Volume-5 Issue-4

S. No

Volume-5 Issue-4, September 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Amira A. El Tayeb, Vikas Pareek, Abdelaziz Araar

Paper Title:

Applying Association Rules Mining Algorithms for Traffic Accidents in Dubai

Abstract: Association rule mining algorithms are widely used to find all rules in the database satisfying some minimum support and minimum confidence constraints. In order to decrease the number of generated rules, the adaptation of the association rule mining algorithm to mine only a particular subset of association rules where the classification class attribute is assigned to the right-hand-side was investigated in past research. In this research, a dataset about traffic accidents was collected from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori association rules algorithms were applied to the dataset in order to explore the link between recorded accidents’ factors to accident severity in Dubai. Two sets of class association rules were generated using the two algorithms and summarized to get the most interesting rules using technical measures. Empirical results showed that the class association rules generated by Apriori algorithm were more effective than those generated by Predictive Apriori algorithm. More associations between accident factors and accident severity level were explored when applying Apriori algorithm.

Keywords: Association Rule Mining, Apriori, Predictive Apriori, Dubai Traffic Accidents

References:
1. Adeyemi Adejuwon, Amir Mosavi, “Domain Driven Data Mining- Application to Business”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No.2, July 2010, pp. 41-44.
2. Qiankun Zhao, Sourav S. Bhowmick, “Association Rule Mining: A Survey”, Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003116, 2003.
3. Bing Liu, Wynne Hsu, Yiming Ma, “Integrating Classification and Association Rule Mining”, KDD-98, New York, Aug 27-31, 1998.
4. Paresh Tanna, Dr. Yogesh Ghodasara, “Using Apriori with WEKA for Frequent Pattern Mining”, International Journal of Engineering Trends and Technology (IJETT),
Volume 12 Number 3, Jun 2014, pp. 127-131.
5. Divya Bansal, Lekha Bhambhu, “Execution of APRIORI Algorithm of Data Mining Directed Towards Tumultuous Crimes Concerning Women”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 9, September 2013, pp. 54-62.
6. Zhou Aoying, Wei Li, YU Fang, “Effective Discovery of Exception Class Association Rules”, Journal of Computer Science and Technology, Volume 17, Issue 3, May 2002,
pp. 304-313.
7. Saddys Segrera, Maria N. Moreno, “Classification Based on Association Rules for Adaptive Web Systems”, Advances in Soft Computing, Volume 44, 2007, pp. 446-453.
8. David Halbert (October 20, 2008), “The World’s Worst Drivers – Car Accident Statistics From Around the World”, [Online]. Available at URL http://www.articlesbase.com/cars-articles/the-worlds-worst-drivers-car-accident-statistics-from-around-the-world-609862.html.
9. Ronald V Miller ( September 8, 2010), “Car Accident Statistics from the CDC”, [online]. Available at URL: http://www.marylandinjurylawyerblog.com/2010/09/car_accident_statistics_from_t.html.
10. Bener, D. Crundall, “Road traffic accidents in the United Arab Emirates compared to Western countries”, Advances in Transportation Studies an international Journal Section A6, 2005, pp. 5-12.
11. Abdelaziz Araar, Amira A. El Tayeb,“Mining Road Traffic Accident Data to Improve Safety in Dubai”, Journal of Theoretical and Applied Information Technology, Vol. 47 No.3, 31st January 2013, pp. 911-925.
12. Ossenbruggen, P. J., J. Pendharkar, et al. "Roadway safety in rural and small urbanized areas." Accidents Analysis and Prevention 33(4): 485-498, 2001.
13. Sohn, S. and S. Hyungwon. "Pattern recognition for a road traffic accident severity in Korea", Ergonomics 44(1): 101-117, 2001.
14. Sohn, S. and S. Lee, "Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea." Safety Science 41(1): 1-14, 2002.
15. Ng, K. S., W. T. Hung, et al., "An algorithm for assessing the risk of traffic accidents." Journal of Safety Research 33: 387-410, 2002.
16. Bedard, M., Guyatt, G. H., Stones, M. J., & Hireds, J. P., “The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities, Accident analysis and Prevention, Vol. 34, pp. 717-727, 2002.
17. Miao M. Chong, Ajith Abraham, Marcin Paprzycki, “Traffic Accident Analysis Using Decision Trees and Neural Networks”, IADIS International Conference on Applied Computing, Portugal, IADIS Press, Pedro Isaias et al. (Eds.), Volume 2, pp. 39-42 2004.
