETICE (Vol. 3, p. 308).

11.    Gomes–andrigo, A., Pettersson, A. and Gorschek–tony, T., Market-Driven Requirements En-gineering Process Model, version 1.0.

12.    Van Lamsweerde, A., 2001. Goal-oriented requirements engineering: A guided tour. In Re-quirements Engineering, 2001. Proceedings. Fifth IEEE International Symposium on (pp. 249-262). IEEE.

13.    Darimont, R. and Lemoine, M., 2006, June. Goal-oriented Analysis of Regulations. In ReMo2V.

14.    Fowler, M., 2004. UML distilled: a brief guide to the standard object modeling language. Ad-dison-Wesley Professional.

15.    Ghezzi, C., Jazayeri, M. and Mandrioli, D., 2002. Fundamentals of software engineering. Prentice Hall PTR.

16.    Mauw, S., Reniers, M.A. and Willemse, T.A.C., 2000. Message Sequence Charts in the soft-ware engineering process. Handbook of Software Engineering and Knowledge Engineering, World Scientific Publishing Co, 1, pp.437-463.

17.    Jones, C., 2009. Software engineering best practices. McGraw-Hill, Inc..

18.    Pandey, D., Suman, U., Ramani, A.K. and AhilyaVishwavidyalaya, D., 2011. A Framework for modelling software requirements. International Journal of Computer Science, 8.

19.    Brace, W. and Cheutet, V., 2012. A framework to support requirements analysis in engineering design. Journal of Engineering Design, 23(12), pp.876-904.

20.    Hoorn, J.F. and Van der Veer, G.C., 2003. Requirements analysis and task design in a dynamic environment. Human-centred computing: Cognitive, social, and ergonomic aspects, 3, pp.472-476.

21.    Brinkkemper, J. and Solvberg, A., 2000. Tropos: A framework for requirements-driven soft-ware development. Information systems engineering: state of the art and research themes, p.11.

22.    Bleistein, S.J., Cox, K., Verner, J. and Phalp, K.T., 2006. B-SCP: A requirements analysis framework for validating strategic alignment of organizational IT based on strategy, context, and process. Information and software technology, 48(9), pp.846-868.

23.    Ali, R., Dalpiaz, F. and Giorgini, P., 2010. A goal-based framework for contextual requirements modeling and analysis. Requirements Engineering, 15(4), pp.439-458.

24.    Robinson, W.N., 2006. A requirements monitoring framework for enterprise systems. Re-quirements engineering, 11(1), pp.17-41.

25.    Yu, E. and Liu, L., 2001. Modelling trust for system design using the i* strategic actors framework. In Trust in Cyber-societies (pp. 175-194). Springer Berlin Heidelberg.

26.    Tung, Y.W. and Chan, K.C., 2009. A Unified Human–Computer Interaction Requirements Analysis Framework for Complex Socio-technical Systems. International Journal of Hu-man-Computer Interaction, 26(1), pp.1-21.

27.    Uszok, A., Bradshaw, J.M., Lott, J., Johnson, M., Breedy, M., Vignati, M., Whittaker, K., Jakubowski, K., Bowcock, J. and Apgard, D., 2011, November. Toward a flexible ontolo-gy-based policy approach for network operations using the KAoS framework. In 2011-MILCOM 2011 Military Communications Conference (pp. 1108-1114). IEEE.

28.    Thüm, T., Kästner, C., Benduhn, F., Meinicke, J., Saake, G. and Leich, T., 2014. FeatureIDE: An extensible framework for feature-oriented software development. Science of Computer Programming, 79, pp.70-85.

29.    Lee, S.W. and Gandhi, R.A., 2005, December. Ontology-based Active Requirements Engi-neering Framework. In APSEC (pp. 481-490).

30.    Zong-yong, L., Zhi-xue, W., Ying-ying, Y., Yue, W.U. and Ying, L.I.U., 2007, July. Towards a multiple ontology framework for requirements elicitation and reuse. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International (Vol. 1, pp. 189-195). IEEE.

31.    Génova, G., Fuentes, J.M., Llorens, J., Hurtado, O. and Moreno, V., 2013. A framework to measure and improve the quality of textual requirements. Requirements engineering, 18(1), pp.25-41.

32.    Saiedian, H., Kumarakulasingam, P. and Anan, M., 2005. Scenario-based requirements analysis techniques for real-time software systems: a comparative evaluation. Requirements Engineering, 10(1), pp.22-33.

33.    Chatzikonstantinou, G. and Kontogiannis, K., 2016. Run-time requirements verification for reconfigurable systems. Information and Software Technology, 75, pp.105-121.

34.    Martins, L.E.G. and Gorschek, T., 2016. Requirements engineering for safety-critical systems: A systematic literature review. Information and Software Technology, 75, pp.71-89.

35.    MITRE, 2016 June 10, Systems Engineering Guide.[Online]. Available.  https://www.mitre.org/publications/systems-engineering-guide/se-lifecycle-building-blocks/requirements-engineering/eliciting-collecting-and-developing-requirements 

36.    Outsource2india, 2016 June 10, Software Development.[Online]. Available. https://www.outsource2india.com/software/SoftwareProjectFailure.asp

37.    Sofia, 2010, Software Development Process- activities and steps. [Online]. Available. http://www.uacg.bg/filebank/acadstaff/userfiles/publ_bg_397_SDP_activities_and_steps.pdf

38.    Chua, B.B. and Verner, J., 2010. Examining requirements change rework effort: A study. arXiv preprint arXiv:1007.5126.

39.    Ghosh, S.M., Sharma, H.R. and Mohabay, V., 2011. Study of Impact Analysis of Software Requirement Change in SAP ERP. International Journal of Advanced Science and Technology, 33, pp.95-100.

40.    Korban,S,  2013, How to Prevent the Negative Impacts of Poor Requirements.  [Online]. Available. https://www.batimes.com/articles/how-to-prevent-the-negative-impacts-of-poor-requirements.html

41.    Bachmann, F., Bass, L., Chastek, G., Donohoe, P. and Peruzzi, F., 2000. The architecture based design method (No. CMU/SEI-00-TR-001). CARNEGIE-MELLON UNIV
PITTSBURGH PA SOFTWARE ENGINEERING INST.

42.    Suryn, W., Abran, A. and April, A., 2003. ISO/IEC SQuaRE. the second generation of stand-ards for software product quality.

43.    Mead, N.R. and Hough, E.D., 2006, April. Security requirements engineering for software systems: Case studies in support of software engineering education. In 19th Conference on Software Engineering Education & Training (CSEET’06) (pp. 149-158). IEEE.

44.    Aranda, J., Easterbrook, S. and Wilson, G., 2007, October. Requirements in the wild: How small companies do it. In 15th IEEE International Requirements Engineering Conference (RE 2007) (pp. 39-48). IEEE.

45.    Pacheco, C. and Garcia, I., 2012. A systematic literature review of stakeholder identification methods in requirements elicitation. Journal of Systems and Software, 85(9), pp.2171-2181.

46.    Fitzgerald, B., 2012. Software crisis 2.0.

47.    Zowghi, D., Firesmith, D.G. and Henderson-Sellers, B., 2005. Using the OPEN process framework to produce a situation-specific requirements engineering method. Proceedings of SREP, 5, pp.29-30.

48.    Beecham, S., Hall, T. and Rainer, A., 2005. Defining a requirements process improvement model. Software Quality Journal, 13(3), pp.247-279.

49.    Hull, E., Jackson, K. and Dick, J., 2002. DOORS: a tool to manage requirements. In Require-ments engineering (pp. 187-204). Springer London.

50.    Damian, D.E. and Zowghi, D., 2003. RE challenges in multi-site software development or-ganisations. Requirements engineering, 8(3), pp.149-160.

51.    Cant, T., McCarthy, J. and Stanley, R., 2006. Tools for Requirements Management: a Com-parison of Telelogic DOORS and the HIVE (No. DSTO-GD-0466). DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION SALISBURY (AUSTRALIA) INFO SCIENCES LAB.

52.    Lu, C.W., Chang, C.H., Chu, W.C., Cheng, Y.W. and Chang, H.C., 2008, July. A requirement tool to support model-based requirement engineering. In 2008 32nd Annual IEEE International Computer Software and Applications Conference (pp. 712-717). IEEE.

53.    Stal, M,. 2012, IRQA – A Requirements Definition and Management Solution for Systems Engineering Projects. https://www.infoq.com/news/2012/01/irqa

54.    Delor, E., Darimont, R. and Rifaut, A., 2003, December. Software quality starts with the mod-elling of goal-oriented requirements. In 16th International Conference Software & Systems Engineering and their Applications (pp. 1-6).

55.    Lami, G., Gnesi, S., Fabbrini, F., Fusani, M. and Trentanni, G., 2004. An automatic tool for the analysis of natural language requirements. Informe técnico, CNR Information Science and Technology Institute, Pisa, Italia, Setiembre.

56.    Wieringa, R. and Ebert, C., 2004. Guest Editors’ Introduction: RE’03–Practical Requirements Engineering Solutions. IEEE Software, 21(2), p.16.

57.    Wang, M. and Zeng, Y., 2009. Asking the right questions to elicit product requirements. In-ternational Journal of Computer Integrated Manufacturing, 22(4), pp.283-298.

58.    Ang, J.K., Leong, S.B., Lee, C.F. and Yusof, U.K., 2011, March. Requirement engineering techniques in developing expert systems. In Computers & Informatics (ISCI), 2011 IEEE Symposium on (pp. 640-645). IEEE.

59.    Adam,S., Riegel, N., Doerr,J., 2014, TORE. A Framework for Systematic Requirements De-velopment in Information Systems.  http://re-magazine.ireb.org/issues/2014-4-steady-flight/tore/

60.    Jiang, L. and Eberlein, A., 2008, March. A framework for requirements engineering process development (FRERE). In 19th Australian Conference on Software Engineering (aswec 2008) (pp. 507-516). IEEE.

61.    Kheirkhah, E. and Deraman, A., 2008, August. Important factors in selecting requirements en-gineering techniques. In 2008 International Symposium on Information Technology (Vol. 4, pp. 1-5). IEEE.6.  Ribière, M., Charlton, P.: Ontology Overview. Motorola Labs, Paris (2002). [Online]. Available: http://www.fipa.org/docs/input/f-in-00045/f-in-00045.pdf (current October 2003)

 

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6.

Authors:

N. Nachammai, R. Kayalvizhi

Paper Title:

Dragonfly Algorithm Based Fuzzy Logic Controller for Power Electronic Converter

Abstract:  Due to the time varying and switching nature of the Luo converters, their dynamic behavior becomes highly non-linear. Conventional controllers require a good knowledge of the system and accurate tuning in order to obtain the desired performances. A fuzzy logic controller neither requires a precise mathematical model of the system nor complex computations. Swarm Intelligence [SI] is a branch of evolutionary computing that inspired by the behavior of swarms in real life to search or optimizean objective function. The Dragonfly Algorithm [DA] is a global optimization technique based on swarm intelligence. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The drawback of fuzzy controller has the tendency to oscillate around the final operating point. Proper selection of the normalizing gains for the inputs avoids oscillations. Hence Dragonfly Algorithm, an optimization technique is required to tune the fuzzy parameters. An attempt has been made in this work to design, simulate and implement, fuzzy logic and DA-fuzzy logic controllers for regulating the output voltage. The performances of the Luo converter with Fuzzy and DA-Fuzzy controllers are evaluated under line and load disturbances using Matlab-Simulink based simulation and compared. Comparison clearly shows the superiority of the proposed Dragonfly Algorithm over fuzzy controller applied for the control of Luo converter.

Keywords:
 Dragonfly Algorithm, Fuzzy Logic Controller, Positive Output Elementary LUO Converter.


References:

1.       F.L.Luo and Hong Ye, Advanced DC/DC Converters, CRC Press, LLC, 2004.
2.       Tarun Kumar Bashishtha and Laxmi Srivastava, “Nature Inspired Meta-heuristic Dragonfly Algorithms For Solving Optimal Power Flow Problem”, International Journal of Electronics, Electrical and Computational System, Vol.5, Issue 5, May 2016, pp. 111-120. 

3.       Gururaghav Raman, Guru praanesh Raman, Chakkarapani Manickam and SaravanaIlango Ganesan, “Dragonfly Algorithm Based Global Maximum Power Point Tracker for Photovoltaic Systems”, Advances in Swarm Intelligence, Springer, 2016, pp. 211-219.

4.       Seyedali Mirjalili, “Dragonfly Algorithm: A New Meta-heuristic Optimization Technique for Solving Single-Objective, Discrete and Multi-Objective Problems”, Neural computing & Applications, Springer,2016,  pp. 1053-1073.

5.       R.H. Bhesdadiya, Mahesh H. Pandya, Indrajit N. Trivedi, Narottam Jangir, Pradeep Jangir and Arvind Kumar, “Price Penalty Factors Based Approach for Combined  Economic Emission Dispatch Problem Solution Using Dragonfly Algorithm”,  Proceedings of International conference on Energy Efficient Technologies for  sustainability, Nagarcoil, 2016, pp. 436-441.

6.       Mustafa Abdul Salam, Hossam M. Zawbaa, E. Emary, Kareem Kamal A. Ghany and B. Parv, “A Hybrid Dragonfly Algorithm With Extreme Learning Machine For Prediction”, Proceedings of International Symposium on innovations in Intelligent systems and applications, Sinaia, 2016, pp. 1-6.

7.       A. Hema Sekhar and Dr. A. Lakshmidevi, “Voltage Profile Improvement and Power System Losses Reduction with Multi TCSC Placement in Transmission System by Using Firing Angle Control Model With Heuristic Algorithms”, IOSR Journal of       Electrical & Electronics Engineering, Vol. 11, Issue 5, Oct 2016, pp. 10-21.

8.       Philip T.Daely  and Soo Y.Shin, “ Range Based Wireless Node Localization Using Dragonfly Algorithm”, Proceedings of Eighth International Conference on Uniquitous and Future Networks, Vienna, 2016, pp.1012-1015.

9.       S.Gomariz, F.Guinjoan, E.Vidal, L.Martinz and A.Poreda, ‘On the use of the describing function in fuzzy controller design for switching DC-DC regulators’, in Proc. IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, 2000, pp. 247-250.

 

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7.

Authors:

Silpa Rajan, Minu Lalitha Madhavu

Paper Title:

Survey on Reversible Data Hiding in Encrypted Images by Reversible Image Transformation (RIT)

Abstract:   To increase the security of the data, an image is in taken in an encrypted format. This process is followed in earlier techniques like RRBE, VRAE etc. In RIT, instead of converting it into an encrypted format, it is converted into another image.  Hence this image appears simply an anotherimage which is difficult for other users to decrypt. Using contrast – enhancement RDH method, data is then hidden in to the image. The advantage of using RDH is that there occurs no loss of data and contrast of the image is highly enhanced. Hence visual quality of the image is increased. The embedded data is extracted after which it is decrypted to recover the original data.

Keywords:
prediction error expansion, reversible data hiding, RRBE (reserving room before encryption), RIT (reversible image transformation), VRAE (vacating room after encryption).


References:

1.       SilpaRajan, MinuLalithaMadhavu, “Reversible Data Hiding by His-togram Modification for Image Contrast Enhancement ” , Internation-al Research Journal of Engineering and Technology,   vol .3 Issue 11 pp.761-766 November 2016
2.       W. Hong, T. Chen, and H. Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199–202, Apr. 2012

3.       W. Zhang, X. Hu, X. Li, and N. Yu, “Recursive histogram modification: Establishing equivalency between reversible data hiding and lossless data compres-sion,”IEEETrans.ImageProcess.,vol.22,no.7,pp.2775–2785, Jul. 2013.

4.       B.ou, X. Li, Y. Zhao, R. Ni, and Y. Shi, “Pairwise prediction-error expansionforefficientreversibledatahiding,”IEEETrans.ImageProcess.,vol. 22, no. 12, pp. 5010–5021, Dec. 2013.

5.       X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011.

6.       Ioan – CatalinDragoi, DinuClotuc ,”Local – prediction – based differ-ence expansion reversible watermarking ”, “IEEE Trans. On Image Processing, vol.23, no.4, pp 1779- 1790, April 2014 ”

7.       X. Hu, W. Zhang, X. Li, and N. Yu, “Minimum rate prediction and optimized histograms modification for reversible data hiding,” IEEE Trans. Inf. Forensics Security, vol. 10, no. 3, 653–664, Mar. 2015.

8.       2014 celebrity photo hack [Online].Available:http://en.wikipedia.org/wiki/2014_celebrity_photo_hack

9.       K. Ma, W. Zhang, X. Zhao, N. Yu, and F. Li, “Reversible data hiding in encrypted images by reserving room before encryption,” IEEE Trans. Inf. Forensics Security, vol. 8, no. 3, pp. 553–562, Mar. 2013.

10.    Z. Qian and X. Zhang, “Reversible data hiding in encrypted image with distributed source encoding,” IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 4, pp. 636–646, Apr. 2016.

11.    W. Zhang, K. Ma, and N. Yu, “Reversibility improved data hiding in encrypted images,” Signal Process., vol. 94, pp. 118–127, Jan. 2014.

12.    W. Zhang, K. Ma, and N. Yu, “Reversibility improved data hiding in encrypted images,” Signal Process., vol. 94, pp. 118–127, Jan. 2014.

13.    X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011

14.    Y. Lee and W. Tsai, “A new secure image transmission technique via secret-fragmentvisible mosaic images by nearly reversible colour transformation,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 4, pp. 695–703, Apr. 2014.

 

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8.

Authors:

Naveen Pathak, Anand Bisen

Paper Title:

A Review on MANET using Soft Computing and Dempster-Shafer Theory

Abstract: Mobile ad hoc networks (MANETs) is an substructure-less, dynamic network include of a sets of wirelessly mobility nodes which communicate with all different without the exploit of any centralized authority. Because of its fundamental characteristics, like as wireless medium, dynamic topology, distributed cooperation. In this paper we study MANET and its characteristics, application, security goals and different types security attacks, soft computing approach and dempster-shafer theory of evidence.

Keywords:
 MANET; soft computing appproch; dempster-shafer theory of evidence;


References:

1.       PriyankaGoyal, VintiParmarand Rahul Rishi, “MANET: Vulnerabilities, Challenges, Attacks, Applications”, IJCEM, Vol.11, January 2011
2.       Aarti,  Dr. S. S. Tyagi “  Study of MANET: Characteristics, Challenges, Application and Security Attacks”  Volume 3, Issue 5, May 2013.

3.       C. R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless  Networks,” IEEE JSAC, vol. 15, pp. 1265–75, Sept. 1997 

4.       Chlamtac, I., Conti, M., and Liu, J. J.-N. Mobile ad hoc networking: imperatives and challenges. Ad Hoc Networks, 1(1), 2003, pp. 13–6

5.       HaoYang, Haiyun& Fan Ye ― Security in mobile ad-hoc networks : Challenges and solutions,‖, Pg. 38-47, Vol 11, issue 1, Feb 2004.

6.       Bin Lu and Udo W. Pooch, “Cooperative Security-Enforcement Routing in Mobile Ad Hoc Networks,” in proceedings of the 4th IEEE International Conference on Mobile and Wireless Communications Network (MWCN 2002), Stockholm, Sweden, September 2002, pp.157 – 161.

7.       Siddesh.G.K,K.N.Muralidhara,Manjula.N.Harihar,2011. Routing in Ad Hoc Wireless Networks using SoftComputing techniques and performanceevaluation using HypernetsimulatorInternational Journal of Soft Computing and Engineering (IJSCE)ISSN: 2231-2307, Volume-1, Issue-3, July 2011.

8.       Skabar and I. Cloete,2001. Discovery of financial traading rules. In Proc. Artificial Intelligence and Applications (AIA2001), pages 121–125, Marbella, Spain.

9.       Cloete and A. Skabar,2001. Feature selection for financial trading rules. In Proceedings of 13th.EuropeanSimulation Symposium:Simulation in Industry, pages 713–717, Marseille,France,

10.    Parimal Kumar Giri, Member,IACSIT,2012.A Survey on Soft Computing Techniques forMulti-Constrained QoS Routing in MANETIJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 03, ISSUE 02, MANUSCRIPT CODE: 130103.

11.    T. Kohonen,1982. Self-organized formation of topologically correctfeature maps. Biological Cybernetics, 43:59–69.

12.    Jaspal Jindal Vishal Gupta Associate Professor in ECE Deptt. M.Tech (ECE) Student P.I.E.T College Smalkha (Panipat) ,2013. International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 6, June2013 ISSN: 2277 128X,June 2013

13.    Sharad Sharma, Shakti Kumar and Brahmjit Singh,1,3Deptt. of Electronics & Communication Engineering, National Institute ofTechnology,Kurukshetra, India2Computational Intelligence (CI) Lab, IST Klawad, Yamunanagar, India2013. Routing in Wireless Mesh Networks: Two Soft Computing Based Approaches. International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol. 3, No.3, June 2013DOI: 10.5121/ijmnct.2013.3304 29.