18. Chang, L. and W. Chen "Data mining of treebased models to analyze freeway accident frequency", Journal of Safety Research 36: 365- 375, 2005.
19. T. Beshah, “Application of data mining technology to support RTA severity analysis at Addis Ababa traffic office”, Addis Ababa, Addis Ababa University, 2005.
20. Chang, L. and H. Wang, "Analysis of traffic injury severity: An application of non-parametric classification tree techniques Accident analysis and prevention", Accident analysis and prevention 38(5): 1019-1027, 2006.
21. Srisuriyachai, S., “Analysis of road traffic accidents in Nakhon Pathom province of Bangkok using data mining”, Graduate Studies, Bangkok, Mahidol University, 2007.
22. Wong, J. and Y. Chung,"Comparison of Methodology Approach to Identify Causal Factors of Accident Severity." Transportation Research Record 2083: 190-198, 2008.
23. Zelalem, R., “Determining the degree of driver’s responsibility for car accident: the case of Addis Ababa traffic office”, Addis Ababa, Addis Ababa University, 2009. 24. Getnet, M., “ Applying data mining with decision tree and rule induction techniques to identify determinant factors of drivers and vehicles in support of reducing and controlling road traffic accidents: the case of Addis Ababa city, ”Addis Ababa, Addis Ababa University, 2009.
25. Sami Ayramo, Pasi Pirtala, Janne Kauttonen, Kashif Naveed, Tommi Karkkainen, “Mining road traffic accidents”, University of Jyvaskyla, Finland, 2009.
26. T. Beshah and S. Hill, "Mining Road Traffic Accident Data to Improve Safety: Role of Road-related Factors on Accident Severity in Ethiopia", Proceedings of AAAI Artificial Intelligence for Development (AI-D'10), 2010.
27. Galvão ND, de Fátima Marin H, “Traffic accident in Cuiabá-MT: an analysis through the data mining technology”, Federal University of Mato Grosso-UFMT, Brazil, 2010.
28. Amirhossein Ehsaei, Harry Evdorides,“Temporal Variation of Road Accident Data caused by Road Infrastructure”, 3rd International Conference of Road Safety and Simulation, September 14-16, Indianapolis, USA, 2011.
29. S.Krishnaveni, Dr.M.Hemalatha, “A Perspective Analysis of Traffic Accident using Data Mining Techniques”, International Journal of Computer Applications,Volum 23- No. 7, pp. 40-48, June 2011.
30. S.Krishnaveni,Dr.M.Hemalatha,“Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms”, International Journal of Computer Applications, Volume 35– No.12, December 2011, pp. 30-37.
31. Beshah, T.; Ejigu, D.; Abraham, A.; Snasel, V.; Kromer, P., “Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety”, World Congress on Information and Communication Technologies (WICT), December 2011, pp. 1241 - 1246.
32. Vandana Munde, Sachin Deshpande, S.K.Shinde,“Data Mining for Traffic Accident Analysis”, International Conference on Advances in Computing and Management, 2012.
33. Olutayo V.A, Eludire A.A, “Traffic Accident Analysis Using Decision Trees and Neural Networks”, I.J. Information Technology and Computer Science, 2014, 02, 22-28.
34. Rajdeep Kaur Aulakh, “Association Rules Mining Using Effective Algorithm: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 3, March 2015, pp. 831-835.
35. Amit Mittal, Ashutosh Nagar, Kartik Gupta, Rishi Nahar, “Comparative Study of Various Frequent Pattern Mining Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 4, April 2015, pp. 550-553.
36. Dr. S. Vijayarani, Ms. R. Prasannalakshmi, “Comparative Analysis of Association Rule Generation Algorithms in Data Streams”, International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015, pp. 15-25.
37. Sunita B Aher, Mr. LOBO L.M.R.J., “Data Mining in Educational System using WEKA”, International Conference on Emerging Technology Trends (ICETT), 2011, pp. 20-25.
38. Sunita B Aher and Lobo L.M.R.J, “A Comparative Study of Association Rule Algorithms for Course Recommender System in E-Learning”, International Journal of Computer Applications, Volume 39 – No. 1, February 2012, pp. 48-52.
39. “Stratified Random Sampling”, [online]. Available at URL: http://www.stat.ualberta.ca/~prasad/361/STRATIFIED%20RANDOM%20SAMPLING.pdf.
40. Carlos Ordonez, Norberto Ezquerra, Cesar A. Santana, “Constraining and Summarizing Association Rules in Medical Data”, Knowledge and Information Systems, Volume 9, Issue 3, 2006, pp.. 259 - 283.