14.    Luis Bernardo, Rodolfo Oliveira, Sérgio Gaspar, David Paulino and Paulo Pinto A Telephony Application for Manets: Voice over a MANET-Extended JXTA Virtual Overlay Network

15.    Indira N, “Establishing a secure routing in MANET using a Hybrid Intrusion Detection System”, 978-1-4799-8159-5/14/$31.00©2014 IEEE.

16.    V. G. Muralishankar, Dr. E. George Dharma PrakashRaj”Routing Protocols for MANET: A Literature Survey” ©2014, IJCSMA All Rights Reserved, www.ijcsma.com.

17.    R.RagulRavi , V.Jayanthi “A Survey of Routing Protocol in MANET” R.RagulRavi et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1984-1988.

18.    Alex Hinds, Michael Ngulube, Shaoying Zhu, and Hussain Al-Aqrabi “A Review of Routing Protocols for Mobile Ad-Hoc NETworks (MANET)” International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013.

19.    Boaz Benmoshe, Eyal Berliner. AmitDvir “Performance Monitoring Framework for Wi-Fi MANET” 2013 IEEE Wireless Communications and Networking Conference (WCNC): SERVICES & APPLICATIONS

20.    Parimal Kumar Giri, Member,IACSIT,2012.A Survey on Soft Computing Techniques forMulti-Constrained QoS Routing in MANETIJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 03, ISSUE 02, MANUSCRIPT CODE: 130103/2012.

21.    Adnan Nadeem “A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks”2012.

22.    Adnan Nadeem “A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks”2012.

23.    S. A. Ade & P. A. Tijare, “Performance Comparison of AODV, DSDV, OLSR and DSRRouting Protocols in Mobile Ad Hoc Networks”, International Journal of Information Technology and Knowledge Management, July-Dec 2010, Volume 2, No. 2, pp. 545-548

24.    B.Praveen Kumar P.ChandraSekharN.PapannaB.BharathBhushan “A SURVEY ON MANET SECURITY CHALLENGES AND ROUTING PROTOCOLS” P Chandra Sekhar et al, Int.J.Computer Technology & Applications,Vol 4 (2),248-256.

 

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9.

Authors:

Nor Azlina Abd Rahman, Vinothini Kasinathan, Rajasvaran Logeswaran, Nurwahida Faradila Taharim

Paper Title:

Edutainment for Effective Teaching and Learning of Digital Natives

Abstract:  This paper studies an effort to enhance the teaching and learning of Digital Natives (ages below 36 years old or born after the year 1980). It explores the concept and current meaning of Edutainment with a focus on a game called QR IT Seek, developed with consideration of the specific characteristics of Digital Native learners who are the future workforce of a nation. The paper endeavors to respond to the demands of the Digital Natives who are distinctly different from the previous generations. The pressure exists for teaching and delivering concepts to the younger generation due to these characteristics. Hence, it is vital for educators of higher learning to develop innovative methods of teaching tertiary education materials and rediscover the concept and application of Edutainment. The need for this study and its findings is enhanced because without attention given to the specific needs of these students at institutions of higher education today, there would be significant impact on the achievement of learning outcomes and result in long term global consequences in this borderless world.

Keywords:
Edutainment, QR-Code, QR IT Seek competition, Digital Natives, pedagogy.


References:

1.       Metin Argan, Necip Serdar Sever “Constructs and Relationships of Edutainment Applications in Marketing Classes: How Edutainment Can be Utilized to Act as a Magnet for Choosing a Course?,” Contemporary Educational Technology, 2010, 1(2). Available at:< http://www.acarindex.com/dosyalar/makale/acarindex-1423874753.pdf> [Accessed 1 April 2015] 
2.       Wessels, P.L & Steenkamp, L.P. (2009). Generation Y students: Appropriate learning styles and teaching approaches in the economic and management sciences faculty. South African Journal of Higher Education, 23(5), 1039-1058

3.       Heather Fry, Steve Ketteridge and Stephanie Marshall, 2009, A Handbook for Teaching and Learning in Higher Education Enhancing Academic Practice, 3rd Edition,
Routledge, ISBN 0-203-89141-4

4.       Walia, 2015, “Entertainment vs. Edutainment: Bollywood Movies as Pedagogical Tools,” International Research Journal of Engineering and Technology (IRJET), Vol2, Issue 1, pg 139 – 140.  Available at: < https://www.irjet.net/archives/V2/i1/Irjet-v2i130.pdf> [Accessed 12 May 2015]

5.       Anderson, D., Kisiel, J., & Storksdieck, M. (2006). Understanding teachers’ perspectives on field trips: Discovering common ground in three countries. Curator, 4(3), 365–386.

6.       Saomya Saxena, 2013, “Best Educational Websites and Games for High-School Students”, EdTechReview. Available at:< http://edtechreview.in/news/834-best-educational-websites-and-games-for-high-school-students> [Accessed 20 July 2015]

7.       Mark Griffiths, 2002, “The educational benefits of videogames,” Education and Health, Vol 20, No 3, pg 47-51. Available at:< http://sheu.org.uk/sites/sheu.org.uk/files/imagepicker/1/eh203mg.pdf> [Accessed 21 July 2015]

8.       Vinothini Kasinathan, Nor Azlina Abd Rahman and Mohamad Firdaus Che Abdul Rani “Approaching Digital Natives with QR Code Technology in Edutainment. A case study: QR Technology in APU Campus Area,” International journal of Education and Research, Vol2, Issue 4, pg 169-178. Available at: <http://www.ijern.com/journal/April-2014/16.pdf> [Accessed 1 April 2015]

9.       MM Mubaslat, “The Effect of Using Educational Games on the Students’ Achievement in English Language for the Primary Stage”, 2012, Institute of Education Sciences. Available at: < http://files.eric.ed.gov/fulltext/ED529467.pdf> [Accessed 5 January 2016]

10.    Buckingham, D. and Scanlon, M. (2005) ‘Selling learning: towards a political economy of edutainment media,’ in Media, Culture and Society, vol. 27, no. 1. pp 41-58

 

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10.

Authors:

Shadrack Mutungi Simon, Abednego Gwaya, Stephen Diang’a

Paper Title:

Exploring the Practice of Resource Planning and Leveling (RP&L) Among Contractors in the Kenyan Construction Industry

Abstract: The performance of construction projects depends to a great extent on how best resources are managed. Resource planning and leveling are critical aspects of resource management which need to be fully incorporated and practised in any site. Failure to manage the resources available through planning and leveling is likely to result in increased project costs, time overruns and poor quality. This assertion is supported by Tarek, (2010) who argues that proper resource planning and leveling helps resolve resource conflicts, which cause numerous challenges to the organization, such as: delay in completion of certain tasks, challenges in assigning a different resource to a certain task, inability to alter task dependencies, addition or removal of certain tasks and overall time and cost overruns of projects. He further argues that the aim of resource leveling is to increase efficiency when undertaking projects by maximizing on the resources available at hand. While it would be true to say that quite a number of authors have addressed the issue of resource management, the author feels that the subject of resource planning and leveling in the Kenyan construction industry is not well covered. This is due to a number of reasons which create a gap to be researched on. Authors such as Abeyasinghe et al., (2001); Ballard, (2000); Bandelloni et al., (1994) among others have covered different aspects of resource planning and leveling. It is however important to note that all these authors address the topic in developed countries. Some of the literature found on the topic is based on the manufacturing industry. This therefore creates the need to study the Kenyan construction industry and establish the underlying factors behind the practice of resource planning and leveling among construction industry players. The purpose of this research was to explore the practice of resource planning and leveling (RP&L) adopted by contractors within the Kenyan construction industry and the factors influencing the adoption of such techniques. This research mainly adopted a case study design where questionnaires were used to collect data from respondents. The research site was Nairobi and the target population was NCA 1-3 contractors. Random sampling was used to identify the 106 respondents. A response rate of 76% was achieved. Data obtained was analyzed using descriptive statistics, relative importance index analysis and spearman’s correlation analysis. The study concluded that: though there is a high level of usage of RP&L in the Kenyan construction industry much of which is non-structured, construction projects’ progress continue to be affected by delayed materials, lack of labour and lack of equipment at the points of need; RP&L is practised more in older contracting firms and where there is support from top management; and finally a high degree of RP is associated with reduced negative impact of construction project progress

Keywords:
 Resource Planning, Resource Leveling, Construction Project Performance.


References:

1.       Abeyasinghe, Greenwood, & Johansen. (2001). An efficient method for scheduling construction projects with resource constraints. International Journal of Project Management, 19(15), 29–45.
2.       Ala-Risku, T., & Kärkkäinen, M. (2006). Material delivery problems in construction projects: A possible solution. International Journal of Production Economics, 104(1), 19–29. http://doi.org/10.1016/j.ijpe.2004.12.027

3.       Ankrah, A. (2007). An investigation into the impact of culture on construction project performance. University of Wolverhampton.

4.       Aslani, P., Christodoulou, S., Griffis, F. H., Ellinas, G., & Chiarelli, L. (2009). Activity prioritisation under resource constraints using a utility index method. The Open Construction & Building Technology Journal, 3, 33–41.
5.       Badawiyeh, B. H. (2010). The Effect of Planning and Resource Leveling.
6.       Ballard. (2000). The last planner system of production control. University of Birmingham, UK.

7.       Bandelloni, M., Tucci, M., & Rinaldi, R. (1994). Optimal resource leveling using non-serial dynamic programming. European Journal of Operational Research, European Journal of Operational Research, 78(2), 162–177.

8.       Broadhurst, K., Holt, K., & Doherty, P. (2012). Accomplishing parental engagement in child protection practice?: A qualitative analysis of parent-professional interaction in pre-proceedings work under the Public Law Outline. Qualitative Social Work, 11(5), 517–534. http://doi.org/10.1177/1473325011401471

9.       Bryman, A. (2004). Social Research Methods (Fourth). London: Oxford university press.

10.    Bryman, A. (2008). Social Research Methods (3rd ed.). New York: Oxford university press.

11.    Bryman, A., & Bell, E. (2007). Business Research Methods. London: Oxford university press.

12.    Charoenngam, C. (2003). Planning and scheduling consideration and constraints in automated construction environment. 13th ISARC, 475–482.

13.    Chitkara. (1998). Essentialsof construction projct managemet. Newsouth publishing.

14.    Clough, R., & Sears, G. (1991). Construction Project Management. New York: John Wiley & Sons, Inc.

15.    Creswell, J. (2009). Research Design; Qualitative, Quantitative and Mixed Methods Approaches. Journal of Chemical Information and Modeling (Second, Vol. 53). London: Sage Publications. http://doi.org/10.1017/CBO9781107415324.004

16.    Cunningham, T. (2013). Factors Affecting The Cost of Building Work – An Overview. Dublin Institute of Technology, 0–21.

17.    Czaja, R., & Blair, J. (1996). Designing surveys: a guide to decisions and procedures. London: Pine Forge Press.

18.    Dubey, A. (2015). Resource Levelling for a Construction Project, 12(4), 5–11. http://doi.org/10.9790/1684-12440511

19.    Hegazy, T. (2010). Resource Leveling Vs Resource Allocation, 59–65. Retrieved from http://www.tutorialspoint.com/management_concepts/resource_leveling.htm

20.    K’Akumu, O. a. (2007). Construction statistics review for Kenya. Construction Management and Economics, 25(3), 315–326. http://doi.org/10.1080/01446190601139883

21.    Kass, M. M. A. E.-A. (2012). A construction resources management system for gaza strip building contractors, 131.

22.    Kumari, K. S., & Vikranth, J. (2012). A Study On Resource Planning In Highway Construction Projects, 2(August), 1960–1967.

23.    Lau, E., & Kong, J. J. (2006). Identification of Constraints in Construction. Projects To Improve Performance. Sustainable Development through Culture and Innovation, 655–663. Retrieved from http://www.irbnet.de/daten/iconda/CIB4451.pdf

24.    Mendoza, C. (1995). Resource Planning and Resource Allocation in the Construction Industry. University of Florida.

25.    Mugenda, O. ., & Mugenda, A. . (1999). Research methods: Quantitative and Qualitative Approaches. Nairobi: Acts Press.

26.    Naief, H. (2002). A comparative evaluation of construction and manufacturing materials management. International Journal of Project Management, 20(4), 263–270.

27.    Reddy, B. S. K., & Nagaraju, S. K. (2015). A Study on Optimization of Resources for Multiple projects by using Primavera. Journal of Engineering Science and Technology, 10(2), 235–248.

28.    Schultz, R. L., Slevin, D., & Pinto, S. K. (1987). Strategy and tactics in a process model of project management interfaces. Management Journal, 17(3), 34–46.

29.    Siboe, W. (2016). Investigating the Adequacy of Construction Planning in Kenya. Jomo Kenyatta University of Agriculture and Technology (JKUAT).

30.    Spector, P. E. (1981). Research designs, 84. http://doi.org/10.4135/9781412985673

31.    Stukhart, G. (1995). Construction materials management. New York: Marcel Dekker Inc.

32.    Tarek, H., & Wail, M. (2010). Critical path segments scheduling technique. Journal of Construction Engineering and Management, American Society for Civil Engineers (ASCE), 138(6), 786–787.

33.    Thomas, S., Skitmore, R., Lam, & Poon, A. (2004). Demotivating factors influencing the productivity of civil engineering projects. International Journal of Project Management, 136–146.

34.    United Nations Centre for Human Settlements. (1984). The Construction Industry in Developing Countries. UNCHS Habitat, 2.

 

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11.

Authors:

Jerry Chong Chean Fuh, Khalida Shajaratuddur Harun, Nor Azlina Abd Rahman, Sandra A. P Gerald

Paper Title:

MENTOR as a Learning Method for Slow Learners

Abstract:  This paper proposed a prototype of an electronic learning system for slow learning children to enable the kindergarten education to create a better learning environment for children between the ages of four to six years old. The purpose is to enable the slow learning children to learn in more effectively and independently at anytime. In general, the term ‘slow learning children’ is referring to children who tend to take longer time to understand certain information when compared to other children with similar age. To elaborate further, kids who require multiple explanations before they are able to grasp a concept. The system should help children improve their ability to be flexible and creative as well as encourage slow learning children to gain confidence in their daily life. The prototype developed after considering several elements that is suitable for slow learner that focusing more on multimedia elements which are images, sounds and interactive activities. The prototype is not just focusing on learning but also enable the teachers to share the children progress with the parents. This paper presented a workable E-learning software prototype which is MENTOR system for young age users for self-improvement and learning. The prototype has 3 users; slow learner children, tutors and parents. In other words the parents able to monitor their child progress using this MENTOR system. The technologies used to develop the prototype and advantages of MENTOR system are also highlighted.

Keywords:
component; MENTOR; slow learning children


References:

1.       Abdollah. N., Ahmah. W, Akhir. E ‘Multimedia courseware for slow learners: A preliminary analysis’ (2010) Available at: http://ieeexplore.ieee.org/xpl/article/slow+learner+teachers
2.       Ahmed Al Hamad, Norlaily Yaacob and A. Y. Al-Zoubi(2008) Integrating ‘Learning Style’ Information into Personalized e-Learning System . ENGINEERING EDUCATION,
Available at: http://www.ewh.ieee.org/soc/e/sac/itee/index.php/meem/article/viewFile/9/12

3.       Rahmah Lob Yussof and Halimah Badioze Zaman, “Usability Methodology of Multimedia Courseware (Mel-Sindd) for Down Syndrome Learner,” Proceedings of the 3rd International Malaysian Educational Technology Convention, Penang, Malaysia,2009.

4.       Lee Lay Wah, “Development of Multimedia Learning Resources for Children with Learning Disabilities in an Undergraduate Special Education Technology Course,” MEDC Vol.1.,2007.

5.       Mohamad Firdaus Che Abdul Rani, Rizawati Rohizan, Nor Azlina Abd Rahman, ‘Web-based learning tool for primary school student with Dyscalculia’, The International Conference on Information Technology and Multimedia (ICIMu 2014), 19 – 20 November

6.       Earnshaw, T. & Seargeant, A. (2005). Dealing with dyslexia and other reading difficulties. Prentice Hall

7.       J. P. Khas, Huraian Sukatan Pelajaran Pendidikan Khas Bermasalah Pembelajaran Sekolah Rendah & Menengah. Kuala Lumpur: Kementerian Pendidikan Malaysia, 2003.

8.       Norfarhana Abdollah, Wan Fatimah Wan Ahmad, Emelia Akashah Patah Akhir, Development and Usability Study of Multimedia Courseware for Slow Learners: ‘Komputer Saya’, 2012 International Conference on Computer & Information Science (ICCIS), 2012

9.       Booch, G. et al (1999) Object oriented analysis and design [online] Available from http://openpdf.com/ebook/object-oriented-analysis-and-design-in-uml-bygrady-booch-pdf.html [Accessed 21 January 2015]

10.    Msdn.microsoft.com (2005) Introduction to WPF. [online] Available from http://msdn.microsoft.com/en-us/library/aa970268.aspx  [Accessed 21 January 2015]

11.    Viescas, J.L., Hernandez, M.J,  Addison Wesley Professional, SQL Queries for Mere Mortals: A Hands-On Guide to Data Manipulation in SQL. 2nd Edition

12.    Paul Deitel, Harvey M. Deitel ,Pearson Educaton, Inc., C# 2012 for Programmers (5th Edition) (Deitel Developer Series), 2013

13.      Jared, M. (2001) ‘Microsoft C# vs. Java: A Syntactical and Functional comparison’ [online] Available from http://www.devhood.com/tutorials/tutorial_details.aspx?tutorial_id=76 [Accessed 17 January 2015]

 

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12.

Authors:

Monicah Wairimu Chonge

Paper Title:

An Investigation of the Performance of Local Contractors in Kenya

Abstract: The performance of contractors is a great determinant of their success or failure. Poor performance is linked to failure whereas good performance is linked to success. Despite of this, contractors in most industries of the world, and most especially the developing countries, have been accused of poor performance. In Kenya, the situation is not different as the performance of the contractors has been termed as poor as far as time, cost and quality is concerned. This study therefore sought to validate this accusation by finding out the level of the performance of contractors in Kenya. Thirteen performance measures as identified in the literature review were used as the scale of measure. These were: time, cost, quality, client satisfaction, health and safety, environment protection, participants’ satisfaction, community satisfaction, sustainability of the development, functionality of the development, communication, profitability and productivity. The study employed the quantitative strategy as well as the cross-sectional research design. Quantitative data was collected through the use of structured questionnaires which were administered to local contractors of category NCA 1, 2 and 3. The contractors were sampled using the stratified random sampling and the systematic random sampling techniques. The data was analyzed using the Statistical Package for Social Sciences (SPSS for windows version 20). The method used for data analysis was descriptive statistics. The analysis revealed that the local contractors are average performers when all the performance measures are used to gauge their performance. But when these performance measures are considered separately, they performed poorly on time, cost, profitability, productivity and client satisfaction. They have an average performance on health and safety, participants’ satisfaction, community satisfaction, environmental protection, sustainability, communication, quality and functionality. This study therefore concludes that local contractors in Kenya of category NCA 1, 2 and 3 can be termed as average performers rather than poor performers.

Keywords:
Contractors performance, Performance measures, Construction industry


References:

1.       Akintoye, A., & Takim, R. (2002). Performance indicators for successful construction project performance. University of Northumbria. Association of Researchers in Construction Management, 2, 545–555.
2.       Atkinson, A. R. (1999). The role of human error in construction defects. Structural Survey, 17(4), 231–236.

3.       Auma, E. (2014). Factors affecting the performance of construction projects in Kenya: A survey of low rise buildings in Nairobi Central Business District. The International Journal of Business Management.

4.       Business dictionary. (2015). The meaning and definition of performance.

5.       Chan, A. ., & Tam, C. M. (2000). Factors affecting quality of building projects in Hong Kong. International Journal of Quality and Reliability Management, 17(4/5), 423–441.

6.       Chan, D. W. M., Chan, M. M., & Kumaraswamy, M. M. (2002). Compressing construction duration: lesson learned from Hong Kong building projects. International J0urnal of Project Management, 20, 23–25.

7.       Cheung, S. O., Suen, H. C., & Cheung, K. K. W. (2004). PPMS: a Web-based construction Project Performance Monitoring System. Automation in Construction, 13(3), 361–366.

8.       Flyvbjerg, B., S., M. K., & B., S. L. (2003). Howcommon and how large are cost overruns in transportinfrastructure projects. TransportReviews, 23(1), 71–88.

9.       Hussaini, M., Syuhaida, I., & Lee, M. R. (2014). Key performance indicators (KPI) of contractor on project performance for housing construction in Malasyia. Razak School of Engineering and Advanced Technology, Kualalumpur Malasyia.

10.    Jha, K. N., & Lyer, K. C. (2006). Critical factors affecting quality performance in constructionprojects. Total Quality Management, 17(9), 1155–1170.

11.    Kibuchi, N., & Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in Kenya: a case study of construction project team participants in Nairobi.

12.    KNBS. (2015). Economic survey 2015.

13.    Kometa, S. T., & Olomolaiye, P. O. (1995). An evaluation of clients’ needs and responsibilities in the construction process. Engineering, Construction and Architectural Management, 2(1), 57–76.

14.    Kumaraswamy, M. M., & Chan, D. W. M. (1995). Determinants of construction duration. Construction Management and Economics, 13, 209–217.

15.    Kuta, J., & Nyaanga, D. M. (2014). The effect of competence of contractors on construction of substandard buildings in Kenya. Prime Journal of Social Sciences, 3(3), 637–641.

16.    Macharia, S. M. (2015). Determinants of successful completion of power projects in Kenya Power and Lightning. International Journal of Social Sciences Entrepreneurship, 1(12), 570–580.

17.    Megha, D., & Rajiv, B. (2013). A methodology for ranking of causes of delay for residential construction projects in Indian context. International Journal of Emerging Technology and Advanced Engineering, 3(3), 396–404.

18.    Muguiyi, M. W. (2012). Factors influencing performance of contractors of government funded projects in Kirinyaga county- Kenya. University of Nairobi.

19.    Nassar, K., & Hosny, O. (2013). Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors. Automation in Construction, 158
168.

20.    Navarre, & Schaan, C. (1998). Design of project management systems from top management perspective. Retrieved from http://hdl.handle.net/10393/18602

21.    Ndaiga, H. (2014). Construction industry posed for growth. Construction Business Review.

22.    Nyangilo, A. O. (2012). An assessment of the organization structure and leadership effects on construction projects’ performance in Kenya: a case study of public building projects within Nairobi region. University of Nairobi.

23.    Ogoma, G. (2014). Introduction to the national costruction authority.

24.    Pinto and Pinto, J. K. (1991). Determinants of cross functional cooperation in the project implementation process. Project Management Journal, 22(2), 13–20.

25.    Pocock, J., Hyun, C., Liu, L., & Kim, M. (1996). Relationship between Project Interaction and Performance Indicators. Journal of Construction Engineering and Management, 2(165), 165–176.

26.    Songer, A. D., & Molenaar, K. R. (1997). Project characteristics for successful public-sector design-build. Journal of Construction Engineering and Project Management, 123(1), 34 – 40.

27.    Walker, D. H. T. (1996). The contribution of the construction management team to good construction time performance-An Australian experience. Journal of Construction Procurement, 2(20), 4–18.

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13.

Authors:

Maha Abdul Ameer Kadhum

Paper Title:

Design A Program to Simulate the Active Antennas

Abstract:  In this research has been studying and analyzing some types of properties antennas normal, then been improved characteristics after conversion to efficient antennas with compared to the old characteristics of antennas and new characteristics which distinguish solving Maxwell’s equations have . Allantij showed  an antenna model improved the overall value of the proportion of the voltage wave, increase bandwidth In addition to giving him a more stable long distance. Study antenna adaptive, which is the best levels used in smart antennas and signal systems with different levels of intelligence and work simulation using (demand) to one of the levels in the system and analyze its results were used algorithm less square error rate high Astaqraratha and simplicity mathematically was a simulation of the system operation.

Keywords:
 antenna, wavelengths, antennas adaptive simulation.


References:

1.       A.Andrews, TEEE “standard for local metropolis Area network, part 16, air interface for fixed broadband Wires access system”.IEEE, std 802, 16-2004.
2.       Kenjon, “An examination of the processing comp laxity of an adaptive antenna system (AAS), for W IMAX”, IEEE, 2005, Southampton.

3.       M.Nicoli, L.Samiretro et al, “Deployment  Journal of wireless for OFDM, HAP-Based Communication international network, vo.13, no.1, Journal, 2006.

4.       L.Gadara,”Application of antenna array comunication, IEEE .Vo.85, no.8,august 1997.

5.       D.Gore,”smart antenna for broadband wireless access network”,paper application in IEEE communication magazine, nov,1999.

6.       R.sampirtro etal, “Guide lines for evaluation of radio transmission technology for IMT-2000” ,Jan,2005.

7.       Chand, L.”Smart antenna”, CRC press, jan, 2004.Technology and engineering.

8.       K.Sarah,”adaptive antenna system design and applications for next generation mobile device “, international journal of engineering and innovative technology (IJET) vo, 1,issue 2,February ,2012.

9.       Rameshaver,”advances in smart antenna “, journal of scientific and industry research, vo.64, September, 2005.

10.    Santhi,”smart antenna algorithm for WCDMA mobile communication system, journal of science and network, vo.8, no.7, July, 2008.

11.    Nayoo,”application for radiation pattern control in WCDMA network submitted for the degree of doctor of philology, department of electronic engineering queen Mary university of London ,2007.

12.    Seangwon etal,”an adaptive beam forming algorithm smart antenna engineering in protocol CDMA environment ,IEEE trans., commutation , vo.E86, no.3, march, 2003.

 

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14.

Authors:

Rucha Dilip Patil, C. M. Jadhav

Paper Title:

Autonomously-Reconfigurable Wireless Mesh Networks

Abstract: Multi-hop wireless mesh network experience link-fail due to channel interference (i/f), dynamic obstacles etc. which causes performance degradation of the network in Wireless Mesh Networks. The paper proposes “The base of Autonomously Reconfigurable Wireless Mesh Networks system is IEEE 802.11” for mr-WMN to recover autonomously when the network failure occurs & to improve the performance of network. The paper uses an autonomously network reconfiguration system (ARS) algorithm to maintain network performance that allows a multi radio WMN to own recover from local link failure. ARS generates needful changes in local radio and channel assignments in order to recover from failures by using channels and radio variability in WMN’s. Next, the system cooperatively reconfigures network setting among local mesh routers based on the generated configuration changes.

Keywords:
IEEE 802.11, multi-radio wireless mesh networks (mr-WMNs), Autonomous-Reconfigurable Network, Wireless Link Failures.


References:

1.       Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: Survey,” Comput. etw., vol. 47, no. 4, pp. 445–487, Mar. 2005.
2.       Brzezinski, G. Zussman, and E. Modiano, “Enabling distributed throughput maximization in wireless mesh networks: A partitioning approach,” in Proc. ACM MobiCom,Los Angeles, CA, Sep. 2006, pp.

3.       F. AKYILDIZ, GEORGIA INSTITUTE OF TECHNOLOGYXUDONG WANG, KIYON, INC.A “Survey on Wireless Mesh Networks”

4.       P. S. Khanagoudar “A New Autonomous System (AS) for Wireless Mesh Network”, JEITVol 2, Issue 1, july 2012.

5.       kyu-Han kim, Member, IEEE and Kang G. Shin “ Self-Reconfigurable Wireless MeshNetwork”, IEEE ACM TRANSACTION ON NETWORKING, VOL 19.NO.2, April 2011.

6.       Jensilin Mary A, “Autonomously Reconfiguring Failure in Wireless Mesh Network”,Journal of Computer Application ISSN, Vol-5, EICA 2012 Feb 10

7.       R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multi-hop wireless meshnetworks,” in Proc. ACM MobiCom, Philadelphia, PA, Sep. 2004, pp. 114–128.

8.       Raniwala and T. Chiueh, “Architecture and algorithms for an IEEE 802.11-basedmulti- channel wireless mesh network,” in Proc. IEEE IN-FOCOM, Miami, FL, Mar. 2005,vol. 3

9.       Xiao Shu, Xining Li, “Link Failure Rate and Speed of Nodes in Wireless Network”, Computingand Info. SCi. University Canada, 2008 IEEE.

10.    L.Qiu, P.Bahl,A. Rao, and L. Zhou, “Troubleshooting multi-hop wire- less networks,”in Proc. ACM SIGMETRICS, Jun. 2005, pp. 380–381.

11.    P. Kyasanur and N. Vaidya, “Capacity of multi-channel wireless net-works: Impact ofnumber of channels and interfaces,” in Proc. ACM Mobi Com, Cologne, Germany, Aug.2005, pp. 43–57.

 

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15.

Authors:

Issa Y. S. Ali, Sedat Nazlibilek

Paper Title:

Design and Performance Analysis of a Robust Power System Stabilizer for Single Machine Infinite Bus using ADRC Approach

Abstract:  Due to the rapid growing demand for electricity, power systems nowadays have become operating under continually changing in loads and operating conditions which is a major cause of instabilities and could potentially result in serious consequences. This paper presents a novel design approach by employing a robust damping control of power systems based on ‘Active Disturbance Rejection Control’ (ADRC) algorithm in order to improve system stability. The advantage of this algorithm is that it requires little information from the plant model since the relative order of open loop transfer function information is quite sufficient to design a robust controller. This makes the power system more robust against a wide range of disturbances that are commonly encountered in such systems. Here, the proposed ADRC control algorithm is developed for a synchronous machine connected to infinite bus (SMIB) through external reactance under small-disturbance condition. The effectiveness of the proposed algorithm has been verified by comparing it with an optimally tuned Conventional Power System Stabilizer (CPSS) under various loading conditions. The comparison shows that the proposed approach guarantees system stability and exhibits higher performance than CPSS which lacks robustness at some severe operating points despite being optimally tuned.

Keywords:
Active Disturbance Rejection Control (ADRC); Dynamic Analysis; Small Signal Stability; Power system stabilizer (PSS); Single Machine Infinite Bus (SMIB). 


References:

1.       K. Padiyar, Power system dynamics: BS publications, 2008.
2.       D. Mondal, A. Chakrabarti, and A. Sengupta, Power System Small Signal Stability Analysis and Control: Academic Press, 2014.

3.       Y. Peng, H. Nouri, Q. M. Zhu, and L. Cheng, “Robust controller design survey for damping low frequency oscillations in power systems,” in Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific, 2011, pp. 1-4.

4.       G. Kasilingam and J. Pasupuleti, “Coordination of PSS and PID controller for power system stability enhancement–overview,” Indian Journal of Science and Technology, vol. 8, pp. 142–151, 2015.

5.       H. P. Patel and A. T. Patel, “Performance evaluation of PSS under different loading condition,” in Communication Technologies (GCCT), 2015 Global Conference on, 2015, pp. 281-284.

6.       Jalilvand, M. D. Keshavarzi, and M. Khatibi, “Optimal tuning of PSS parameters for damping improvement using PSO algorithm,” in Power Engineering and Optimization Conference (PEOCO), 2010 4th International, 2010, pp. 1-6.

7.       S. Paul and P. Roy, “Optimal design of power system stabilizer using oppositional gravitational search algorithm,” in Non-Conventional Energy (ICONCE), 2014 1st International Conference on, 2014, pp. 282-287.

8.       H. T. Canales, F. C. Torres, and J. S. Chávez, “Tuning of power system stabilizer PSS using genetic algorithms,” in Power, Electronics and Computing (ROPEC), 2014 IEEE International Autumn Meeting on, 2014, pp. 1-6.

9.       D. Sambariya and R. Prasad, “Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 229-238, 2014.

10.    Y. Huang and W. Xue, “Active disturbance rejection control: methodology and theoretical analysis,” ISA transactions, vol. 53, pp. 963-976, 2014.

11.    P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability and control vol. 7: McGraw-hill New York, 1994.

12.    B. Sun and Z. Gao, “A DSP-based active disturbance rejection control design for a 1-kW H-bridge DC-DC power converter,” IEEE Transactions on Industrial Electronics, vol. 52, pp. 1271-1277, 2005.

13.    S. Li, J. Yang, W.-H. Chen, and X. Chen, Disturbance observer-based control: methods and applications: CRC press, 2014.

14.    Z. Gao, “Scaling and bandwidth-parameterization based controller tuning,” in Proceedings of the American control conference, 2006, pp. 4989-4996.

15.    R. Krishan and A. Verma, “Robust tuning of power system stabilizers using hybrid intelligent algorithm,” in Power and Energy Society General Meeting (PESGM), 2016, 2016, pp. 1-5.

 

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17.

Authors:

Pankaj Agarwal, Shreeya Sharma, Lavanya Gupta, B. Manideep

Paper Title:

Smart Electronic Garbage Management System-Based IOT

Abstract:  This paper aims to provide an overview of the voluntary approaches towards enhancing the design of a smart dustbin for the implementation of advanced waste management systems. In most of the places, the Municipal garbage bins are overflowing and they are not cleaned at proper time. As a result of which the consequences are severe. It includes overflow of garbage which results in land pollution, spread of diseases, also it creates unhygienic conditions for people, and ugliness to that place. There needs to be system that gives prior information of the filling of the bin that alerts the municipality so that they can clean the bin on time and safeguard the environment. To avoid all such situations we intend to propose a solution for this problem “Smart Garbage Bin”, which will alarm and inform the authorized person by buzzer and alert system when the garbage bin is about to fill. To avoid all such unhygienic circumstances we are going to implement a project based on iot called smart trash management by interfacing an trash bin with infrared sensors, lcd, buzzer, wifi modules via an arduino atmega .The current status of trash bin is depicted by sensors and automatically updates garbage level on html page with the help of a wifi module. The main objective of this paper is to propose a plan to reduce human effort and resources along with the enhancement of smart city vision and to maintain a pollution free environment around our homes and specially in public places

Keywords:
Smart Garbage Bin, Level of Garbage Detection, Wifi Module, Update Garbage level, Buzzer and Alert System, Smart City Vision.


References:

1.    L.A. Guerrero, G Ger, H William, “Solid waste management challenges for cities in developing countries”, Garbage Management, vol. 33, no. 1, pp. 220-232, January 2013.
2.    Akyildiz, X. Wang, “A survey on wireless mesh networks”, IEEE Communications Magazine, vol. 43, no. 9, pp. S23-S30, September 2005.

3.    D.M. Scott, “A two-color near infrared sensor for sorting recycled plastic waste”, Measurement Science and technology, vol. 6, pp. 156-159, 1995.

4.    Narayan Sharma, Nirman Singha, Tanmoy Dutta, “Smart Bin Implementation for Smart Cities”, International Journal of Scientific & Engineering Research, Volume 6, Issue 9, September-2015, pp. 787–791

5.    Vikrant Bhor, Pankaj Morajkar, Maheshwar Gurav, Dishant Pandya4 “Smart Garbage Management System” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTV4IS031175 Vol. 4 Issue ,03 March-2015

6.    Arkady Zaslavsky, Dimitrios Georgakopoulos ”Internet of Things: Challenges and State-of-the-art solutions in Internet-scale Sensor Information Management and Mobile Analytics” 2015 16th IEEE International Conference on Mobile Data Management

 

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18.

Authors:

V. Kanchana, M. Prabu

Paper Title:

An Implementation of Sensors based to Mitigate over Train-Elephant Conflicts

Abstract: Animal accidents caused due to train are one of the major issues these days.  “Train-Elephant Conflict” Causes difficulties for both the human and the elephants. It is very dangerous issues and this causes a vast reduction in the animal species. More over elephants are the species that are rare to see and this accident still reduces the population of elephants. Mostly at night times the forest officials and the train operator cannot so attentive due which accidents occur. In the proposed system, there is an acoustic sensor fixed at the path of elephant which would be sensed and an automatic message will be sent to the train operator, thereby minimizing the accidents occurring.

Keywords:
 Acoustics, Train-Elephant Conflict, Sensor, accidens


References:

1.       Ce´ dric Vermeulen, Philippe Lejeune, Jonathan Lisein, Prosper Sawadogo, and Philippe Bouche, “Unmanned Aerial Survey of Elephants”, PLOS ONE, Volume 8, Issue2, e54700,2013.
2.       Chinthaka Dissanayake, Ramamohanarao Kotagiri, Malka Halgamuge, Bill Moran, Peter Farrell, “Propagation Constraints in Elephant Localization Using an Acoustic Sensor Network”, ICIAfS, Page 101-105, 2012, DOI: 10.1109/ICIAFS.2012.6419889.

3.       ttp://hosuronline.com/index.php/12-elephants-diedhosur-forest-range-24-months/. Retrieved 07-11-2015

4.       www.newindianexpress.com/states/tamil_nadu/article1450085.ece. Retrieved 14-07-2015

5.       http://www.thehindu.com/news/cities/chennai/wildelephant-tramples-man-to-death-nearhosur/article5693880.ece. Retrieved 1-09-2015

6.       Lalith Seneviratne, Rossel, Gunasekera, Madanayake, Yapa and   Doluweera, “Elephant Infrasound Calls as Method for Electronic Elephant Detection”, Proc. Of the Symp. On Human-Elephant Relationships and Conflicts, 2004.

7.       Mathur, Nielsen, and Prasad, “Wildlife conservation and rail track monitoring using wireless sensor networks”, VITAE, DOI: 10.1109/VITAE.2014.6934504, 2004.

8.       Mayilvaganan and Devaki, “Elephant Vocalization Direction of Arrival Estimation for real Time Data in Forest under Acoustic Sensor Network Using Hyperbolic circular
Array”, IJRCAR, ISSN 2320-7345, 2014.

9.       Mayilvaganan and Devaki, “Elephant Localization Estimation within  Acoustic Sensor Network Based On Real Time Data”, IJCTT, Vol 17, Number 4, 2014.

10.    Nimain Palei, Bhakta Rath and Kar, “Death of Elephants Due to Railway Accidents in Odisa, India”, Gajah, Volume 38, 2103, PP 39-4.

11.    Nirmal Prince and Sugumar, “Surveillance and Tracking of Elephants Using vocal Spectral Information”, IJRET, eISSN: 2319-1163, 2014.

12.    Patrick Clemins and Michael Johnson, “Automatic Type Classification and speaker Identification of African Elephant Vocalization”, J Acoust Sco Am, Volume 117, Number 2, 2005, PP 956-63.

13.    Ritesh Joshi, “Train Accident Deaths of Leopards Panthera Pardus in Rajaji National Park: A Population in threat”, World Journal of Zoology, Volume 5, Number 3, 2010, PP. 156-161.

14.    Singh and Chalisagaonkar, “Restoration of Corridors to Facilitate the Movement of Wild Asian Elephant of Wild Asian Elephant in Rajaji-Corbett Elephant Range”, India,
2006.

15.    Vartika Anand, Shalini Shah and Sunil Kumar, “Intelligent Adaptive Filtering For Noise Cancellation”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Volume. 2, Issue 5, 2013.

16.    Vibha Tiwari, “MFCC and its applications in speaker recognition” International Journal on Emerging Technologies, Volume 1,Number 1, 2010, PP 19-22, ISSN : 0975-8364.

17.    Jashvir Chhikara and Jagbir Singh, “Noise cancellation using adaptive filter”, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.3, 2012, PP 792-795.

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Volume-7 Issue-2

 Download Abstract Book

S. No

Volume-7 Issue-2, May 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Priyanka B Karande, Rupali S Kumbhar, Priydarshanee A Pawale, Ajinkya. C. Bapat

Paper Title:

Identifying Efficient Frequency Standards of Wireless Network

Abstract:  For encouraging wireless network contineous improvement is important. this is done by comparing related protocol and resulting the efficient protocol. This paper shows the overview of IEEE 802.15.4 (x-bee), 802.11(wifi), 802.16 (wimax) and carefully observed the comparision between them on the basis of Throughput, PDR, Delay and energy through simulation on NS2s. On the basis of observed results, this paper proved the Efficient Standards among xbee wifi & wimax.

Keywords:
Throughput ,End to end delay,Power consumption,Packet delivery ratio, NS2


References:

1.    Marina Petrova, Janne Riihij arvi,Petri Mahonen and Saverio Labella WTH Aachen University, Germany, “Performance Study of IEEE802.15.4 Using Measurements and Simulations”
2.    “ZigBee Wireless Networking”, Drew Gislason (via EETimes)IEEE P802.15.4/D18,”Draft Standard: Low Rate Wireless Personal”Area Networks, Feb. 2003.

3.    Ms.Swati.V.Birje,Mr.Mahesh.S.Kumbhar,Mr.Raviraj.S.Patkar “Performance comparision of 802.11 and 802.15.4 based networks” International Journal of Advanced Research in Computer and Communication Engineering.