1-12

2.

Authors:

Sadik Khan, Yashpal Singh, Ajay Kumar Sachan

Paper Title:

Web Mining in Search Engines for Improving Page Rank

Abstract: An application of web mining can be seen in the case of search engines. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. In this research, a survey of page ranking algorithms and comparison of some important algorithms in context of performance has been carried out.So this kind of problem is actual need of this proposed research work. One of the major problems for automatically constructed portals and information discovery systems is how to assign proper order to unvisited Web pages. Topic-specific crawlers and information seeking agents should try not to traverse the off-topic areas and concentrate on links that lead to documents of interest. In this chapter, we propose an effective approach based on the relevancy context graph to solve this problem.Some commonly used link algorithms are page rank, HITS and Weighted Page Content Rank. Most of the search engines are ranking their search results in response to user’s queries to make their search navigations easier. In this paper we give a study of page ranking algorithms and description about Pagerank , HITS, based on web content mining and structure mining that shows the relevancy of the pages to a given query is better determined, as compared to the Page Rank and HITS.

Keywords: Web Mining ,Data mining, HITS, Search Engines, web content, Page rank, Web Logs, web structure mining, web content mining.

References:
1. M Eirinaki, M Vazirgiannis, Web Mining for Web Personalization, in ACM Transactions on Internet Technology (TOIT), 3(1), February (2003).
2. 1.M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM, 46(5):604-632, September (1999).809
3. S. Chakrabarti, B. Dom, D. Gibson, 1. Kleinberg, R Kumar, P. Raghavan, S. Rajagopalan, A Tomkins, Mining the Link Structure of the World Wide Web, IEEE Computer (1999) Vol.32 No.6.
4. S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks, 30(1 7): 107-117, 1998, Proceedings of the 7th International World Wide Web Conference(WWW7).
5. 1.M. Kleinberg, Hubs, Authorities, and Communities, ACM Computing Surveys, 31 (4), December (1999).
6. D7. Gibson, J. Kleinberg, P. Raghavan, Inferring Web Communities from Link Topology, in the Proceedings of the 9th ACM Conference on Hypertext and Hypermedia, (1998).
7. R Kumar, P. Raghavan, S. Rajagopalan, A Tomkins, Trawling the Web for Emerging Cyber-Communities, in Proceedings of the 8th WWW Conference (WWW8), (1999).
8. Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, SIGKDD Explorations, January 2000Nol. 1, Issue 2, pp. 12-23.
9. M. Rajman, M. Vesely, From Text to Knowledge: Document Processing and Visualization: a Text Mining Approach, in Proceedings of the NEMIS Launch Conference, International Workshop on Text Mining & its Applications, Patras, Greece, April(2003).
10. N. Oikonomakou, MVazirgiannis, A Review of Web Document Clustering approaches, in Proceedings of the NEMIS Launch Conference, International Workshop on Text Mining & its Applications, Patras, Greece, April (2003).
11. G. Pinski, F. Narin, Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics, in Information Processing and Management. 12, (1976).
12. S. Shearin and H. Liebermann, Intelligent Profiling by Example, Proc. of Intern. Conf. of Intelligent User Interfaces (IUI2001), p. 145-152, Santa Fe, NM, Jan. 14-17, 2001.13. WEB MINING: A ROADMAP, Magdalini Eirinaki, Dept. of Informatics Athens University of Economics and Business.
14. Evaluating the datamining techniques and their roles in increasing the search speed data in web, Ayatollah Amoli Branch, Comput. Dept., Islamic Azad Univ., Amol, Iran , DOI: 10.1109/ICCSIT.2010.5563818 Conference: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, Volume: 9

13-16

3.