4.    Introduction to wifi technology, retrived on September 24, 2006 from
www.wifitechnology.com
5.    IEEE 802.16 and wimax;broadband wireless access for every one, Intel Corporation 2003, http://www.intel.com/ebuisness/pdf/wireless/intel/802.16_wimax.pdf

6.    A.Wiling,”An architecture for wireless extension of profibus,”in proc.IEEE Int.conf.Ind.Electron.(IECON’03) ,Ronoku,VA, Nov2003,

7.    Chan,H.Anthony. “ssoverview of Wireless data network standards and there implementation issues.”talk presented at the 12th ICT Cape Town(2005)

8.    Morrow, R.”Wireless network coexistence”,McGraw-Hill:New York,NY(2004).

9.    Conexaxant,single-chip WLAN Radio CX53111.New port beach,CA,2006.

10. E.Ferro and F.Potorti, “Bluetooth and Wifi wireless protocols:A survey and a comparision,”IEEE wireless communication ,vol.12,no.1,pp.12-16,feb2005

11. J.S.Lee, “performance evolution of IEEE 802.15.4 for low rate wireless sspersonal area network,”IEEE trans.consumer electron,vol.52.no.3,pp.742-749,august2006

 

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2.

Authors:

Shagufta Praveen, Umesh Chandra

Paper Title:

A Comparative Study On: Nosql, Newsql and Polygot Persistence

Abstract: After a long journey of decades, most of the leading web applications opted for non-relational database. Traditional database exist for so long but data mining application doesn’t find relational database as a right choice for it. NoSQL movement was a question mark for the future of SQL. The High Volume, rich heterogeneity and speedy velocity of data generation in entire world is responsible for the Big Data. NoSQL was introduced to us for resolving scalability issues but consistency issue after scalability moved us from NoSQL to NewSQL. This paper emphasizes about NoSQL and NewSQL and it also highlights the reason for recent arrival of Polygot Persistence. Both technologies are distinguished with the help of some parameters (Models, Properties and as per Current Scenario need).

Keywords:
 Big Data, Database, Polygot Persistence, NewSQL, NoSQL.


References:

1.       S. J. Veloso, 2015, Data Analytics Topic: Big Data [Online]. Available:http://community.mis.temple.edu/sjveloso/data-analytics-topic-big-data/
2.       U.Banerjee,21 december 2012, Technology Trend Analysis[Online].Available:https://setandbma.wordpress.com/2012/12/21/definition-of-big-data/

3.       [Online].Available:https://en.wikipedia.org/wiki/NoSQL

4.       V. Sharma and M. Dave ,SQL and NoSQL Database ,Internation Journal of advance research in computer science and software engineering,2012

5.       Jose J, Subramoni H, Miao L, Minjia Z, Jian H, Wasiur M. Memcached design on high performance RDMA capable interconnects[C]. Parallel Processing(ICPP), 2011 IEEE International Conference on:743-752.

6.       R. Hetch and S. jablonski ,NoSQL evaluation: A use case oriented survey, proceeding CSC’11 Proceedings of the 2011 International conference cloud and Service computing, 2011

7.       C. He,Survey on NoSQL Database technology ,JOURNAL OF APPLIED SCIENCE AND ENGINEERING INNOVATION,2015

8.       Grolinger et.al.,Database management in cloud environment: NoSQL and NewSQL DataStore, Journal of Cloud computing, Advances, system and application 2013.

9.       [Online].Available:http://natishalom.typepad.com/nati_shaloms_blog/2009/12/the-common-principles-behind-the-nosql-alternatives.html

10.    Pavlo and M. Asslett What’s really new with NewSQL by, SIGMOD Record,June 2016

11.    Google-Launches Cloud –spanner-A newSQL databse for enterpeise by Jankiran MSC

12.    [Online].Available:www.technopedia.com/29093/newsql

13.    [Online].Available:http://www.jamesserra.com/archive/2015/07/what-is-polyglot-persistence/

14.    [Online].Available:databasemanagement.wikia.com/wiki/Concurrency_Control

15.    ABM Moniruzzamam , New SQL:Towards Next Generation Scalable RDBMS for Online Transaction processing for Big Data Management,2016

16.    S. Praveen et.al., A literature review on evolving database, International journal of computer Application, 2017

17.    J.M. Monterio et.al ,What comes after NoSQL? NewSQL: A New Era of Challenges in DBMS Scalable Data Processing ,2016

 

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3.

Authors:

Snehal S Awasare, Pratiksha K Chavan, Shital S Patil, Ajinkya C Bapat

Paper Title:

ATS: A New Way To Deal With Security of Public Places

Abstract: With the rising concern of the security at public places it is essential to find a solution to this issue.CCTV cameras only captures the movement and we need to monitor that continuously. Therefore it is necessary to design a system which can invigilate and traced out the suspicious object in real time without any human efforts. This paper is proposing an idea to develop a system which could find a threatening object and alert the security agencies about it. The proposed system will also have provision of IoT with an effective cryptographic technique to ensure the authenticity. A technologically improved system will surely boost up the security at public places over the traditional system.

Keywords:
public place security, Image processing, IoT, Cryptography..


References:

1.       Quanfu Fan; Prasad Gabbur; SharathPankanti“ Relative attributes for large scale abandoned object detection”Computer vision(ICCV), 2013 IEEE International Conference
2.       Hemangi  R. Patil , Prof. K. S. Bhagat “ detection and Tracking of moving object: A Survey” .Department of Electronics and Telecommunications, J.T.Mahajan College of engg. North Maharashtra.

3.       Chih-Hsien, Ding-Wei Huang, Jen-ShiunChiang and zong-Jheng Wu, “ MovingObject Tracking using Symmetric Mask-Based Scheme”, 2009 IEEE FifthInternational conference on InformationAssurance and Security.

4.       Swati Thorat, ManojNagmode, “Detectionand Tracking of Moving Objects,”International Journal of Innovative Research

5.       in Advanced Engineering (IJIRAE), Volume1, Issue 1 (April 2014).

6.       Mrinali M. Bhajibhakare, Pradeep K.Deshmukh, “Detection and Tracking ofMoving Object for Surveillance System,”International Journal of Application orInnovation in Engineering & Management (IJAIEM) Volume 2, Issue 12, December2013.

7.       Ajinkya C. Bapat, S. U. Nimbhorkar,” RFID Based Object Tracking System Using Collaborative Security Protocol” IJESC Vol 4107.

8.       MamtaSood, Rajeev Sharma, ChavanDipakKumar D, “Motion Human Detection andTracking Based on Background

9.       Subtraction,” International Journal ofEngineering Inventions e-ISSN: 2278-7461,p- ISSN: 2319-6491 Volume 2, Issue 6(April 2013) PP: 34-37.

10.    Saeidbagheri-golzar, FaribaKaramisorkhechaghaei, Amir-MasudEftekhari- Moghadam,” A New Method for Video Object Tracking,” The Journal of Mathematics and Computer Science Vol 4 No. 2 (2012) 120-128.

11.    Kuihe Yang, ZhimingCai, Lingling Zhao, “Algorithm Research on Moving ObjectDetection of Surveillance Video Sequence,”

12.    Optics and Photonics Journal, 2013, 3, 308-312.

13.    Himani S. Parekh, Darshak G. Thakore,Udesang K. Jaliya, “ A Survey on ObjectDetection and Tracking Methods,”International Journal of Innovative Researchin Computer and Communication

14.    Engineering, Vol. 2, Issue 2, February 2014

15.    Priyanka S. Bhawale, Ruhi R. Kabra,“Object Detection and Motion BasedTracking of Moving Objects a Survey,”

16.    International Journal of Advance Researchin Computer Science and ManagementStudies, Volume 2, Issue 12, December2014

17.    Rajesh Kumar Tripathi, Anand Singh Jalal, CharulBhatnagar“ A framework for abandoned object detection from video surveillance” , computer vision pattern recognition,image processing and graphics,2013 fourth national confereance.

18.    Eric Jardim, Xiao Bian , Eduardo A.B. da Silva , Sergio L.Netto, Hamid Krim “ On the Detection of Abandoned Objects a moving camera using robust subspace recovery and sparse representation” 2015 IEEE International conference.

19.    Diego Ortego, Juan C. SanMiguel, and Jose M. Martinez “Long-Term Stationary Objet Detection Based On Spatio-Temporal Change Detection” IEEE Signal Processing Letters, VOL.22, NO.12,December 2015

20.    Xuli Li; Chao Zhang; Duo Zhang;”Abondoned object detection using dMMouble illumination invariant foreground masks”

21.    JinhuiLan, Yaoliang Jiang, Guoliang Fan, DongyangYu,QiZhang”Real time automatic obstacle detection method for traffic surveillance in urbon traffic”, Journal of Signal Processing System, March 2016

22.    Ajinkya C Bapat, Sonali U Nimbhorakar“ Designing RFID based object tracking system by multilevel security” IEEE WiSPNET, March 2016

23.    Ajinkya C Bapat, Sonali U Nimbhorakar “Multilevel Secure RFID based object tracking system” ICISP Procedia Computer science 78,336-341,2016

 

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4.

Authors:

R.V. Patil, Aishwarya Bhosale, Ramdas Choramale, Shiwani Tummulwar, Vaibhav Rajguru

Paper Title:

Authentication and Encryption Based Cloud Data Access Privilege with Load Balancing Technique

Abstract: Cloud computing is a booming computing branch in which consists of a virtualized set of highly scalable computing resources and provided as an internet based computing where many users upload, download and modify data with cloud users. Problems in cloud computing are sharing data in a multi users, while data preservation and privacy of identity from a non-trustable cloud is still a challenge, due to the frequent change of the members of cloud. By allowing group signature and encryption techniques, any cloud user can anonymously share data with others. The main is to provide secure multi-owner data sharing in large groups. This poses a security challenge to the data stored on the cloud. As the result, the encryption cost is reduced; storage overhead and scheme are not dependent on the number of removed users with proof and experiments

Keywords:
 Cloud, Server, Encryption, Decryption, Anonymity, Shared authority.


References:

1.    Taeho Jung, Xiang-Yang Li, Senior Member, IEEE, Zhiguo Wan, and Meng Wan, Member, IEEE, “Control Cloud Data Access Privilege and Anonymity with fully Anonymous attribute based encryption” IEEE transactions on information forensics and security, vol. 10, no. 1, January 2015
2.    Hong Liu, Student Member, IEEE, Huansheng Ning, Senior Member, IEEE, Qingxu Xiong, Member, IEEE, and Laurence T. Yang, Member, IEEE “Shared Authority Based Privacy preserving Authentication Protocol in Cloud Computing” IEEE transactions on parallel and distributed systems  VOL:PP NO:99 year 2014

3.    Suchita Khare, Abhishek Chauhan, “Balancing model based   on cloud Partitioning for public cloud” Department of Computer Science, NRIIST, Bhopal, India, 7 July 2014

4.    Mayanka Katyal, Atul Mishra, “A comparative study of Load Balancing Algorithms in Cloud Computing” volume 1 issue 2 December 2013.

5.    C. Selvakumar, G. Jeeva Rathanam, M.R. Sumalatha, “Improving Cloud Data Storage Security Using Data Partitioning Technique” Department of Information Technology MIT Campus, Anna University Chennai, Tamil Nadu, Indian, 2013 3rd IEEE International Advance Computing Conference (IACC)

6.    Karen D. Devine, Erik G. Boman, Robert T. Heaphy, Bruce A. Hendrickson, “New Challenges in Dynamic Load Balancing”

7.    SuchitaKhare, Abhishek Chauhan, “A Review on Load Balancing Model Based on Cloud Partitioning for the Public Cloud” Department of Computer Science, NRIIST, Bhopal, India, July 2014

 

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5.

Authors:

N. Dhanasekar, R. Kayalvizhi

Paper Title:

Hardware Implementation of Fuzzy Logic Controller for Triple-Lift Luo Converter

Abstract:  Positive output Luo converters are a series of new DC-DC step-up (boost) converters, which were developed from prototypes using voltage lift technique. These converters perform positive to positive DC-DC voltage increasing conversion with high power density, high efficiency and cheap topology in simple structure. They are different from other existing DC-DC step-up converters with a high output voltage and small ripples. Triple lift LUO circuit is derived from positive output elementary Luo converter by adding the lift circuit three times. Due to the time varying and switching nature of the Luo converters, their dynamic behavior becomes highly non-linear.The classical control methods employed to design the controllers for Luo converters depend on the operating point so that it is very difficult to select control parameters because of  the presence of parasitic elements, time varying loads and variable supply voltages. Conventional controllers require a good knowledge of the system and accurate tuning in order to obtain the desired performances. A Fuzzy Logic Controller (FLC) is a soft computing technique which neither requires a precise mathematical model of the system nor complex computations. Hence in this research work, design and hardware implementation of fuzzy logic controller have been carried out using TMS320C242 DSP for the Triple-lift Luo converter .The experimental results are presented and analyzed under line and load disturbances.

Keywords:
Fuzzy Logic Controller, Triple-lift Luo converter, Digital Signal Processor (DSP).


References:

1.    F.L, Luo “Positive output Luo-converter lift technique”, IEE-EPA/proceedings, 146(4), pp.415-432, July 1999
2.    F.L, Luo. “Luo converters – Voltage lift technique” Proceedings of the IEEE Power Electronics Special Conference IEEE – PESC’ 98. Fukuoka Japan, pp. 1783-1789, May 1998.

3.    R. Kayalvizhi, S.P. Natarajan, V. Kavitharajan and R.Vijayarajeswaran,“TMS320F2407 DSP Based Fuzzy Logic Controller for Negative Output Luo Re-Lift Converter: Design, Simulation and Experimental Evaluation” IEEE Proceedings of Power Electronics and Drive systems, pp. 1228-1233. Dec 2005.

4.    N.F.Nik Ismail, N. Hasim and R.Baharom, “A comparative study of proportional integral derivative controller and    fuzzy logic controller on DC/DC Buck Boost converter”, IEEE symposium on industrial Electronics and Applications(ISIEA), Langkwi, pp.149-154, Sep.2011.

5.    B.Achiammal and R.Kayalvizhi “ Hardware implementation of optimized PI controller for LUO converter”, International Journal of Applied Engineering Research(IJAER),Volume 10, no 14,pp.34899-34905,2015.

6.    Liping Guo,John Y.Hung and R.M. Nelms, “Evaluation of DSP-based PID-Fuzzy controller for DC-DC converter ”, IEEE Transaction on Industrial Electronics, Vol 56,no.6,June 2009.

 

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6.

Authors:

Parminder Singh, Amarjit Kaur

Paper Title:

Enhance Decision Tree Techniques on Mobile Environment in Data Mining

Abstract: There are several techniques that are used in data mining, each one having advantages but also disadvantages. To find out which one is most appropriate for our case, when we want to use our databases in a decision-make process we need to have information about our data business and data mining techniques. Alternatively we can try them all and find out which one is the best in our case. This research is based on the findings maximum use of mobile service. The results in this report are based on data from mobile service related. As we look at Data Mining tools, we see that there are different algorithms used for creating a decision making (or predictive analysis) system. There are algorithms for creating decision trees such as ID3 and CART along with algorithms for determining known nearest neighbor or clustering when working on classification. The goal of this research is to look at one particular decision tree algorithm called enhanced algorithm and how it can be used with data mining for mobile service. The purpose is to manipulate vast amounts of data and transform it into information that can be used to make a decision.

Keywords:
 Techniques, Advantages, Appropriate (or predictive analysis), CART, ID3, Alternatively


References:

1.    Margaret H.Dunham,”Data Mining Introductory and Advanced topic”, published by person education Delhi, India,[2004].
2.    K. Cios, W.Pedrycz, and R. Swiniarsski. Data Mining Methods for Knowledge Discovery. Boston: Kluwer Academic Publishers,[1998]

3.    Omer Adel Nassar,Dr.Nedhal A.Saiyd,”the integrating between web usage mining and data mining techniques,”5th internal conference on computer science and information technology,[2013].

4.    Shahida Sulaiman, “Data Mining Technique for Expertise Search in a Special Interest Group Knowledge Portal”, 2011 3rd Conference on Data Mining and Optimization (DM O) 28-29 June [2011].

5.    Ren Yanna, “ The Design of Algorithm for Data Mining System Used for Web Service” ,IEEE [2011] .

6.    B.N.Lakshmi,G.H Raghunandhan “A conceptual overview of data mining”,IEEE ,Proceeding of the national conference on innovation in emerging technology,pp.27-32,17&18 feb,[2011].

7.    G. Sathyadevi “application of CART algorithm hepatitis disease diagnosis”, IEEE-International Conference on recent trends in information technology, ICRTIT 2011, June 3-5,[2011].

8.    Quinlan J R,” Induction of decision tree,” Machine Learning, vol.4,no.2,pp.81-106,[1986].

9.    Shiow-yang wu, Hsiu-Hao Fan” Activity-based proactive data management in mobile environments IEEE transaction on mobile computing ,vol 9,no.3 March[2010].

 

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7.

Authors:

Abhishek Bhatt, Romil Gopani, Lukash Chaddwa, Gajanan Bherde

Paper Title:

Systematic Investment Plan Date Prediction

Abstract:  Neural networks have been used on variety of prediction problems in field of finance. Mutual funds in particular SIP (Systematic Investment Plan) have been very lucrative form of high gain investment in recent years [A3]. In the paper, we have proposed a way to maximize investors return by providing an insight on possible values of NAV thought the month in the beginning of the month so they can buy units at Low rates. We have used artificial neural network (ANN) along with resilient propagation algorithm for prediction. We want to create a system which will help an investor to gain more profit compared to another investor investing in the same SIP. The proposed system will notify the user the date on which investment to be made to maximize profit. Results of our experiment have been attached which shows good performance on HDFC TOP 200 fund (G).

Keywords:
 Systematic Investment Plan, Mutual Fund, Artificial Neural Net.


References:

1.    X. Wu, M. Fund and A. Flitman, “Forecasting Stock Performance using Intelligent Hybrid Systems”, Springerlink, 2001, pp. 4 4 7-4 56.
2.    Yunus YETISl, Halid KAPLAN2, and Mo JAMSHIDI3, Fellow IEEE Department of Electrical and Computer Engineering, University of Texas at San Antonio San Antonio, Texas, USA

3.    D. E. Rumelhart, G. E. Hinton, R. 1. Wiliams, “Learning Internal Representation by Error Propagation Parallel Distributed Processing Explorations in the Microstructures of Cognition “, McClelland J. L. (eds.), 1:318-362, MIT Press, Cambridge, 1986.

4.    Riedmiller, Martin, and Heinrich Braun. “A direct adaptive method for faster backpropagation learning: The RPROP algorithm.” Neural Networks, 1993, IEEE International Conference on. IEEE, 1993. APA

5.    http://www.livemint.com/Money/VBRowqYA6XWPQo55asrVDJ/Mutual-fund-folios-rise-a-record-14-in-fiscal-year-201516.html

6.    Data source http://www.hdfcfund.com/products/equity-growth-fund

 

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8.

Authors:

Harmandeep Kaur, Vijay Kumar Joshi

Paper Title:

To Improve Performance Response of Economic Load Dispatch by using Optimization Technique

Abstract: The power systems are grown in complexity of power demands. The focus is shifted to enhance the performance of power system, customer focus, increasing the reliability, clear power and reducing cost. Optimal system includes the economy of operation, fuel costs, system security with the aim of improving the efficiency of electric power system. The Economic load dispatch is the scheduling of power generators with respect to the load to minimize the total cost of transmission and operational costs of generating units while meeting the constraints. The objective of the ELD is to allocate the total transmission loss and total load demand among power plants while satisfying the operational constraints simultaneously. This paper presents solution for improvement of performance response of ELD by using genetic algorithm and fuzzy logic optimization approaches.

Keywords:
Economic load dispatch, optimization, fuzzy logic, genetic algorithm


References:

1.       Kumari, Rajani, Sandeep Kumar, and VivekKumar Sharma. “Fuzzified Expert System for Employability Assessment”, Procedia Computer Science, 2015.
2.       Coelho L.d.S, Mariani V.C,2009“Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units,” International Journal of Chaos, Solitons and Fractals( Elsevier) , pp 2376–2383.

3.       Liang, Zi-xiong; Duncan Glover, J., “A zoom feature for a dynamic programming solution to economic dispatch including transmission losses,” IEEE transaction on power systems, volume 7(2), pp. 544-550 , 1902.

4.       Dhillon J.S., Parti S.C. and Kothari D.P., “Multiobjective Stochastic Optimal thermal power dispatch”, ibid, pp. 136-140, 1987.

5.       Palanichamy C. and Shrikrishna K. (1991), “Simple algorithm for economic power dispatch” electrical power syst. Res, Vol.21 pp 174-153.

6.       Lee. K.Y, Bai. X and Park. Y.M, “Optimization method for Reactive power Planning by Modified Simple Genetic Algorithm” IEEE Transactions on Power Systems Vol.10, No.4, November1995                   

7.       King T.D., El-Hawary M.E. and El-Hawary F., “Optimal environmental dispatching of electric power systems via an improved hopfield neural network model.” IEEE Transactions on Power Systems, vol. 10, no. 3, pp.1559-1565, Aug. 1995. 

8.       Hess S.W., Parker D., Alms John E., Le K.D., Day J.T. and Malone M.J., “Planning System Operations to meet NOx constraints”, IEEE Computer Applications in Power, vol. 5, no.3, pp. 10-14, July 1992.

9.       Zhuang F. and Galiana F.D., “Unit Commitment by Simulated Annealing,” IEEE Transactions. on Power Systems, vol. 5, no.1, pp. 311-318,1990.                                           

10.    Hamid Bouzeboudja, Abdelkader Chaker, Ahmed Alali, Bakhta Naama., “Economic Dispatch Solution Using A Real-Coded Genetic Algorithm,” Acta Electrotechnica et Enformatica No. 4, Vol. 5, 2005              

11.    Vijayalakshmi. G.A., Rajsekaran. S, “Neural Networks, Fuzzy Logic, and Genetic Algorithms” synthesis and application                                  

12.    George. J Klir/Boyuan, “Fuzzy Sets and Fuzzy Logic”, prentice Hall of India Private Limited, New Delhi – 2000.

13.    Happ H.H., “Optimal power dispatch- a comprehensive survey”, IEEE Transactions on Power Apparatus and Systems, Vol.PAS-96,no.3, May/June1977.  

14.    Hopfield J.J. and Tank D.W., “Simple neural optimization networks: an A/D converter, signal decision network, and a linear programming circuit,” IEEE Transactions on Circuit and Systems, vol. CAS-33, pp. 533-541, May 1986.         

15.    Wood Allen.J. & Woolenberg Bruce.F. (1996), “power generation, operation and Control”, johnwilley & sons.

16.    D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.

17.    Sarat Kumar Mishra, Sudhansu Kumar Mishrab, “A Comparative Study of Solution of Economic Load Dispatch Problem in Power Systems in the Environmental Perspective”, International Conference on Intelligent Computing, Communication & Convergence (ICCC-2014), Procedia Computer Science 48 ( 2015 ) 96 – 100,

18.    ELSEVIER G. A. Bakare, Removal of overloads and Voltage problems in Electric Power Systems using Genetic Algorithm/Expert System Approaches, Shaker Verlag, Aachen Germany, 2001.   

19.    C.-L. Chiang, “Genetic-based algorithm for power economic load dispatch”, IET Gen., Transm., Distrib., vol. 1, no. 2, pp.261 -269, 2007 

20.    T. Adhinarayanan and M. Sydulu ,”A directional search genetic algorithm to the economic dispatch problem with prohibited operating zones”, Proc. IEEE/PES Transmission and Distribution Conf. Expo., pp.1 -5,2008. 

21.    GiridharKumaran, V. S. R. K. Mouly, “Using evolutionary computation to solve the economic load dispatch problem”, IEEE Transactions on power systems, Vol. 3, pp. 296-301, 2001.        

22.    J. H. Park, S. O. Yang, K. J. Mun, H. S. Lee and J. W. Jung, “An application of evolutionary computations to economic load dispatch with piecewise quadratic cost functions”, IEEE Transactions on power systems, Vol.69,pp.289-294,1998.       

23.    Ah King R. T. F. and Rughooputh H. C. S., “Elitist Multiobjective Evolutionary Algorithm for Environmental/Economic Dispatch”, IEEE Congress on Evolutionary Computation, Canberra, Australia, vol. 2, pp. 1108-1114, 2003.

 

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9.

Authors:

Vijayalakshmi.B, Amreen Atiq, Jyoti Bhadoriya, Nithya

Paper Title:

Some Studies on Energy of Triple Connected Graphs

Abstract:  The field of mathematics plays a vital role in various fields, one of the important areas in mathematics is graph theory. The concept of connectedness plays an important role in any networks.Let G be a simple graph with n vertices and m edges. The ordinary energy of a graph is defined as sum of absolute values of eigen values of its adjacency matrix. In recent times analogous of energies are being considered based on eigen values of variety of other graph matrices. In this paper we analyzed various energies of triple connected graphs and obtained bounds.

Keywords:
 energy, eigen values, triple connected graphs, incidence energy, AMS Mathematics Subject Classification (2010): 05C78


References:

1.       Paulraj Joseph J,M.K. AngelJebitha, P.chitradevi and G.Sudhana,Triple connected  Graphs,  Indian Journal of Mathematical Sciences ,Vol 8,No 1(2012) ,61-75.
2.       Gutman, The energy of a graph, Ber. Math. Stat. Sekt. Forschungsz. Graz, 103(1978),1-22.

3.       Chandrashekar Adiga, E. Sampathkumar, M.A. Sriraj, Shrikanth A. S. ,Color Energy of a Graph Proceedings of the Jangjeon Mathematical Society • January 2013.

4.       R Balakrishnan, Energy of a Graph, Proceedings of the KMA National Seminar on Graph Theory and Fuzzy Mathematics, August (2003), 28-39.

5.       Mohammadreza Jooyandeh, Dariush Kiani, Maryam Mirzakhah, Incidence energy of a graph, MATCH Commun. Math. Comput. Chem. 62 (2009) 561-572

6.       Laura buggy, Amalia culiuc, Katelyn mccall,Duy nguyen, The energy of graphs and matrices,

7.       M. Lazi_c, On the Laplacian Energy of a Graph, Czech. Math. Journal, 56 (131) (2006), 1207-1213.

8.       Gutman, et. al., On Incidence Energy of a Graph, Linear Algebra Appl. (2009) -in press.

9.       S.Meenakshi  and S. Lavanya A Survey on Energy of Graphs, Annals of Pure and Applied Mathematics, Vol. 8, No. 2, 2014, 183-191

 

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10.

Authors:

Emmanuel Thyaka Mbusi, Moses Mitau Mulwa

Paper Title:

Behavior Description of Monetary and Fiscal Policy Factors That Impact Construction Output in Kenya for the Period 2000 – 2013

Abstract: The main function of construction industry in the world is provision of physical and constructed facilities to give other activities space for taking place as seen in Hillebrandt, (2000). She further observes that, these physical and constructed facilities are referred to as construction output and are usually quantified in monetary terms. This quantification is done by Kenya National Bureau of Statistics in this country. Construction industry in Kenya mostly maintains a steady and an upward trend in its growth. Recently; 2013 and 2014, an economic survey report released by Kenya National Bureau of Statistics (KNBS) indicated that Kenya’s building and construction as having contributed 4.8% to the Gross Domestic Product (GDP). The GDP had risen from Kshs.4.73 trillion to Kshs.5.36 trillion in 2014 as Macharia, (2015) indicates. This gives a clear picture that the sector is growing, though at a little bit slow pace. Description of the behavior of monetary and fiscal policy factors in Kenya was thought of as a means of enlightening the construction sector stakeholders and players about their existence. The factors play a major role in decision making regarding construction projects anywhere in the world, but they are usually not accounted for keenly at this crucial stage of decision making. Time series data was collected from KNBS and CBK on quarterly basis for the period starting from 2000 up to 2013, for the five factors. These data showed varied behavior; some displayed upward trends while others showed a zigzag behavior. Conclusion was drawn that in Kenya, there are five monetary and fiscal policy factors that have influence on construction output and therefore policy makers, stakeholders and players in the construction sector should ensure a keen consideration of the factors during decision making stage.  This will avert the problem of many construction projects stalling and ensure steady growth of the sector. This shall therefore contribute towards achieving the much taunted two digit growth of the country’s GDP.

Keywords: construction output, fiscal policy, monetary policy, time series.

References:

1.       Bon, R. (1992). The Future of International. Pergamon Press Ltd.
2.       Businessdictionary.com. (2014.). Interest rate. Retrieved 10 11, 2015, fromBusinessdictionary.com: http://www.businessdictionary.com/definition/interest-rate.html

3.       CBK.(2013). CBK Rates. Retrieved from Interest Rates: www.centralbank.go.ke

4.       Girardi, D., & Mura, A. (2013, September).Construction and economic development: empirical evidence for the period 2000-2011. Retrieved from Universita` Di Siena 1240: http://www.econ-pol.unisi.it/quaderni/684.pdf

5.       Gransberg, D. D., Popescu, C. M., & Ryan, R. C. (2006).Construction Equipment Management For Engineers, Estimators and Owners. Boca Raton: CRC Press.

6.       Gruneberg, S. L. (1997). Construction Economics, An Introduction. New York: Palgrave.
7.       HandBook, C. (1998, March).Commercial Real Estate and Construction Lending. Retrieved 2014
8.       Hillebrandt, P. M. (2000). Economic Theory and the Construction Industry. London: Palgrave Macmillan.

9.       Investopedia. (2015, May 3). Exchange rate. Retrieved from Investopedia:http://www.investopedia.com/terms/e/exchangerate.asp

10.    Kalinga, Maehle, Powell, S., McIntyre, & Jacobs. (2003). Kenya: Selected Issues and Statistical Appendix. Washington. D.C: International Monetary Fund Publication Service.

11.    Macdonald, R. (2007). Exchange Rate Economics Theories and Evidence. New York: Routledge.

12.    Macharia, N. (2015, May 5). Overview: Kenya Construction Industry. Retrieved from The Nation Business Review: http://www.kenyanbusinessreview.com

13.    /562/construction-industry-in-kenya/

14.    Masinde, J. (2015, May 7). Central Bank holds basic lending rate as shilling takes more beating. Daily nation, p. 35.

15.    Mawdesley, M. J., & Qambar, S. (2000). System Thinking and Construction Productivity. 1st.InternationalConference on Systems thinking in Management, (pp. 414 – 419).

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Volume-7 Issue-3

 Download Abstract Book

S. No

Volume-7 Issue-3, July 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Vipul Dalal, Latesh Malik

Paper Title:

Data Clustering Approach for Automatic Text Summarization of Hindi Documents using Particle Swarm Optimization and Semantic Graph

Abstract: Automatic text summarization is a process of describing important information from given document using intelligent algorithms. A lot of methods have been proposed by researchers for summarization of English text. Automatic summarization of Indian text has received a very little attention so far. In this paper, we have proposed a data clustering approach for summarizing Hindi text using semantic graph of the document and Particle Swarm Optimization (PSO) algorithm. PSO is one of the most powerful bio-inspired algorithms used to obtain optimal solution. The subject-object-verb (SOV) triples are extracted from the document. These triples are used to construct semantic graph of the document and finally clustered into summary and non-summary groups. A classifier   is trained using PSO algorithm which is then used to obtain document summary.

Keywords:
 bio-inspired algorithms, text mining, text summarization, semantic graph, PSO, data clustering


References:

1.       LUHN, H.P., 1958. “THE AUTOMATIC CREATION OF LITERATURE ABSTRACTS”. IBM J. RES. DEVELOP., 2: 159-165.
2.       P. B. Baxendale, “Machine-made index for technical literature: an experiment,” IBM J. Res. Dev., vol. 2, pp. 354-361, 1958.

3.       Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM, 16(2):264-285.

4.       Lin, C.Y. 1999. “Training a selection function for extraction”. Proceedings of the 18th Annual International ACM Conference on Information and Knowledge Management, pp:55-62.

5.       Massih R. Amini, Nicolas Usunier, and Patrick Gallinari, “Automatic Text Summarization Based on Word-Clusters and Ranking Algorithms”, ECIR 2005, LNCS 3408, pp. 142–156, (2005).

6.       Rafeeq Al-Hashemi, “Text Summarization Extraction System (TSES) Using Extracted Keywords”, International Arab Journal of e-Technology, Vol. 1, No. 4, June, pp. 164-168, (2010).

7.       Jade Goldstein, Jaime Carbonell. “SUMMARIZATION: (1) USING MMR FOR DIVERSITY- BASED RERANKING AND (2) EVALUATING SUMMARIES”. Carnegie Group Inc.’s Tipster III Summarization Project

8.       Aysun Güran, Eren Bekar, Selim Akyokuş “A Comparison of Feature and Semantic-Based Summarization Algorithms or Turkish”. INISTA 2010, International Symposium on Innovations in Intelligent Systems and Applicaitons, 21-24June 2010, Kayseri & Cappadocia,TURKEY.

9.       Ono, K., Sumita, K., and Miike, S. (1994). “Abstract generation based on rhetorical structure extraction.” In Proceedings of Coling ’94, pages 344{348, Morristown,NJ, USA.

10.    Marcu, D. (1998a). “Improving summarization through rhetorical parsing tuning”. In Proceedings of The Sixth Workshop on Very Large Corpora, pages 206-215, pages 206,215, Montreal, Canada.

11.    Giuseppe Carenini and Jackie Chi Kit Cheung, “Extractive vs. NLG-based Abstractive Summarization of Evaluative Text: The Effect of Corpus Controversiality”.

12.    Pierre-Etienne Genest, Guy Lapalme, “Framework for Abstractive Summarization using Text-to-Text Generation”, Workshop on Monolingual Text-To-Text Generation, pages 64–73,Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 64–73,Portland, Oregon, 24 June 2011. c 2011 Association for
Computational Linguistics.

13.    Vipul Dalal, Dr. Latesh Malik.: “A Survey of Extractive & Abstractive Text Summarization”, 6th International Conference on Emerging Trends in Engineering & Tecnology (ICETET), 2013

14.    M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm based features selection for text summarization”, International Journal of Computer Science and Network Security IJCSNS, vol. 9, pp. 175-179, 2009b.

15.    M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm Based Text Summarization”,  Computer Science and Information Technology – Spring Conference, 2009. IACSITSC ’09. International Association of, 2009, pp. 145-150.

16.    Albaraa Abuobieda M. Ali, Naomie Salim, Rihab Eltayeb Ahmed, Mohammed Salem Binwahlan, Ladda Sunamali, Ahmed Hamza.: “Pseudo Genetic And Probabilistic-Based Feature Selection Method For Extractive Single Document Summarization”, Journal of Theoretical and Applied Information Technology, 15th October 2011. Vol. 32 No.1,
ISSN: 1992-8645, E-ISSN: 1817-3195.

17.    Alkesh Patel,  Tanveer Siddiqui,  U. S. Tiwary.: “A language independent approach to multilingual text summarization”, Conference RIAO2007, Pittsburgh PA, U.S.A. May 30-June 1, 2007 – Copyright C.I.D. Paris, France

18.    Naresh Kumar Nagwani, Shrish Verma.: “A Frequent Term and Semantic Similarity based Single Document Text Summarization Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 17– No.2, March 2011.

19.    Kamal Sarkar.: “Bengali Text Summarization By Sentence Extraction”

20.    Upendra Mishra, Chandra Prakash.: MAULIK: “An Effective Stemmer for Hindi Language”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397, Vol. 4 No. 05 May 2012

21.    Vishal Gupta, Gurpreet Singh Lehal.: “Preprocessing Phase of Punjabi language Text Summarization”

22.    Jurij Leskovec, Natasa Milic-Frayling, Marko Grobelnik.: “Extracting Summary Sentences Based on the Document Semantic Graph, Microsoft Research, Microsoft Corporation

23.    Regina Barzilay, Michael Elhadad.: “Using Lexical Chains for Text Summarization”, In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS’97). Madrid: ACL, 1997. 10-17.

24.    Kavita Ganesan, ChengXiang Zhai, Jiawei Han.: “Opinosis: A Graph-Based Approach to Abstractive Summarization of Highly Redundant Opinions”.

25.    Eduard Hovy and Chin-Yew Lin.: “Automated Text Summarization in SUMMARIST”, In I. Mani and M. Maybury (eds), Advances in Automated Text Summarization. MIT Press.
26.    Udo Hahn, Inderjeet Mani. : “The Challenges of Automatic Text Summarization”, IEEE Computer Society Press Los Alamitos, CA, USA, Volume 33 Issue 11, November 2000, Page 29-36  ISSN:0018-9162.
27.    Chetana Thaokar, Latesh Malik, “Test Model for Summarizing Hindi Text using Extraction Method”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013).

28.    Reddy Siva. Natural Language Processing Tools. December. 2012 URL: http://sivareddy.in/downloads

 

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2.

Authors:

Haeeder Munther Noman

Paper Title:

PCF and DCF Performances Evaluation for a Non Transition 802.11 Wireless Network using OPNET Modular

Abstract:  Wireless Local Area Networks (WLANs) take increased a percentage of acceptance as they can offer an access to independent site network among computing systems. IEEE 802.11 WLAN is the best organized wireless knowledge with probably show a key role in the wireless tele-communication networks for the next generation. Many access techniques have been utilized in Wireless Networks, mainly DCF in addition to PCF can be the essential access methods. Main features to the 802.11 WLAN technologies deal with simplicities, flexibilities, and effectiveness of cost. 802.11 standards specify Many _mechanisms of essential access: Distributed Coordination Function (DCF) and Point Coordination Function (PCF) present in the MAC layer of the OSI Protocol  stack. This paper mainly deals with a performance presented at these mechanisms from where the end to end delay, throughput and average delays.

Keywords:
Wireless LAN, IEEE 802.11, DCF, PCF, Opnet Simulator.


References:

1.       Bhaskar, B. Mallick, “Performance Evaluation of MAC Protocol for IEEE 802.11, 802.11Ext. WLAN and IEEE 802.15.4 WPAN using NS-2”, International Journal of Computer Applications, Volume 119 – No.16, June 2015.
2.       Boskovic, B. and Markovic, M. (2000). On Spread Spectrum Modulation Techniques Applied in IEEE 802.11 Wireless LAN Standard. 4, 238-241.

3.       Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots to Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Vol. 2, Issue 5 (July-Aug. 2012), PP 29-33.

4.       Sarah Shaaban, Dr. Hesham M. El Badawy, Prof. Dr. Attallah Hashad, “Performance Evaluation of the IEEE 802.11 Wireless LAN Standards,” Proceedings of World Congress on Engineering, vol. I, 2-4, 2008.

5.       J. Alonso-Zárate, C. Crespo, Ch.Skianis, L. Alonso, Ch. Verikoukis, “Distributed Point Coordination Function for IEEE 802.11 Wireless Ad hoc Networks”,  Elsevier Ad Hoc Networks Journal, October 2011, doi:10.1016/j.adhoc.2011.09.004.

6.       Moustafa A. Youssef, Arunchandar Vasan, Raymond E. Miller, “Specification and analysis of the DCF and PCF protocols in the 802.11 standard using systems of communicating machines”, 2002, ISSN:1092- 1648,pp:132 – 141.Symposium, 4, 11-14.Telecommunications Review-4, 5, 287-291.
7.       Mohammad Hussain Ali, Manal Kadhim Odah, “Simulation Study 0f 802.11b   DCF Using OPNET Simulator”, Eng. & Tech. Journal, Vol. 27, No6, 2009.
8.       N. Singha, K. Aroraa, S. Goyal, “Performance of Wireless LAN in DCF and EDCF using OPNET”, IJESM Vol.2, No.3 (2012).

9.       I.Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots To Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Volume 2, Issue 5 (July-Aug. 2012), PP 29-33.

10.    OPNET LABS, “Creating Wireless Network,” 200. 

 

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3.

Authors:

Neety Bansal, Parvinder Kaur

Paper Title:

A Survey on Soft Computing Based Approaches for Fuzzy Model Identification

Abstract: The identification of an optimized fuzzy model is one of the key issues in the field of fuzzy system modeling. This can be formulated as a search and optimisation problem and many hard computing as well as soft computing approaches are available in the literature to solve this problem. In this paper we have made an attempt to present a survey on fuzzy model identification using some soft computing techniques like ACO, BBO, BB-BC, ABC, etc.

Keywords:
 Fuzzy system, Fuzzy model identification, Soft computing, Nature inspired approaches.


References:

1.          L.A.Zadeh, “Fuzzy Sets,” Information and Control, Vol.8, pp. 338-353, 1965.
2.          John Yen and Reza Langari, “Fuzzy Logic Intelligence, Control and Information,” Prentice Hall, New Jersey, 1999.

3.          Plamen A. et al., “Identification of Evolving Fuzzy Rule-Based Models,” IEEE Transactions on Fuzzy Systems, Vol. 10, No.5, pp.667-677, 2002.

4.          P. Bhalla et al., “Soft Computing Approaches to Fuzzy System Identification: A Survey,” 3rd International Conference on Intelligent Systems and Networks (IISN-2009), February 14-16,2009.

5.          L.A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Commun. ACM, vol. 37, pp. 77-84, 1994.

6.          W.A. Farag, V.H. Quintana, and G. Lambert-Torres, “A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems,” IEEE Trans. Neural Networks, vol. 9, pp. 756-767, 1998.

7.          C.L. Karr and E.J. Gentry, “Fuzzy Control of pH using genetic algorithms,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 1, pp.46-53, 1993.

8.          J. Liska and S. Melsheimer, “Complete Design of Fuzzy Logic Systems using Genetic Algorithms,” Proceedings of International Conference on Fuzzy Systems, Orlando, FL, pp.1377-1382, 1994.