Authors:

Md. Osman Goni Nayeem, Maung Ning Wan, Md. Kamrul Hasan

Paper Title:

Prediction of Disease Level Using Multilayer Perceptron of Artificial Neural Network for Patient Monitoring

Abstract: ANN has been proved as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. In our research, ANN has been used for predicting three different diseases (heart disease, liver disorder, lung cancer). Feed-forward back propagation neural network algorithm with Multi-Layer Perceptron is used as a classifier to distinguish between infected or non-infected person. The results of applying the ANNs methodology to diagnosis of thesedisease based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In our proposed work, Multi-Layer Perceptron with having 2 hidden layer is used to predict medical diseases. Here in case of liver disorder prediction patients are classified into four categories: normal condition, abnormal condition (initial), abnormal condition and severe condition. This neural network model shows good performance in predicting disease with less error.

Keywords: Artificial Neural Network (ANN), Multilayer Perceptron, Heart Diseases, Liver Disorder, Lung Cancer.

References:
1. J. Dayhoff and J. DeLeo, “Artificial neural networks opening the black box,” Cancer Supplement, vol. 91, No. 8, pp. 16151635, 2001.
2. Dimitrios H. Mantzaris, George C. Anastassopoulos and Dimitrios K. Lymberopoulos,“Medical Disease Prediction Using Artificial Neural Networks” vol.91, NO.82,pp, 16253765 , July 5, 2008, IEEE.
3. V. Piuri and F. Scotti, “Morphological classification of blood leucocytes by microscope images,” in 2004 IEEE Int. Conf. Computational Intelligence for Measurement Systems and Applications, pp. 103108.
4. D. S. Huang and S. D. Ma, “Linear and nonlinear feedforward neural network classifiers: A comprehensive understanding,” J. Intelligent Syst., vol. 9, pp. 1–38, 1999.
5. Irfan Y. Khan, P.H. Zope, S.R. Suralka, “Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease” Volume 2, Issue 2, March 2013, International Journal of Engineering Science and Innovative Technology (IJESIT).
6. Kadhim Al-Shayea, “Artificial neural network in medical diagnosis”, International journal of computer science Vol.8, Issue 2, March 2011.Qeethara
7. Qeethara Kadhim Al-Shayea and Itedal S.H. Bahia, “Urinary system Dieseases Diagnosis Using Artificial neural networks”, IJCSNS, Vol.10, No.7, July 2010.
8. S. Moein, S.A. Monadjemi, and P. Moallem, “A Novel Fuzzy Neural Based Medical Diagnosis system”, International Journal of biological and Life Science 4:3, 2008.
9. Abdel- Badeeh M. Salem, “Case Base Reasoning Technology for Medical Diagnosis”, World Academic of Science, Engineering and Technology 7, 2007.
10. Ashish Dehariya, Ilyas Khan,Vijay K. Chaudhari and Saurabh Karsoliya, “ A Novel flow Reasoning of Medical Diagnostic system using Artificial Feed Forward Neural Networks”, IJCSE, Vol.3, No-3, March 2011.
11. Zhi- Hua Zhou, Yoan Jiang, Yu- Bin Yang & Shi- Fu Chen, “ Lung Cancer Cell Identification Based On Artificial Neural Network ensembles”, Artificial Intelligence in Medicine Vol24, No-1, 2002.
12. Xin Yao Senior Member, IEEE and Yong Liu, “A new evolutionary system for evolving Artificial Neural Networks”, IEEE Transaction on Neural Network Vol.8, No-1, May 1997 Transactions on information Technology in Biomedicine Vol 11, No.3, May- 2012.
13. Baker J A, Kornguth PJ, Lo JY, Williford ME, Floyd CE Jr(1995) : “Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon”. Radiology, 1995; 196(3): 817-22
14. P. S. Heckerling, G. Canaris, S. D. Flach, T. G. Tape, R. S. Wigton and B. S. Gerber, Predictors of urinary tract infection based on artificial neural networks and genetic algorithms, International Journal of Medical Informatics; April, 2007, Vol. 76 Issue 4, pp. 289-296.
15. S. A. Monadjemi and P. Moallem, Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach, International Review on Computers & Software, Jul., 2008, Vol. 3 Issue 4, pp. 406-411
16. R. Suganya, and S. Rajaram, "Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach”, American Journal of Applied Sciences 9 (6), 2009, pp. 938-945.
17. S. Babaei, A. Geranmayeh, "Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals", Computers in Biology and Medicine (39), 2009, pp. 8–15.
18. Zhi-Hua Zhou, Member IEEE and Yuan Jiang, “Medical Diagnosis with C4.5 Rule Preceded By Artificial Neural Network Ensemble”, IEEE Transactions on information Technology in Biomedicine Vol-7, No-1, March 2003.
19. D. Gil, M. Johnsson, J. M. Garicia Chemizo, A. S. Paya and D. R. Fernandez, Application of Artificial Neural Networks in the Diagnosis of Urological Dysfunctions, Expert Systems with Applications, April, 2009, Vol. 36 Issue 3, pp. 5754-5760.
20. W. G. Baxt, “Application of artificial neural networks to clinical medicine,” Lancet, vol. 346, no. 113, pp. 58, 1995.
21. J. S. Chiu, Y. C. Li, F. C. Yu, and Y. F. Wang, “Applying an artificial neural network to predict osteoporosis in the elderly,” Studies in Health Technology and Informatics, vol. 124, pp. 609614, 2006.
22. G. Zhang and V. Berardi, “An investigation of neural networks in thyroid function diagnosis,” Health Care Management Science, vol.1, pp. 29–37, 1998.