9.          T. Pal and Nikhil R. Pal, “SOGARG: A self organized genetic algorithm based rule generation scheme for fuzzy controllers,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 4, Aug. 2003.

10.       Andreas Bastian, “Identifying fuzzy models utilizing genetic programming”, Fuzzy sets and systems– Elsevier, 2000.

11.       M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance”, IEEE transactions on Fuzzy Systems, vol. 8, 2000.

12.       Haralambos Sarimveis, George Bafas, “Fuzzy model predictive control of non-linear processes using genetic algorithms”, Fuzzy sets and systems– Elsevier, Oct. 2002.

13.       Eghbal G. Mansoori, M.J. Zolghadri and S.D. Katebi, “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE
Transactions on Fuzzy Systems, Vol.16, No.4, pp. 1061-1071, Aug. 2008.

14.       Z. Ning, Y S. Ong, K.W. Wong and K.T. Seow, “Parameter identification using Memetic algorithms for fuzzy systems,” Proc. of the fourth Int’l conf. on intelligent technologies (Intech’03), pp 833-839, 2003.

15.       M. Ishikawa, “Structural learning with forgetting,” Neural Networks, vol. 9, pp. 509–521, 1996.

16.       W. Duch, R. Adamczak, and K. Grabczewski, “Extraction of logical rules from neural networks,” Neural Process. Lett., vol. 7, pp. 211–219, 1998.

17.       L. Fu, “Rule generation from neural networks,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, No. 8, pp. 1114-1124, Aug. 1994.
18.       R. Setiono and W. K. Leow, “FERNN: An algorithm for fast extraction of rules from neural networks,” Appl. Intell., 2000, to be published.
19.       R. Setiono and H. Liu, “Neuro Linear: From neural networks to oblique decision rules,” Neurocomputation, vol. 17, pp. 1–24, 1997.

20.       Wen Yu and Xiaoou Li, “Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms”, IEEE Transactions On Fuzzy Systems, Vol. 12, No. 3, June 2004.

21.       G Leng, TM McGinnity, G Prasad, “An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network”, Fuzzy sets and systems- Elsevier, 2005.

22.       Rahib Hidayat Abiyev, Okyay Kaynak, “Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study”, IEEE Transactions on Industrial Electronics, Vol. 55, Issue: 8, Aug. 2008.

23.       Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.

24.       He Zhenya, Wei Chengjian, Yang Luxi, Gao Xiqi, Yao Susu, R.C. Eberhart, Yuhui Shi, “Extracting rules from fuzzy neural network by particle swarm optimisation”, Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 1998.

25.       R. Marinke ; E. Araujo ; Ld.S. Coelho ; I. Matiko, “Particle swarm optimization (PSO) applied to fuzzy modeling in a thermal-vacuum system”, Hybrid Intelligent Systems, 2005. HIS ’05.

26.       A.A.A. Esmin, A.R. Aoki, G. Lambert-Torres, “Particle swarm optimization for fuzzy membership functions optimization,” IEEE Int’l Conf. on Syst., Man and Cybern., vol. 3, Oct. 2002.

27.       Arun Khosla, Shakti Kumar, K.K.Aggarwal, Jagatpreet Singh, “Particle Swarm Optimizer for building fuzzy models,” Proceeding of one week workshop on applied soft computing SOCO-2005, Haryana Engg.College, Jagadhri, India, July 25-30, pp 43-71, 2005.

28.       Ernesto Araujo, Leandro dos S. Coelho, “Particle swarm approaches using Lozi map chaotic sequences to fuzzy modeling of an experimental thermal-vacuum system”, Applied Soft Computing- Elsevier, October 2007.

29.       Marco Dorigo and Thomas Stutzle, Ant Colony Optimization, Eastern Economy Edition, PHI, 2005.

30.       J. Casillas, O. Cordon and F. Herrera, “Learning fuzzy rules using ant colony optimization algorithms,” Proc. 2nd Int. Workshop Ant Algorithms, 2000, pp. 13-21.

31.       Shakti Kumar, “Rulebase generation using ant colony optimization,” Proc. of the one-week workshop on applied soft computing (SOCO-2006), Haryana Engg. College, Jgadhri, July 2006.

32.       Shakti K., P. Bhalla and S.Sharma, “Automatic Fuzzy Rule-base Generation for Intersystem Handover using Ant Colony Optimization Algorithm,” International Conference on Intelligent Systems and Networks (IISN-2007), Feb 23-25, 2007, MAIMT, Jagadhri, Haryana, India, pp. 764-773.

33.       Shakti K., P. Bhalla, “Fuzzy Rulebase Generation from Numerical Data using Ant Colony Optimization,” MAIMT- Journal of IT & Management. Vol.1, No.1 May – Oct. 2007, pp. 33-47.

34.       Chia-Feng Juang ; Po-Han Chang, “Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization”, IEEE Transactions on Fuzzy Systems (Vol.: 18, Issue: 1, Feb. 2010).

35.       SM Vieira, JMC Sousa, TA Runkler, “Two cooperative ant colonies for feature selection using fuzzy models”, Expert Systems with Applications, 2010 – Elsevier.

36.       D Simon. ‘Biogeography-Based Optimization’. IEEE Transaction on Evolutionary Computation, vol 12, no 6, December 2008, pp 702-713.

37.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Biogeography-based Optimization”, Journal of Institution of Engineers IE (I), vol. 90, pp. 8-13, July 2009.

38.       Yu-Jun Zheng ; Hai-Feng Ling ; Sheng-Yong Chen ; Jin-Yun Xue, “A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes”, IEEE Transactions on Fuzzy Systems ( Volume: 23, Issue: 4, Aug. 2015 ).

39.       Erol, O. K. and Eksin, I. 2006. A new optimization method: Big Bang-Big Crunch, Advances in Engineering Software. 37(2): 106-111.

40.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Big Bang-Big Crunch Optimization”, Journal of Institution of Engineers IE (I), vol. 91, pp. 18-25, January 2011.

41.       Tufan Kumbasar ; Engin Yesil ; Ibrahim Eksin ; Mujde Guzelkaya, “Inverse fuzzy Model Control with online adaptation via Big Bang-Big Crunch optimization”, Communications, Control and Signal Processing, 2008. ISCCSP 2008.

42.       S. Kumar, S.S Walia, A. Singh, “Parallel Big Bang-Big Crunch Algorithm”, International Journal of Advanced Computing, ISSN:2051-0845,Vol.46, Issue.3, Sept. 2013.

43.       Engin Yesil, “Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization”, Applied Soft Computing, Volume 15, February 2014, Pages 100–112.

44.       Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.

45.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation: An Artificial Bee Colony Approach”, 5th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN-2011), February 18 -20, 2011.

46.       Dervis Karaboga and Celal Ozturk, “Fuzzy clustering with artificial bee colony algorithm”, Scientific Research and Essays Vol. 5(14), pp. 1899-1902, 18 July, 2010.

47.       Fayssal Beloufa, M.A. Chikh, “Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm”, Computer methods and programs in biomedicine, 2013 – Elsevier.

48.       Hacene Habbi, Yassine Boudouaoui, Dervis Karaboga, Celal Ozturk, “Self-generated fuzzy systems design using artificial bee colony optimization”, Information Sciences, 2015 – Elsevier.

49.       S. Kumar, S.S. Walia, P. Kaur, “Fuzzy System Identification: A Firefly Optimisation Approach”, International Journal of Electronics and Communication Engineering (IJECE) ISSN(P): 2278-9901; ISSN(E): 2278-991X Vol. 3, Issue 6, Nov 2014.

50.       N. Susila, S. Chandramathi, Rohit Kishore, “A Fuzzy-based Firefly Algorithm for Dynamic Load Balancing in Cloud Computing Environment”, Journal Of Emerging Technologies In Web Intelligence, Vol. 6, No. 4, November 2014.

51.       Nguyen Cong Long and Phayung Meesad, “An optimal design for type–2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction”, Journal of Intelligent & Fuzzy Systems 27 (2014) 1335–1346.
52.       K. Mohana Sundaram, R. Senthil Kumar, C. Krishnakumar and K. R. Sugavanam, “Fuzzy Logic and Firefly Algorithm based Hybrid System for Energy Efficient Operation of Three Phase Induction Motor Drives”, Indian Journal of Science and Technology, Vol 9(1), January 2016.
53.       P. Kaur, S. Kumar, A.P. Singh, “Nature Inspired Approaches for Identification of Optimized Fuzzy Model: A Comparative Study”, J. of Mult.-Valued Logic & Soft Computing, Vol. 25, pp. 555-587, March 2014.

54.       Sana Bouzaida, Anis Sakly and Faouzi M’Sahli, “Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm”, International Journal of General Systems, Vol. 43, 2014.

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4.

Authors:

Amrapali Bansal, A. K. Upadhyay

Paper Title:

Microsoft Power BI

Abstract: With the changeable business circumstances, the significance of Business Intelligence has gained lots of deliberation. Business Intelligence tools can provide the standarization with a fast and persuasive decision making process based on the multiple data sources, which might be able to affect the survival of the organization on the market. And because of the changes in the extrinsic business environment and the profession needs, a new access of BI solution, Self-service BI solution, is introduced and during the last few years, the number of the market players using the approach has increased expeditiously. The objective of this paper was to build a BI solution according to one of Self-service BI solutions: Power BI presents by Microsoft, one of the leading professionals in the area of MSBI. This research contains two parts. The first part is the theory package which covers the BI and Self-service BI approaches in order to provide the readers with an overall under-standing of these concepts. It also sets up the understructure for the empirical part of this research paper project. The research paper started with analyzing BI and Self-service BI and the relationship between them. After this, the Microsoft BI solution was introduced before moving to the back-ground facts about Power BI. The second part of this research represents how to use Power BI to build a best BI solution based on the business scenario. During this testing process, the compulsory steps for building a BI solution were popularized also covering the main range of capabilities in the tool package. The consequence of this research paper was a BI solution built using Power BI and it met the requirements set for it. The observational case presented in this study can be used as a supportive user guide for all those who are concerned about Microsoft Power BI.

Keywords:
Auto-Scheduling; analytics; exploring; Integration; impactful; Intelligence; Visualization


References:

1.       https://support.office.com/en-us/article/Power-BI-Getting-Started-Guides-bd30711a-7ccf-49e8-aafa-2e8f481e675d?ui=en-US&rs=en-US&ad=US
2.       https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-getting-started/

3.       Microsoft Community : http://community.powerbi.com/

 

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5.

Authors:

Fernandes R. J, Javali F. M, Patil S. B

Paper Title:

Analysis and Design of Reinforced Concrete Beams and Columns using open STAAD

Abstract:  Structural designers especially in India use STAAD software to execute the structural analysis, but for the design purpose still manual calculations and excel spread sheets are being used. It leads to cumbersome and time consuming process to obtain analysis results from STAAD Pro to design calculations, hence to automate this process an MS Excel spread sheet has been developed. A vba program has been developed to access the analysis results from STAAD Pro to MS Excel such that the design process is fully automated which reduces manual interference.

Keywords:
 MS Excel, Open STAAD, VBA, IS 456:2000, Analysis, Design, Beam, Column.


References:

1.       Jonathan Meyer, “SCR Pile Cap Foundation Design Using STAAD v8i & Excel,” Structures Congress 2011, pp. 2485-2495, April 2012
2.       Ishwaragouda S. Patil and Dr. Satish A. Annigeri, “Introduction to PSA as a Free Structural Analysis Software,” Bonfring International Journal of Man Machine Interface, Vol. 4, Special Issue, July 2016

3.       P.Mujumdar and J. U. Maheswari, “Integrated Framework for Automating the Structural Design Iteration,” Proceedings of the International Symposium on Automation and Robotics in Construction, 2015

4.       Purva Mujumdar and Vasant Matsagar, “Design Optimization of Steel Members Using Openstaad and Genetic Algorithm,” Advances in Structural Engineering, V. Matsagar (ed.),  Springer India 2015, pp.233-244

5.       Bentley, “OpenSTAAD V8i (SELECT series 4) Reference manual,” 2012. Available: http://www.bentley.com

6.       Bentley, STAAD Pro V8i. Available: http://www.bentley.com

7.       Microsoft Excel. Visual Basic Applications for Excel. http://www.office.microsoft.com

8. Tim Burnett (2009, November), “VBA for office 2010,” Kingfisher Computer Consulting [online]. https://msdn.microsoft.com/en-us/library/office/ee814735(v=office.14).aspx#VBA Programming 101

9.       IS 456:2000, “Indian standard code of practice for plain and reinforced concrete – code of practice,” Bureau of Indian Standards, New Delhi, 2000.
10.    SP 16-1980, “Design aids for reinforced concrete to IS 456-1978,” Bureau of Indian Standards, New Delhi, 1980.
11.    SP 24-1983, “Explanatory Handbook on Indian standard code of practice for plain and reinforced concrete (IS 456-1978),” Bureau of Indian Standards, New Delhi, 1983.

12.    IS 875(Part 1)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.

13.    IS 875(Part 2)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.

14.    Guy Hart-Davis, “Mastering VBA,” 2nd edition, WILEY dreamtech, 2006.

15.    Website:http://www.civilnstructural.com/soft-tools/(OpenSTAAD learning videos).

16.    Website: https://www.excelcampus.com/ (MS Excel VBA coding_ language).

 

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6.

Authors:

Chander Shekhar Devra

Paper Title:

Issues and Challenges with Product Life Cycle Management (PLM) System Implementation Guidelines

Abstract: Deployment of PLM System is today’s need for all commercial organizations. For successful implementation of PLM system solution, Commercial organisation adopt various available implementation guidelines. Sometime available implementation guidelines may results into un-successful implementation/ re-implementation. Each Unsuccessful / re-implementation leads to waste of time, money & efforts. There is a need for analysis of current available implementation guidelines with bit detailing in real PLM implementation project in Indian context specifically process manufacturing industry. Paper will provide reliable guideline for successful PLM implementation specific to Indian Process manufacturing Industries. It will reduce the failure rate of PLM implementation. It will provide faster PLM implementation. It will save cost & efforts for implementation.

Keywords:
PLM System, successful, specifically process manufacturing industry.


References:

1.       Stark, J. (2004) Product Lifecycle Management: 21st century Paradigm for Product Realisation, Springer-Verlag, New York.
2.       Mattias B. (2012) „Evaluating PLM Implementations Using a Guidelines-based Approach‟ thesis for the degree of licentiate of engineering, Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.

3.       Pikosz, P., Malmström, J. and Malmqvist, J. (1997) „Strategies for introducing PDM systems in engineering companies‟, Advances in Concurrent Engineering – CE97, 20–22 August, Rochester Hills, MI, USA, pp.425–434

4.       Rangan, R., Rohde, S., Peak, R., Chadha, B. and Bliznakov, P. (2005) „Streamlining product lifecycle processes: a survey of product lifecycle management implementations, directions, and challenges‟, Journal of Computing and Information Science in Engineering, Vol. 5, No. 3, pp.227–237

5.       Jennings, M. and Rangan, R. (2004) „Managing complex vehicle system simulation models for manufacturing system development‟, Journal of Computing and Information Science in Engineering, Vol. 4, No. 4, pp.372–378.

6.       Illback, J. and Sholberg, J. (2000) „Application integration in the Boeing enterprise‟, Paper presented at the Fourth International Enterprise Distributed Object Computing Conference (EDOC 2000), 25–28 September, Makuhari, Japan

7.       Chadha, B. and Welsh, J. (2000) „Architecture concepts for simulation-based acquisition  of complex systems‟, Paper presented at the 2000 Summer Computer Simulation Conference, 16–20 July, Vancouver, Canada.

8.       Grieves, M. (2006) Product Lifecycle Management: Driving the Next Generation of Lean Thinking, McGraw-Hill, New York.

9.       Brown, C. and Vessey, I. (2003) „Managing the next wave of enterprise systems: leveraging lessons from ERP‟, MIS Quarterly Executive, Vol. 2, No. 1, pp.65–77.

10.    Wognum, P. and Kerssens-van Drongelen, I. (2005) „Process and impact of product data management implementation‟, International Journal of Product Development, Vol. 2, Nos. 1–2, pp.5–23.

11.    Hartman, N. and Miller, C. (2006) „Examining industry perspectives related to legacy data and technology toolset implementation‟, Engineering Design Graphics Journal, Vol. 70, No. 3, pp.12–21.

12.    Berle, A. (2006) „PLM development and implementation at Volvo 3P, using Catia V5 and Enovia V5‟, Paper Presented at the 1st Nordic Conference on Product Lifecycle Management, 25–26 January, Gothenburg, Sweden.

13.    Zimmerman, T. (2008) „Implementing PLM across organization – for multi-disciplinary and cross-functional product development‟, PhD thesis, Chalmers University of Technology, Gothenburg, Sweden.

14.    Aras implementation methodology, best practices for implementing the Aras plm solution suite, Aras Corporation, USA , Rapid results business – driven PLM Implementation Methodology; website http://kalypso.com/rapidresults/

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7.

Authors:

Mahmoud Al-Zyood

Paper Title:

Forecast Car Accident in Saudi Arabia with ARIMA Models

Abstract:  Traffic accidents are the main cause of deaths and injury in Saudi Arabia, this work is a challenge to examine the best ARIMA model for forecast a car accident. Results show that an appropriate model is simply an ARIMA (1, 0, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag2 which is an indication of the said model. The forecasted car accident cases from 1998 to 2016. The selected model with least AIC value will be selected. We entertained nine tentative ARMA models and Chose that model which has minimum AIC (Akaike Information Criterion).The chosen model is the first one AIC (-0.274306) The selected ARIMA (1, 0) (0, 0), model to forecast for the future values of our time series (car accident). Forecasted for the next 7 years with (95%) prediction intervals The prediction values of traffic accidents show that there will be increasing in deaths and injury coming years

Keywords:
 Forecasting, ARIMA models, car accident, Akaike Information Criterion (AIC), Bayessian Information Criterion (BIC).


References:

1.       Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners. Boston: Kluwer Academic.  
2.       Box, G.E. and Jenkins, G.M. (1994) Time Series Analysis: Forecasting and Control. Prentice Hall, Englewood Cliffs.

3.       Berube, M. S. (Ed.). (1985). American heritage dictionary (2nd ed.). Boston, MA: Houghton Mifflin.

4.       Box, G. E., & Jenkins, G. M. (1994). Time series analysis: Forecasting and control (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.

5.       Box, G. E., Jenkins, G. M., & Bacon, D. W. (1967). Models for forecasting seasonal and nonseasonal time series. In B. Harris (Ed.), Spectral analysis of time series. New York, NY: John Wiley & Sons.

6.       Boylan, J. (2005). Intermittent and lumpy demand: A forecasting challenge. The International Journal of Applied Forecasting, 1, 36-42. 

7.       Caldwell, J. G. (n.d.) The Box-Jenkins forecasting technique. Retrieved March 3,2012, from http://www.foundationwebsite.org/BoxJenkins.htm

8.       Cryer, J.D. and Chan, K.S. (2008) Time Series Analysis with Application in R. Springer, New York. http://dx.doi.org/10.1007/978-0-387-75959-3

9.       Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press. commandeur JJ, Bijleveld FD, Bergel-Hayat R, Antoniou C, Yannis G,

10.    Papadimitriou E. On statistical inference in time series analysis of the evolution of road safety. Accid Anal Prev. 2013; 60:424–3doi:10.1016/j.aap.2012.11.006. [PubMed: 23260716].

11.    G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis Forecasting and Control, Third ed. Englewood Cliffs, NJ: PrenticeHall, 1994.

12.    Hannan, E., (1980), The Estimation of the Order of ARMA Process, Annals of Statistics, Vol. 8,pp. 1071-1081.

 

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8.

Authors:

Manju, Rajesh Kumar

Paper Title:

Complexity of  and Its Connection with Logic

Abstract: The authors investigate the state complexity of some operations on regular languages. We prove that the upper bounds on the state complexity of these operations, which were known to be tight for larger alphabets, are tight also for binary alphabet. Upper and lower bounds for the finite-state complexity of arbitrary strings, and for strings of particular types, are given and incompressible strings are studied.

Keywords:
 Finite Automata, Formal Languages, Logic, Regular languages, State Complexity


References:

1.        J.C. Birget, Intersection and union of regular languages, and state-complexity, Inform. Process. Lett. 43 (1992) 185- 190.
2.        J.C. Birget, Partial orders on words, minimal elements of regular languages, and state-complexity, Theoret. Comput. Sci. 119 (1993) 267-291.

3.        J. Btzozowski and E. Leiss. On equations for regular languages, finite automata, and sequential networks, Theoret. Comput. Sci. 10 (1980) 19-35.

4.        A. Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133.

5.        J. Cohen, D. Penin and J.-E. Pin, On the expressive power of temporal logic, J. Comput. System Sci. 46 (1993) 271-294.

6.        J. Hopcroft and J. Ullman, Introduction to Automata, L   anguages and Computation (Addison-Wesley, Reading, MA,   19791.

7.        D. Kozen, On parallelism in Turing machines, in: Proc. Ann. Symp. on Founaiuions of Computer Science (1976) 89-97.

8.        E. Leiss, Succinct representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 ( 198 1) 323-330.

9.        E. Leiss, Succinct representation of regular languages by boolean automata, Part II, Theoret. Comput. Sci. 38 (1985)    133-136.

10.     A.R. Meyer and M.J. Fischer, Economy of description by automata, grammars, and formal systems, in: Proc. 12Th IEEE Ann. Symp. on Switching atul Automata Theory (1971) 188-191.

 

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9.

Authors:

Dragos Ionut ONESCU

Paper Title:

EU and Cyber Security

Abstract:  Securing network and information systems in the European Union is essential to ensure prosperity and to keep the online economy running. The quick and constant development of information and communication technologies, globalization, the drastic increase in data volumes and the growing number of different types of equipment connected to data networks have an impact on daily life, the economy and the functioning of the state. On the one hand, this level of ICT development will contribute to the improved availability and usability of services, enhance transparency and citizen participation in governance, and cut public as well as private sector costs. Instead, the increasing importance of technology is accompanied by an increase in the state’s growing dependence on already entrenched e-solutions, and cements the expectation of technology operating aimlessly. Social processes are also becoming increasingly dependent on a growing number of information technology resources, and in the future attention must be drawn to the fact that society at large, and each individual in particular, will be able to maintain control over the corresponding processes. The number of actors and state in cyberspace that are involved in cyber espionage targeted at computers connected to the Internet as well as closed networks continues to grow, with their aim being to collect information on both national security as well as economic interests. The amount and activeness of states capable of cyber-attacks are increasing. Meaningful and effective cooperation between the public and private sector in the development of cyber security organization as well as in preventing and resolving cyber incidents is becoming increasingly unavoidable. National defense and internal security are dependent on the private sector’s infrastructure and resources, while at the same time the state can assist vital service providers and guarantors of national critical information infrastructure as a coordinator and balancer of various interests, please download TEMPLATE HELP FILE from the website.

Keywords:
European Union; security; cyber security


References:

1.       Cyber Security Strategy was published as the two British security strategies under the direction of the Cabinet Office, the document is available at http://www.cabinetoffice.gov.uk/reports/cyber_security.aspex.
2.       Estonia hit by „Moscow cyber war”, „The Economist”, the document is available at: http://news.bbc.co.uk/2/hi/europe/6665145.stm

3.       Douglas W. Hubbard, Richard Seiersen, Patrick Cronin, How to Measure Anything in Cybersecurity Risk, Audible Studios, 2016

4.       Robert K. Knake, Pete Larkin, Richard A. Clarke, Cyber War: The Next Threat to National Security and What to Do About It, Tantor Audio, 2014

5.       George Cristian Maior,2009 strategic thinking and Uncertainty in international relations in the twenty-first century, RAO, Bucharest

6.       The National Security Strategy of the United Kingdom-2008, (5.6)
7.       http://nato.mae.ro/node/435
8.       P. W. Singer, Allan Friedman, Cybersecurity and Cyberwar: What Everyone Needs to Know®, New York Times, 2003

9.       Rid Thomas, Peter McBurney, Cyber-Weapons, The RUSI Journal, 157:1

10.    R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880.   Available: http://www.halcyon.com/pub/journals/21ps03-vidmar

 

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10.

Authors:

Rajesh Kumar, Manju

Paper Title:

Complexity of Binary and Uniary Operations on Regular Grammar 

Abstract: It appears that the state complexity of each operation has its own special features. Thus, it is important and practical to calculate good estimates for some commonly used general cases. In this paper, the author consider the state complexity of combined Boolean operations on regular language and give an exact bound for all of them in the case when the alphabet is not fixed. Moreover, the author shows that for any fixed alphabet, this bound can be reached in infinite cases.

Keywords:
 Alternating finite automaton, Automata, Combined operations, Estimation, Formal languages, Multiple operations, State complexity


References:

1.       Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133
2.       E. Leiss, Succint representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 (1981) 323- 330.

3.       G. Liu, C. Martin-Vide, A. Salomaa, S. Yu, State complexity of basic language operations combined with reversal, Information and Computation 206 (2008) 1178–1186.

4.       G. Rozenberg, A. Salomaa, Handbook of Formal Languages, Springer-Verlag, Berlin, Heidelbergm, New York, 1997

5.       J. Berstel, D. Perrin, Theory of Codes, Academic Press Inc., 1985.

6.       J. Hopcroft, J. Ullman, Introduction to Automata Theory Languages and Computation, 2nd ed., Addison-Wesley, Reading, MA, 1979.

7.       K. Salomaa, S. Yu, On the state complexity of combined operations and their estimation, International Journal of Foundations of Computer Science 18 (4) (2007) 683–698.

8.       M. Domaratzki, State complexity of proportional removals, Journal of Automata Languages and Combinatorics 7 (4) (2002) 455–468.

9.       M. Domaratzki, K. Salomaa, State complexity of shuffle on trajectories, Journal of Automata Languages and Combinatorics 9 (2–3) (2004) 217–232.

10.    S. Yu, Q. Zhuang, K. Salomaa, The state complexities of some basic operations on regular languages, Theoretical Computer Science 125 (2) (1994)315–328

11.    S. Yu, Regular Languages, In [23] Ch.1 (1997) 41–110.

12.    S. Yu, State complexity: Recent results and open problems, invited talk at International Colloquium on Automata, Languages and Programming 2004 Formal Language Workshop, also appears in  Fundamenta Informaticae 64 1–4 (2005) 471–480.

13.    S. Yu, On the state complexity of combined operations, in: invited talk at 11th International Conference on Implementation and Application of Automata, in: Lecture Notes in Computer Science, vol. 4094, Springer, 2006, pp. 11–22.   

 

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11.

Authors:

Ngo Tung Son, Tran Binh Duong, Bui Ngoc Anh, Luong Duy Hieu

Paper Title:

An Empirical Research in Autonomous Vehicles Control

Abstract:  Recent years have witnessed a growing attention to automatic-driving vehicles as this is one of the key technologies for the future industry. Even though being successful at many aspects, there has been a long interest in designing an efficient control system for automatic driving vehicles. This paper empirically demonstrates the efficiency of our system which only employs low cost camera for visual sensing. Our approach puts the focus on 2 main objectives in autonomous vehicle control: (1) lane detection and (2) speed and direction decisions for the sake of fast processing. This is to help the vehicle always moves in the right lane while keeping a suitable speed. For decision making fuzzy logic is used for effective reasoning. We test our system in mini automatic-vehicles to show that it is not only efficient but also reliable. At a practical test, the system has won third place at the Vietnam Digital Race challenge 2017.

Keywords:
Image Processing, Lane Detection, Support Vector Machine, Automatic-Car Control, Fuzzy Logic.


References:

1.       Autonomous Cars: Self-Driving the New Auto Industry Paradigm, MORGAN STANLEY RESEARCH, November 6, 2013.
2.       Overview of Autonomous Vehicle Sensors and Systems, Jaycil Z. Varghese, Professor Randy G. Boone, Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering Orlando, Florida, USA, September 10-11, 2015.

3.       Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter, Tehrani Nik Nejad Hossein, Seiichi Mita, Han Long, Intelligent Vehicles Symposium (IV), 2010 IEEE

4.       An Empirical Evaluation of Deep Learning on Highway Driving, An Empirical Evaluation  of  Deep Learning on Highway Driving, B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, et al., [arXiv], (2015).

5.       P. V. C. Hough, “Method and Means for Recognizing Complex Patterns”, US Patent 3,069,654, Ser. No. 17, 7156 Claims, 1962.

6.       “Real-Time Lane Detection for Driving System Using Image Processing”, IRJET, Volume: 02 Issue: 05, Aug-2015.

7.       Allam Shehata Hassanein , Sherien Mohammad, Mohamed Sameer, and Mohammad Ehab Ragab, “A Survey on Hough Transform, Theory, Techniques and Applications”, Informatics Department, Electronics Research Institute, El-Dokki, Giza,12622, Egypt.

8.       S.ARUNADEVI, Dr. S. DANIEL MADAN RAJA, A Survey on Image Classification Algorithm Based on Per-pixel, International Journal of Engineering Research and General Science Volume 2, Issue 6, October-November, 2014 ISSN 2091-2730.

9.       Vapnik (1995), The Nature of Statistical Learning Theory. Springer, Berlin.

10.    Jitendra Kumar, Image Classification using SVM-RBF in the field of Image Processing, International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences (IJIRMPS) Volume 1, Issue 2, December 2013.

11.    S. Agrawal, N. K. Verma, P. Tamrakar and P. Sircar, “Content Based Color Image Classification using SVM,” 2011 Eighth International Conference on Information Technology: New Generations, Las Vegas, NV, 2011, pp. 1090-1094.

12.    Timothy J. Ross “Fuzzy Logic With Engineering Applications” Second Edition.

13.    J. E. Naranjo, M. A. Sotelo, C. Gonzalez, R. Garcia and T. D. Pedro, “Using Fuzzy Logic in Automated Vehicle Control,” in IEEE Intelligent Systems, vol. 22, no. 1, pp. 36-45, Jan.-Feb. 2007.

14.    Design and Implementation of Autonomous Car using Raspberry Pi, Gurjashan Singh Pannu, Mohammad Dawud Ansar, Pritha Gupta, International Journal of Computer Applications (0975 – 8887) Volume 113 – No. 9, March 2015.

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12.

Authors:

Ashwani Kumar Aggarwal

Paper Title:

Intelligent Electronic Surveillance Systems for Personal and Team Security in Public Places

Abstract: Public security is of prime importance for establishing law and order in any society. With the advent of cheap and fast electronic systems, public security is prone to fall in the control of intruders. Whilst many electronic surveillance systems available in market claim to work effectively, their operation is questionable in crowded places where the subject under surveillance is occluded under clutter. Under such challenging task, computer vision techniques are very helpful which work on foreground segmentation of captured images to remove clutter. Further, images are preprocessed before applying many machine learning methods to log in the details of person behavior. Action recognition techniques are then used to detect unusual behavior which helps in personal security in public places.

Keywords:
Artificial intelligence, computer vision, database, descriptors, feature points, image processing, machine learning, optimization, electronic surveillance.


References:

1.       M. Watney, “Intensifying State Surveillance of Electronic Communications: A Legal Solution in Addressing Extremism or Not?” Availability, Reliability and Security (ARES), 2015 10th International Conference on, Toulouse, 2015, pp. 367-373.    
2.       D. C. Andrew, “Ground stations for analysis of electronic surveillance imagery,” Human Interfaces in Control Rooms, Cockpits and Command Centres, 1999. International Conference on, Bath, 1999, pp. 418-421.        

3.       C. Ovseník, J. Turán and A. K. Kolesárová, “Video surveillance systems with optical correlator,” MIPRO, 2011 Proceedings of the 34th International Convention, Opatija, 2011, pp. 227-230.        

4.       M. Yaghoobi, B. Mulgrew and M. E. Davies, “An efficient implementation of the low-complexity multi-coset sub-Nyquist wideband radar electronic surveillance,” Sensor Signal Processing for Defence (SSPD), 2014, Edinburgh, 2014, pp. 1-5.        

5.       J. Teng, J. Zhu, Boying Zhang, D. Xuan and Y. F. Zheng, “E-V: Efficient visual surveillance with electronic footprints,” INFOCOM, 2012 Proceedings IEEE, Orlando, FL,
2012, pp. 109-117.        

6.       G. Elkana and I. Baskara Nugraha, “Low cost embedded surveillance for public transportation,” ICT for Smart Society (ICISS), 2014 International Conference on, Bandung, 2014, pp. 242-245.        

7.       P. Pasupathy, S. Munukutla, D. P. Neikirk and S. L. Wood, “Versatile wireless sacrificial transducers for electronic structural surveillance sensors,” Sensors, 2009 IEEE, Christchurch, 2009, pp. 979-983.        

8.       Z. B. May, “Real-time alert system for home surveillance,” Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on, Penang, 2012, pp. 501-505.        

9.       V. M. López, A. Navarro-Crespín, C. Brañas, F. J. Azcondo, R. Schnell and R. Zane, “Frequency control and phase surveillance in resonant electronic ballast,” IECON 2011 – 37th Annual Conference on IEEE Industrial Electronics Society, Melbourne, VIC, 2011, pp. 2929-2934.        

10.    Gang Kang and O. P. Gandhi, “Comparison of various safety guidelines for electronic article surveillance devices with pulsed magnetic fields,” in IEEE Transactions on Biomedical Engineering, vol. 50, no. 1, pp. 107-113, Jan. 2003.        

11.    X. Pan and Y. Wu, “Modeling and simulations of ECCM of ocean surveillance satellite electronic intelligence,” Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on, Chongqing, 2012, pp. 1476-1480.        

12.    M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds, ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, Volume 179, 15 December 2012, Pages 300-314.

13.    L. Zhao, S. Huang and G. Dissanayake, “Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining,” Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, Tokyo, 2013, pp. 24-30.

14.    Z. Kang and G. Medioni, “3D Urban Reconstruction from Wide Area Aerial Surveillance Video,” Applications and Computer Vision Workshops (WACVW), 2015 IEEE Winter, Waikoloa, HI, 2015, pp. 28-35.

15.    J. Ventura and T. Höllerer, “Wide-area scene mapping for mobile visual tracking,” Mixed and Augmented Reality (ISMAR), 2012 IEEE International Symposium on, Atlanta, GA, 2012, pp. 3-12.

16.    G. Bleser, H. Wuest and D. Stricker, “Online camera pose estimation in partially known and dynamic scenes,” Mixed and Augmented Reality, 2006. ISMAR 2006. IEEE/ACM International Symposium on, Santa Barbard, CA, 2006, pp. 56-65.

17.    T. J. Cham, A. Ciptadi, W. C. Tan, M. T. Pham and L. T. Chia, “Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map,” Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, San Francisco, CA, 2010, pp. 366-373.

18.    M. Chatzigiorgaki and A. N. Skodras, “Real-time keyframe extraction towards video content identification,” Digital Signal Processing, 2009 16th International Conference on, Santorini-Hellas, 2009, pp. 1-6.

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Volume-7 Issue-4

 Download Abstract Book

S. No

Volume-7 Issue-4, September 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

E. Baloui Jamkhaneh

Paper Title:

System Reliability using Generalized Intuitionistic Fuzzy Exponential Lifetime Distribution

Abstract:  The aim of this paper is investigates the reliability characteristics of systems using generalized intuitionistic fuzzy exponential lifetime distribution, in which the lifetime parameter is assumed to be generalized intuitionistic fuzzy number. Generalized intuitionistic fuzzy reliability, generalized intuitionistic fuzzy hazard function, generalized intuitionistic fuzzy mean time to failure and their (α_1,α_2)-cut have been discussed when systems follow generalized intuitionistic fuzzy exponential lifetime distribution. Further, reliability analysis of the series and parallel systems has been done.

Keywords:
generalized intuitionistic fuzzy number (GIFN), (α_1,α_2)-cut, generalized intuitionistic fuzzy distribution, generalized intuitionistic fuzzy reliability.

References:
1.       L. A.  Zadeh, “Fuzzy sets”, Information and Control, 8(3), 1965, pp. 338-356.
2.       D. Singer, “A fuzzy set approach to fault tree and reliability analysis”, Fuzzy Sets and Systems, 34, 1990, pp. 145-155.

3.       K.Y. Cai, C.Y. Wen and M. L Zhang, “Fuzzy states as a basis for a theory of fuzzy reliability, Microelectronic Reliability, 33, 1993, pp. 2253-2263.

4.       S. M. Chen, “Fuzzy system reliability analysis using fuzzy number arithmetic operations”, Fuzzy Sets and Systems, 64, 1994, pp. 31-38.

5.       D.L. Mong, C.H. Cheng,“Fuzzy system reliability analysis for components with different membership functions”, Fuzzy Sets and Systems, 64, 1994, pp. 145–157.

6.       D. Pandey, S.K.  Tyagi, “Profust reliability of a gracefully degradable system”, Fuzzy Sets and Systems, 158, 2007, pp. 794-803.

7.       D. Pandey, S.K.  Tyagi, V. Kumar, “Failure mode screening using fuzzy set theory”, International Mathematical Forum, 4(16), 2009, pp. 779-794.

8.       E. Baloui Jamkhaneh,  “An evaluation of the systems reliability using fuzzy lifetime distribution”, Journal of Applied Mathematics, Islamic Azad University of Lahijan, 7(28), 2011, pp. 73-80.

9.       E. Baloui. Jamkhaneh, “ Analyzing system reliability using fuzzy Weibull lifetime distribution”, International Journal of Applied Operational Research, Islamic Azad University of Lahijan, 4, 2014,pp. 93-102.

10.    K.  Atanassov, “ Intuitionistic Fuzzy Sets,” Fuzzy Sets and Systems, 20, 1986, pp. 87-96.

11.    P. Burillo, H. Bustince, V. Mohedano, “Some definition of intuitionistic fuzzy number”, Fuzzy based expert systems, fuzzy Bulgarian enthusiasts, September 28-30, 1994, Sofia, Bulgaria.

12.    G. S. Mahapatra, T.K. Roy, “Reliability evaluation using triangular intuitionistic fuzzy numbers arithmetic operations”, World Academy of Science, Engineering and Technology, 50, 2009, pp. 574-580.

13.    G. S. Mahapatra, B.S. Mahapatra, “ Intuitionistic fuzzy fault tree analysis using intuitionistic fuzzy numbers”, International Mathematical Forum, 5(21), 2010, pp. 1015 – 1024.

14.    D. Pandey, S.K.  Tyagi, V. Kumar, “Reliability analysis of a series and parallel network using triangular intuitionistic fuzzy sets”, Applications and Applied Mathematics: An International Journal, 6(11), 2011, pp. 1845– 1855.

15.    M. Kumar, S.P. Yadav, S. Kumar, “A new approach for analyzing the fuzzy system reliability using intuitionistic fuzzy number”, International Journal Industrial and Systems Engineering 8(2), 2011, pp. 135–156.

16.    M. Kumar, S.P. Yadav,  “Analyzing fuzzy system reliability using arithmetic operations on different types of  intuitionistic fuzzy numbers”,  In: Deep K., Nagar A., Pant M., Bansal J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, Advances in Intelligent and Soft Computing, vol 130, Springer, India.

17.    M. K. Sharma, “Reliability analysis of a system using intuitionistic fuzzy sets”, International Journal of Soft Computing and Engineering, 2, 2012, pp. 431-440.

18.    H. Garg, M. Rani, S.P. Sharma, “Reliability Analysis of the Engineering Systems Using Intuitionistic Fuzzy Set Theory”, Journal of Quality and Reliability Engineering, vol. 2013, Article ID 943972, 10 pages, 1993, doi:10.1155/2013/943972.

19.    H. Garg, M. Rani, “An approach for reliability analysis of industrial systems using PSO and IFS technique”, ISA Transactions, 52(6), 2013, pp. 701-710. DOI:10.1016/j.isatra.2013.06.010.

20.    K. S. Bohra, S. B.  Singh, “Evaluating fuzzy system reliability using intuitionistic fuzzy Rayleigh lifetime distribution, Mathematics in Engineering”, Science and Aerospace, 6(2), 2015,  pp. 245-254 .

21.    P. Kumar, S.B. Singh, “Fuzzy system reliability using intuitionistic fuzzy Weibull lifetime distribution”, International Journal of Reliability and Applications , 16(1), 2015, pp. 15-26.

22.    P. Kumar, S.B. Singh, “Fuzzy system reliability using generalized trapezoidal intuitionistic fuzzy number with some arithmetic operations”, Nonlinear Studies, 24(1), 2017, pp. 139-157.

23.    E. Baloui Jamkhaneh, S. Nadarajah, “A new generalized intuitionistic fuzzy sets”, Hacettepe Journal of Mathematics and Statistics, 44 (6), 2015, pp. 1537 – 1551.

24.    A. Shabani, E. Baloui Jamkhaneh, “A new generalized intuitionistic fuzzy number”, Journal of Fuzzy Set Valued Analysis, 2014, pp. 1-10.

 

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2.

Authors:

Jyoti Kumawat, Mukesh Kumar Yadav

Paper Title:

Synthesis of Effect of Self Heating Effect on Electrical Characteristics in SOI MOSFET

Abstract: This article describes the use of silvaco TCAD tools to develop SOI MOSFET technology and device simulation results. The purpose of this simulation is to investigate the effect of self heating effect on the electrical properties of the device. The results show that the impact of self heating phenomenon on the Id-Vd curve. The device was manufactured using ATHENA software, and the simulation was done with the help of ATLAS software. All charts were made using Silvaco. We briefly introduce SOI MOSFETs Transistors and problems at high temperatures Self-heating effect, and then we present the simulation results get related using the SILVACO TCAD tool SOI n-MOSFET structure. We will also show some of the simulation results we have obtained The effect of temperature changes on our structure directly affect its drain current.