17-23

4.

Authors:

Alireza Noroziroshan, Shaghayegh Habibi

Paper Title:

A Performance Analysis of Memetic Algorithm, Genetic Algorithm and Simulated Annealing in Production System Optimization

Abstract: Researchers laid the foundation of evolutionary algorithms in the late 60s and since then, heuristic algorithms have been widely applied to several complex scheduling and sequencing problems during the recent studies. In this paper, memetic algorithm (MA), genetic algorithm (GA) and simulated annealing (SA) are applied to a complex sequencing problem. The problem under study concerns about sequencing problem in mixed-shop floor environment. The main objective is to minimize the overall make-span of multiple mixed-model assembly lines by finding the best job sequence and allocation. The superiority of MA’s performance is proved by evaluating standard deviation, optimal solution and mean value of obtained solutions.

Keywords: Genetic Algorithm, Make-span, Memetic Algorithm, Simulated Annealing.

References:
1. M. P. Groover, Automation, production systems, and computer-integrated manufacturing, p.^pp. 212-215, Upper Saddle River, N.J.: London : Prentice Hall ; Prentice-Hall International, 2001.
2. D. R. Sule, Industrial scheduling, 20 Park Plaza,Co Boston: PWS Publishing Company. , 1997.
3. R. S. Russell, and B. W. Taylor, Operations management: quality and competitiveness in a global environment: Wiley, 2006.
4. V. Sekar, “Minimizing the make-span in a high-product mix shop-floor using integer programming,” M.S., State University of New York at Binghamton, United States -- New York, 2007.
5. R. Brahim, “Assembly line design: The balancing of mixed-model hybrid assembly lines with genetic algorithms,(Series in advanced manufacturing),” 2006, 2006.
6. E. Eiben, and J. E. Smith, Introduction to evolutionary computing, Verlag Berlin Heidelberg New York: Springer, 2003.
7. Naderi, M. Zandieh, A. Khaleghi Ghoshe Balagh et al., “An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness,” Expert Systems With Applications, 2008.
8. M. Zandieh, S. M. T. Fatemi Ghomi, and S. M. Moattar Husseini, “An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times,” Applied Mathematics and Computation, vol. 180, no. 1, pp. 111-127, 2006.
9. B. Wang, Y. Rao, X. Shao et al., "Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm." p. 387.
10. S. Karabati, and S. Sayin, “Assembly line balancing in a mixed-model sequencing environment with synchronous transfers,” European Journal of Operational Research, vol. 149, no. 2, pp. 417-429, 2003.
11. P. R. McMullen, and G. V. Frazier, “A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line,” IIE Transactions, vol. 32, no. 8, pp. 679-686, 2000.
12. W. L. Winston, Introduction to mathematical programming: applications and algorithms, Fourth Edition ed., CA93950, USA: Duxbury Resource Center, 2003.
13. M. Vazquez, and D. Whitley, "A hybrid genetic algorithm for the quadratic assignment problem." pp. 135–142.
14. L. Wang, and D.-Z. Zheng, “An effective hybrid optimization strategy for job-shop scheduling problems,” Computers & Operations Research, vol. 28, no. 6, pp. 585-596, 2001.
15. M. Yazdani, M. Gholami, M. Zandieh et al., “A simulated Annealing Algorithm for Flexible Job Shop Scheduling Problem,” Journal of Applied Sciences, pp. 1-9, 2009.
16. M. Negnevitsky, Artificial intelligence: a guide to intelligent systems: Addison-Wesley, 2005.
17. Y. K. Kim, C. J. Hyun, and Y. Kim, “Sequencing in mixed model assembly lines: A genetic algorithm approach,” Computers & Operations Research, vol. 23, no. 12, pp. 1131-1145, 1996.
18. Z. Michalewicz, Genetic algorithms+ data structures= evolution programs, Charlotte, USA: Springer, 1996.
19. J. J. Grefenstette, “Optimization of control parameters for genetic algorithms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 16, no. 1, pp. 122-128, 1986.
20. Y. Y. Leu, L. A. Matheson, and L. P. Rees, “Assembly Line Balancing Using Genetic Algorithms with Heuristic-Generated Initial Populations and Multiple Evaluation Criteria*,” Decision Sciences, vol. 25, no. 4, pp. 581-606, 1994.
21. C. Oysu, and Z. Bingul, “Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining,” Engineering Applications of Artificial Intelligence, vol. 22, no. 3, pp. 389-396, 2009.
22. Fogel, L. J., Owens, A. J., and Walsh, M. J. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
23. Moscato, P. On genetic crossover operators for relative order preservation.C3P Report 778, California Institute of Technology, Pasadena,CA 91125, 1989.
24. Stalk, George. "Time--the next source of competitive advantage." (1988): 41-51.