Keywords:
SOI technology, SOI MOSFET, Self-heating effects, Silvaco Software, Silicon-On-Insulator, MOSFET, Silvaco, ATHENA, ATLAS.


References:

1.       A.K. Goel_, T.H. Tan, High-temperature and self-heating effects in fully depleted SOI MOSFETs Microelectronics  Journal 37 (2006)     963–975.
2.       J.  P.  Colinge,  Silicon-on-Insulator  Technology:  Materials    toVLSI. Boston, MA: Kluwer, 1991.

3.       Wei Ma thesis, Linearity Analysis of Single and Double-Gate Silicon-On-Insulator Metal-Oxide-Semiconductor-Field-Effect- Transistor, Ohio University, August 2004.

4.       Alexandre    SILIGARIS    « Modélisation    grand    signal    de MOSFET  en hyperfréquences : application  à l’étude  des  non linéarités    des    filières    SOI »   Thèse    de   doctorat    2004- UNIVERSITE   DES   SCIENCE   ET   TECHNOLOGIES   DE LILLE.

5.       http://docinsa.insa-lyon.fr/these/pont.php?id=daviot

6.       G.K. Celler, S. Cristoloveanu, Journal of Applied Physics, Vol.93, no. 9, p. 4955,2003.

7.       F.S.  Shoucair,  W.  Hwang,  Electrical  characteristics  of  large scale integration (LSI) MOSFETs at very high temperatures part II: experiment, Microelectron. Reliab. 24(3) (1984) 497–510.

8.       Cristoloveanu, Sorin. “Introduction to silicon on insulator materials and

9.       devices.” Microelectronic engineering 39.1 (1997): 145-154.

10.    Lim, Hyung-Kyu, and Jerry G. Fossum. “Threshold voltage of thin-film

11.    Silicon-on-insulator (SOI) MOSFET’s.” Electron Devices, IEEE Transactions on 30.10 (1983): 1244-1251.

12.    Bergveld, P. “ISFET, theory and practice.” IEEE Sensor Conference Toronto. Vol. 10. 2003.

13.    Aydin, C., et al. “Double Gate Coupling and Quantum Tunneling in Ultrathin SOI MOSFETs.” Future Trends in Microelectronics, Wiley & Sons (2004).

14.    H. J. Jang, and W. J. Cho,  “Performance enhancement of capacitive-coupling dual-gate ion-sensitive field-effect transistor in ultra-thin-body”, Scientific Reports 4, 2014.

 

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3.

Authors:

P. Julia Grace, S. Sumathy

Paper Title:

Hardware Based Offline Licensing System

Abstract:  Software licensing allows to get paid for each copy of the software. This paper is about licensing the software for any application. Normally software can be copied by one (or) more users without the knowledge of the owner. In order to avoid the situation, this work is proposed. To make Gas Booking work easy, the application is designed using ASP.NET and SQL SERVER 2005.

Keywords:
 offline Licensing system, MAC address, Gas agency.

References:
1.    Jesse Liberty, Dan Hurwitz – Programming using .NET Technology. Complete   Reference-Wiley publications, Newyork 2006 3rd edition
2.    Lim., “New Technology in Computer Science “MSIS, University of Auckland, New Zealand, 2000. 2nd edition

3.    Randy C.Marchany, Joseph G.Tront, “Implementing projects in .Net”, Proceedings of the    Hawaii International conference on Systems, January 2002.7th edition

4.    Meyers, B.C. and Obendorf, P., (2001). Managing Software Acquisition: Open Systems and COTS Products, Addison-Wesley, New York.

5.    Arand, Kevin, Software Piracy/Copyright Issues http://cerebro.xu.edu/~arand/csci380 /papers/piracy.htm, Xavier University Computer Science. April 2002.

6.    Bertrand Anckaert, Bjorn De Sutter, Koen De Bosschere, Software Piracy Prevention through Diversity DRM’04, October 25, 2004, Washington, DC, USA. 2004 ACM 1-58113-969-1/04/0010

7.    H. Chang and M. Atallah. Protecting software code by guards. Security and Privacy in Digital Rights Management,LNC S, 2320:160–175, 2002.

8.    George Coulouris, Jean Dollimore & Tim Kindberg. Election Algorithm, Bully Algo & Ring based algo. In Distributed Systems page 445-448, 2006

9.    http://www.ubicc.org/files/pdf/DISTRIBUTED%20SOFTWARE%20AND%20LICENSE%20KEY%20MANAGEMENT%20%E2

%80%9CAN%20INITIATATIVE%20TO%20STOP%20SOFTWARE%20PIRACY_404.pdf

 

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4.

Authors:

Grace Wangari Karanja, Abednego Gwaya, Wanyona Githae

Paper Title:

A Framework for Construction Supply Chain Management in Kenya: A Case for Targeted Construction Completion Time

Abstract:   The Kenyan Construction industry is crucial for the growth of the country’s economy. According to S.D Khutale et al. 2013 output from the Kenyan construction industry is a major and integral part of the nations GDP. The Kenyan National Bureau of statistics (2014) recorded the Gross domestic Product (GDP) for years 2010, 2011 and 2012 was 4.5%, 4.3% and 4.8% respectively. The construction industry is increasingly becoming competitive hence focus is increasingly being on improving performance. Based on these observations and government reports, kimondo et al.(2015) proposed construction supply chain management as a solution to the perennial problem of failure to meet target completion time on construction projects. A survey approach covering 140 randomly selected construction sites whose companies were duly registered by NCA in Nairobi was used. The findings revealed a positive and significant relationship between CSC practices, CSC characteristics, CSC outcomes, CSCM frameworks and targeted project completion time. Based on the findings, the study concluded that construction supply chain management influences the performance of targeted project completion time for construction firms within Nairobi County. The study developed a framework and recommended that construction firms work by adopting the framework to improve on their attributes; and ensure that their outcomes are successful hence meeting the construction project targeted time. Finally, the study recommended that similar studies should be conducted in other Counties for comparison purposes.

Keywords:
Construction supply chain, Construction Supply chain Management, Construction Project, Targeted ompletion time.        


References:

1.        Benton, W.C, Jr. Linda F. McHenry, (2010) Construction Purchasing & Supply Chain Management, McGraw-Hill, New York.
2.        Briscoe, G. and dainty, A., (2005), Construction Supply Chain Integration: an Elusive Goal?. Supply Chain Management: An International Journal, 10(4), pp. 319-325.

3.        Dubois, A. and Gadde, L.-E. (2000), “Supply strategy and network effects – purchasing behaviour in the construction industry”, European Journal of Purchasing & Supply Management, Vol. 6 Nos 3-4, pp. 207-15.

4.        Economic Survey (2014) published by the Kenya National Bureau of Statistics KNBS, ISBN: 978-9966-102-00-3, pp 21.

5.        Economic Survey (2015) published by the Kenya National Bureau of Statistics KNBS..

6.        Fernie S.and Tennat S. (2014), “Theory to Practice: A typology of Supply Chain management in Construction.” International Journal of Construction Management 14 (1) page 56-66.

7.        Green, S.D., Fernie, S. and Weller, S. (2005) Making sense of supply chain management: a comparative study of aerospace and construction. Construction Management and Economics, 23, 579–593

8.        Kimondo, J. M. ; Mutuku R. & Winja O.(2015), “Dynamics of Supply Chain Management in the Kenyan Construction Industry: A Case Study of National Irrigation Board” International Journal of Innovative Scientific & Engineering Technologies Research 3(4):1-14.Love, P. E. D., Irani, Z. & David, J. E. (2004). A seamless supply chain management model for construction. Supply Chain Management: An International Journal, 9(1), 43-56.

9.        Kimari Gitau Peter (2000) “Construction Process in Kenya with reference to Hazina Housing Estate” Housing Development & Management – HDM, ICM2000, Lund University.

10.     Kimemia James Gacheru (2015) “Determinants of Projects Delay In The Construction Industry In Kenya; The Case Of Selected Road Projects Implemented By Kenya National Highways Authority In Kenya’s Coast Region” unpublished Master’s Thesis, University of Nairobi.

11.     Mehdi Riazi, Salman Riazi, Skitmore, Martin, & Cheung, Yan Ki Fiona (2011) The use of supply chain management to reduce delays : Malaysian public sector construction projects. In Proceedings of the 6th Nordic Conference on Construction Economics and Organisation in Society Volume 2, Danish Building Research Institute, Aalborg University, Copenhagen, Denmark, pp. 403-414.”

12.     Mulla Aneesa.I , Dr. A.K.Gupta , Prof. D.B.Desai “Supply Chain Management: Effective Tool in Construction Industry” International Journal of Novel Research in Engineering and Science Vol. 2, Issue 1, pp: (35-40), Month: March 2015 – August 2015.

13.     Magalhães-mendes Jorge, Fernanda Rodrigues Maria, Miguel d. F. Ferreira Luís  (2012) “Construction supply chain management: a Portuguese case study.”  A paper presented at 3rd European Conference of Civil Engineering (ECCIE’12) .

14.     O’Brien, W. J. (1998) Capacity Costing Approaches for Construction Supply-Chain Management. Ph.D. dissertation, Stanford University.
15.     O’Brien W. J, London K, Vrijhoef R. (2002) “Construction supply chain modeling: a research review and interdisciplinary research agenda” a paper presented in the 10th Annual Conference in the International Group for Lean Construction, Gramado, Brazil.
16.     Oswald A. G, Masu S. M, O. W. O. “The Role of Servant Leadership in Project Management in Kenya” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-4 Issue-5, November 2014

17.     Outa Michael Ochieng , Dr. Mbithi Shedrack , Dr. Musiega Douglas “Role of Cost Estimation On Timeliness In Completion of Kenya National Government Building Construction Projects.” International Journal of Management Research & Review ISSN: 2249-7196 IJMRR/May 2015/ Volume 5/Issue 5/Article No-1/285-295.

18.     William, W. H., (2006). Dynamics of perceptions and actions. American psychological association , 113, (2), 358-389

 

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5.

Authors:

Emin Borandag, Fatih Yucalar

Paper Title:

Audio News Reading System for Visually Handicapped People

Abstract: Visually handicapped people need to access news sites in an easy way in order to benefit from the working conditions and social rights in their daily lives. Today, there are a variety of software developed abroad with different features for the use of visually handicapped people living in our country. In this study, a software named “Audio News” converting text to speech was developed for visually handicapped people by using possibilities of mobile technology. This software will allow them to listen news on national news sites by entering them with a convenient, easy and fast interface. This software enable visually handicapped people to access information on the news portals. In addition, the usability of information technologies for visually handicapped people was questioned and some suggestions are made for the software developed along with national and international studies in the literature.

Keywords:
 Audio, Software Developed, Handicapped People


References:

1.       T.C. Başbakanlık Türkiye İstatistik Kurumu. Haber Bülteni. “Özürlüler İdaresi Başkanlığı Özürlülerin Sorun ve Beklentileri Araştırması”, Ulusal Özürlüler Database, June 2010. Number 71. 
2.       T.C. Başbakanlık Türkiye İstatistik Kurumu. Engelli İstatistikleri. Available: http://www.tuik.gov.tr/PreTablo.do?alt_id=1017 , 2014

3.       Code Factory Mobile Geo and Mobile Speak (2014). Available: http://www.codefactory.es. 2014.

4.       Freedom Scientific JAWS Available: http://www.freedomscientific.com. 2014.

5.       Handy Tech Talks&Zooms and TextScout Available: https://www.handytech.de. 2014.

6.       NV Access NVDA Available: http://www.nvaccess.org. , 2014.

7.       Microsoft Voice Command. “Microsoft Gives Mobile Devices a New Voice” (2003). Available: http://www.microsoft.com/en-us/news/press/2003/nov03/11
03voicecommandlaunch2003pr.aspx, 2014.

8.       Cyberon Corporation Voice Commander Available: http://www.cyberon.com.tw/english. 2014.

9.       eSpeak Open source project.Available: http://espeak.sourceforge.net. Available:2010

 

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6.

Authors:

Muhammad A. R Yass, Mohammed Wajeeh Ameen

Paper Title:

Pitch Mode Control System Design of Guided Missile

Abstract:  In this paper, the analysis of non-homogenous longitudinal equation of motion for transport Airplane in pitch mode to estimate and calculate the performance of the dynamic motion and Transfer function in pitch mode. The pitch feedback control system diagram with actuator Design to calculate the behavior using time response method for different gain values (K) and Different Gyro sensitivity values (GR) to obtain the best stability, peak value and time response. The results shows that the best stability and control behavior achieved when K=1.41 and GR= 1.19.

Keywords:
control system, stability, aerodynamic


References:

1.    Etkin “Dynamics of Flight Stability and Control “John Willey, 2002. http:\www.Amazon Online Reader Dynamics of Flight Stability and Control.mht
2.    P.garnell and D.J. East “Guided weapon Control System” Royal military college of science, England, 2001. www.amazon.com/Guided-Weapon-Control-Systems-Garnell/dp/0080254683 – 204k –

3.    John H. Block lock “Automatic Control of Aircraft and Missiles” John Willey and sonic, 2006. ttp:\www.Amazon_com Automatic Control of Aircraft and Missiles John H_ Blake lock Books.mht

4.    Alan Pope “Aerodynamic Of Supersonic Flight Optimum Aeronautical Publications”2002.n http:\www .Amazon_com Used and New AERODYNAMICS OF SUPERSONIC FLIGHT an Introduction.mht

5.    J.J D’Azzo. C.H.Houpis “Feedback Control System Analysis and Synthesis” Mc Graw. Hill, .2006.  http:\www.Customer Image Gallery for Feedback Control System Analysis and Synthesis.mht 7. John J.D’Azzo C.H. Houpis “linear Control System Analysis and Design”

6.    Gustav Doetsch. “Guide to the Applications of the Laplace and B-Transformation “, 2008. http:\www.Amazon_com Gustav Doetsch Books.mht

7.    J.J D’Azzo. C.H.Houpis “Linear Control System, Analysis and Design “, 2006.

8.    Flight Dynamic NOTES   C. I. T.    U. k    C. I. T. U.k, 1986.

 

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7.

Authors:

Ebenezer Komla Gavua, Seth Okyere-Dankwa, Gignarta Kasaye Soka

Paper Title:

Big Data Sentiment Analysis based on PLSA and its Application

Abstract: Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic value to vendors and other interested parties. This study conducted a case study in the movie domain, and tackles the problem of mining reviews for predicting product sales performance. Based on an analysis of the complex nature of sentiments, this paper studies Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. The study then presents SAAR, a sentiment-aware autoregressive model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. In this study SAAR is compared with alternative models that do not take sentiment information into account; as well as a model with different feature selection methods. Experiments confirm the effectiveness and superiority of the approach studied. 

Keywords:
 Blog mining, Hadoop, MapReduce.


References:

1.       Bar-Ilan, J. ( 2004). An outsider’s view on “topic-oriented blogging”. In WWW Alt. ’04, pages 28–34..
2.       Blei, D. Ng, A. & Jordan, M. (2003). “Latent dirichlet allocation”. Journal of Machine Learning Research.

3.       Dalli, A. (2006). “System for spatio-temporal analysis of online news and blogs”. In WWW ’06, pages 929–930,

4.       Dempster, A. P., Laird, N. M., & Rubin D. B.(1977). “Maximum likelihood from incomplete data via the em algorithm”. Journal of Royal Statistical Society, B(39):1–38, 1977.

5.       Efron, M.(2004). “The liberal media and right-wing conspiracies: using co-citation information to estimate political orientation in web documents”. In CIKM ’04, pages 390–398, 2004.

6.       Enders W.(2004). Applied Econometric Time Series. Wiley, New York, 2nd edition, 2004.

7.       Gruhl, D., Guha, R., Kumar, R., Novak, J. & Tomkins, A.(2005) “The predictive power of online chatter”. In KDD ’05, pages 78–87.

8.       Gruhl, D., Guha, R. ,Liben-Nowell, D., & Tomkins A.(2004) “Information diffusion through blogspace”. In WWW ’04, pages 491–501.

9.       Hofmann T.(1999). Probabilistic latent semantic analysis. In UAI’99.

10.    Jank, W., Shmueli, G., & Wang S.(2006) Dynamic, real-time forecasting of online auctions via functional models. In KDD ’06, pages 580–585.

11.    Kamps, J. & Marx, M.(2002) “Words with attitude.” In Proc. of the First International Conference on Global WordNet, pages 332–341, 2002.

12.    Kumar R., Novak, J., Raghavan, P. & Tomkins, A.(2003). On the bursty evolution of blogspace. In WWW ’03, pages 568–576, 2003.

13.    Kumar R., Novak, J., Raghavan, P. & Tomkins, A.(2004) “Structure and evolution of blogspace.” Commun. ACM, 47(12):35–39.

14.    Li, Z. Wang, B., Li, M., & Ma, W.(2005) “A probabilistic model for retrospective news event detection.” In SIGIR ’05, pages 106–113.

15.    Liu, B., Hu, M. & Cheng, J.(2005). “Opinion observer: analyzing and comparing opinions on the web.” In WWW ’05, pages 342–351, 2005.

16.    Mei, Q., Liu, C. ,Su, H. & Zhai, C.X.(2006) “A probabilistic approach to spatiotemporal theme pattern mining on weblogs”. In WWW ’06, pages 533–542.

17.    Mei, Q. & Zhai,, C.X.(2006) “A mixture model for contextual text mining”. In KDD ’06, pages 649–655.

18.    Pang, B. & Lee, L.(2004) “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.” In ACL ’04, pages 271–278 .

19.    Pang, B. & Lee, L. (2005). “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales”. In ACL ’05, pages 115–124.

20.    Pang, B., Lee L., & Vaithyanathan S.(2002). “Thumbs up? sentiment classification using machine learning techniques”. In Proc. of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP).

21.    Technorati (2007). URL:http://technorati.com/about/. Retrieved on January 27.

22.    Tseng, B. L., Tatemura, J., & Wu, Y.(2005). “Tomographic clustering to visualize blog communities as mountain views”. In Proc. Of 2nd Annual Workshop on the Weblogging Ecosystem.

23.    Turney, P.D. (2001). “Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews”. In ACL ’02, pages 417–424.

24.    Whitelaw, C., Garg, N., & Argamon S.(2005). “Using appraisal groups for sentiment analysis”, In CIKM ’05, pages 625–631.

25.    Zhang, Z. and Varadarajan, B.(2006). “Utility scoring of product reviews”. In CIKM ’06, pages 51–57.

26.    Foutz, N.Z. & Jank, W.(2007 “The Wisdom of Crowds: Pre-Release Forecasting via Functional Shape Analysis of the Online Virtual Stock Market,” Technical Report 07-114 Marketing Science Inst. Of Reports.

 

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8.

Authors:

 Maha Abdul Ameer Kadhum

Paper Title:

Application of the Guided Method to study the Characteristics of the Frequency Modulation Circuits

Abstract: This paper provides to implementation many of the experiences that uses in the communication library by using (Multisum.version 11) program. The electronic component circuit connection easily although the complexity in electronic component also useful from simulation in program to absolved the results in exact and speedy time instructional program was designed and simulated according system so that students have a knowledge base in the field of evaluation of the modulation .The instructional program takes of individualized difference because it does not require the learner to go along the sequence of units .He can by bass many of these units according to this needs speed and ability to learn.

Keywords: communication, frequency, information, instruction, modulation.

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  2. Roy Blake, “Electronic Communication Systems”, 2nd Ed, 2014.
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  5. Bayez,K , “The Basics of Communication”, Dar Al-Hikma, University of Technology, 2000.
  6. Noor Ali Hussain Al-Sudani “Audio Denoising Using Wavelet Transform”, Sc. Thesis , Computer Department ,  College of Science , Al-Nahrain University , 2011.
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  8. Saad Abdel-Wahab Al-Shaaban, “Effective in the treatment of analogue and digital signal”, Scientific Affairs Committee, University of Technology, 1999.
  9. Basem Nazhat al-Samarrai and D. Muthanna Jabr, “Principles of Engineering Statistics”, Dar al-Hikma, University of Technology, 2001.
  10. Mabarz, Manal Abdel-Aal, Ismail, Sameh Said, “Self-Learning and Distinction”, Amman, Dar Al-Fikr, 2010.
  11. Salama, Abdel Hafez and Abu Raya, Muhammad. “Computer in Education”. Al Ahlia Publishing, Jordan, 2002, [11].
  12. T, “The Innovation Adoption Perspective in M-learning for Engineering Education”, Journal of Engineering and Applied Sciences,Volume 7,Issue 1,2012, Page No. 23-30.

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