24-30

5.

Authors:

Ibrahim F. Alshammari, Haider A. Abdulkarim, Ali Abdulraheem Alwan

Paper Title:

CW Laser Combined with LED to Reduce the FWM in SAC-OCDMA Network

Abstract: A new technique based on LED combined with CW lasers in Spectral Amplitude Coding (SAC) Optical Code Division Multiple Access (OCDMA) networks, which allow reduction of the four-wave mixing (FWM) effect. In this paper, SAC-OCDMA networks have been developed and analyzed based on Multi Diagonal (MD) and Zero Cross Correlation (ZCC) codes. We simulate and investigate of three users design and concluded that the FWM can be reducing by using the LED source combined with CW lasers for each user in the code's design. Our results show that the MD code gives better performance than the ZCC code by using our technique. In other words, the ratio of reducing the FWM power in the MD code is approximately -20 dBm, while in ZCC is -10 dBm..

Keywords: Optical code division multiple access (OCDMA), Spectral amplitude coding (SAC); Multi diagonal (MD);Zero-Cross Correlation (ZCC); Four-Wave Mixing (FWM; Light Emitting Diode (LED).

References:
1. Abtin Keshavarzian, J. A. S. "Optical Orthogonal Code Acquisition in Fiber-Optic CDMA Systems via the Simple Serial-Search Method." IEEE Transactions on Communication Vol. 50, No. 3 (2002).
2. Indu Bala, V. R. "Performance analysis of SAC based non-coherent optical CDMA system for OOC with variable data rates under noisy environment." Indian Journal of Science and Technology Vol.2 No. 8: 49-52(2009)
3. Fuad A. Hatim, F. N. H., Sahbudin Shaari. "Effects of Nonlinear Stimulated Brillouin Scattering on Performance Analysis of an Optical CDMA Transmission System." Journal of Optical Communications 30: 104-108(2009)
4. Osamu Aso, M. T., Shu Namiki. "Four-Wave Mixing in Optical Fibers and Its Applications." Furukawa Review 19: 63-68(2000)
5. K.P. Lor, K. S. C.. "Theory of nondegenerate four-wave mixing in a birefringent optical fibre." Optics Communications 152: 26-30(1998)
6. Abd, T. H., S. A. Aljunid, et al. "Development of a new code family based on SAC-OCDMA system with large cardinality for OCDMA network." Optical Fiber Technology 17(4): 273-280
7. Hamza M. R. Al-Khafaji, S. A. Aljunid., Hilal A. Fadhil. Spectral Efficiency of Unipolar SAC–OCDMA System Considering Noise Effects. IEEE Symposium on Indestrial Electronics and Applications (ISIEA 2011). Langkawi, Malaysia, IEEE explore: 218-222(2011)
8. S. P. Singh, N. S.. "Nonlinear Effects In Optical Fibers: Origin, Managment And Applications." Progress In Electromagnetics Research, PIER Vol. 73: 249–275(2007)
9. S.V. Kartalopoulos, Introduction to DWDM Technology -- Data in a Rainbow, John Wiley & Sons, 2000
10. Anuar, M. S., S. A. Aljunid, et al. (2007). "New design of spectral amplitude coding in OCDMA with zero cross-correlation." Optics Communications 282(14): 2659-2664(2007)

31-35

6.

Authors:

K. Uma Devi, B. Lalitha

Paper Title:

Optimizing Service Selection in Combinatorial Auction by Resolving Non-Linear Programming Constraints

Abstract: The selection of services with the aim to fulfill the quality constraints became critical and challenging research aspect in the field of service computing to promote automated service selection in service-based systems (SBSs), especially when the quality constraints are stringent. However, none of the existing approaches for quality-aware service composition has sufficiently considered QoS parameters to determine the best service. This paper proposes an optimization model for SBS to automate the process of quality aware service selection. Furthermore, this paper presents a compositional quality model to analyze and optimize the quality constraints that play a vital role in Winner Determination Problem (WDP)

Keywords: critical and challenging research aspect, computing to promote automated service selection, QoS parameters, optimization model for SBS, Winner Determination Problem (WDP).

References:
1. Qiang He, Jun Yan,” Quality-Aware Service Selection for Service-Based Systems Based on Iterative Multi-Attribute Combinatorial Auction”, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 40, NO. 2, FEBRUARY 2014, pp: 192-215.
2. M.R. Andersson, T. Sandholm, “Time-quality tradeoffs in reallocative negotiation with combinatorial contract types”, Proc. American Association for Artificial Intelligence-99, Orlando, FL, 1999, pp. 3-10.
3. Federal Communications Commission.
4. http://wireless.fcc.gov/auctions/31/, April 2000.
5. S.J. Rassenti, V.L. Smith, R.L. Buffin, “A combinatorial auction mechanism for airport time slot allocation”, Bell Journal of Economics, vol. 13, 1982, pp. 402 - 417.
6. F.Kelly and R.Steinberg, “A combinatorial auction with multiple winners for universal service”, Management Science, vol. 46, 2000, pp. 586 - 596.
7. T. Sandholm and S. Suri, “BOB: Improved winner determination in combinatorial auctions and generalizations”, Artificial Intelligence, vol. 145, 2003, pp. 33 – 58.
8. G. Canfora, M.D. Penta, R. Esposito, F. Perfetto, and M.L. Villani, “Service Composition (Re)Binding Driven by Application-Specific QoS,” Proc. Fourth Int’l Conf. Service-Oriented Computing (ICSOC ’06), pp. 141-152, 2006.
9. Y. Li, J. Huai, T. Deng, H. Sun, H. Guo, and Z. Du, “QoS-Aware Service Composition in Service Overlay Networks,” Proc. IEEE Int’l Conf. Web Services (ICWS ’07), pp. 703-710, 2007.
10. D. Ardagna and B. Pernici, "Adaptive Service Composition in Flexible Processes," IEEE Transactions on Software Engineering, vol. 33, pp. 369-384, 2007
11. Object Management Group. (2011). Business Process Model And Notation (BPMN) Version 2.0. Available: http://www.omg.org/spec/BPMN/2.0/PDF/
12. OASIS. (2007). Web Services Business Process Execution Language Version 2.0. Available: http://docs.oasis-open.org/wsbpel/2.0/wsbpelv2.0. pdf
13. Q. He, J. Han, Y. Yang, J. Grundy, and H. Jin, "QoS-Driven Service Selection for Multi-tenant SaaS," Proc. 2012 IEEE Fifth International Conference on Cloud Computing, Honolulu, HI, USA, 2012, pp. 566- 573.
14. L. Zeng, B. Benatallah, A. H. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, "QoS-Aware Middleware for Web Service Composition," IEEE Transactions on Software Engineering, vol. 30, pp. 311-327, 2004.

36-39

7.

Authors:

Ruby Singh, Chiranjit Dutta, Ranjeet Singh

Paper Title:

Increasing Efficiency & Detailing in Analysis of Market Trends using SAS

Abstract: In the fast moving world and changing scenario of market (Business) there is need for improving and updating at every point of time, in order to obtain maximum and exact output companies need detailed data to work on hence this paper involves researching on increasing the efficiency so as to obtain better and exact prediction for the product to be used. The SAS System of software provides a wide variety of tools for analyzing market research data. Everything from simple summary analysis to advanced statistical and graphical techniques is available. Users holding different levels of expertise in both software and market research methodologies benefit from these tools. This project briefly discusses some of the methods available in the SAS System and will examine a case study of a current SAS software user, see how they have implemented their market research applications and increase the efficiency in prediction of aspects related to products. SAS ®is widely accepted as the gold standard for determining safety and efficacy for clinical trials, and it provides the primary mechanism for preparing data for traditional clinical research analysis activities. However, most SAS users in the biopharmaceutical industry are unaware of the broad range of SAS analytics that are widely applied in other industries. This paper discusses and describes how SAS business and advanced analytics can be used to design Better trials, forecast patient-based activities, and optimize other operational processes. Applying business and advanced analytics to clinical trial operations represents a new and improved approach to reducing the cost and time associated with managing clinical research projects. As a result, the roles of SAS experts in the biopharmaceutical industry are expanded.

Keywords: SAS, BI-Tools, Market-Research

References:
1. Kuhfeld, Warren F. (1993), Marketing Research Methods in the SAEfPSystem, A Collection of Papers and Handouts.Latour, Kristin (1994), "Market Research Methods in the SASe System," CSMA Conference, Orlando, FL.
2. Roeder, Kelly (2014), "Giving Customers What They Want; SAS Communicetions", 20, 14-16.
3. SAS Institute Inc. (2014), Introduction to Marlcet Research Using the SAS" System, Cary, NC; SAS Institute Inc.
4. SAS Institute Inc. (2013), SAEfPTechnical Report R-109, Conjoint Analysis Examples, Cary, NC: SAS Institute Inc.
5. Shorland, Michael and Zodrow, Michael (2013), "BearCreek Builds In-house Gold Mine," Direct Marlceting,35-40.
6. Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications By Kattamuri S. Sarma
7. SAS | Business Analytics and Business Intelligence www.sas.com/

40-45

8.

Authors:

Hindayati Mustafidah, Suwarsito

Paper Title:

Error Rate Testing of Training Algorithm in Back Propagation Network

Abstract: Artificial Neural Network (ANN), especially back propagation method has been widely applied to help solve problems in many areas of life, eg for the purposes of forecasting, diagnostics, and pattern recognition. An important part at ANN in determining the performance of the network is training algorithm used. Because there are 12 training algorithms that can be used at back propagation method, of course, it’s needed to be selected the most optimal algorithm in order to obtain the best results. Training algorithm performance is said optimal in providing solutions can be seen from the error generated. The smaller the error is generated, the more optimal performance of the algorithm. In this study, testing to get the training algorithm has the smallest error rate of 12 existing algorithms. Testing begins with the preparation of a computer program modules using MATLAB programming language to get the error value of the network output for each training algorithm. Each program for each training algorithm executed 20 times. Furthermore, the error of the network output was tested using analysis of variance with an alpha level of 5% to get a training algorithm which has the smallest error rate. The conclusion of the test results is that the training algorithm “trainlm” has the smallest error with the network parameters for the target error = 0.001 (10-3), the maximum epoch = 10000, learning rate (lr) = 0.01, and 5 neuron input data with 1 neuron output.

Keywords: error rate, training algorithm, back propagation, network parameters

References:
1. J.J. Siang, 2009, Neural networks and programming Using MATLAB, ANDI, Yogyakarta.
2. M.T. Jones, 2008, Artificial IntelligenceA Systems Approach. Infinity Science Press LLC, New Delhi.
3. S. Kusumadewi , S. Hartati, 2006, Integration of Fuzzy Systems and Neural Networks, Graha Ilmu, Yogyakarta.
4. S. Kusumadewi, 2004, Develop Artificial Neural Networks Using MATLAB and EXCEL LINK, Graha Ilmu, Yogyakarta.
5. Harjono, D. Aryanto, 2009, “Application of Artificial Neural Networks to Predict Student Achievement Study”, SAINTEK ISSN1411-2558, Vol. 5 No. 2.
6. H. Mustafidah, D. Aryanto, D.K. Hakim, 2013, “Optimization Test of Algorithm Conjugate Gradient Training on Artificial Neural Networks”, Proceeding SENATEK, ISBN: 978-602-14355-0-2 pp. B.9-1. 21st September 2013.
7. H. Mustafidah, D.K. Hakim, S. Sugiyanto, 2013, “Optimization Level of Training Algorithm on Artificial Neural Networks (Case Study: Student Achievement Prediction)”, JUITA ISSN: 2086-9398 Vol. II No. 3, May 2013, pp. 159 – 166.
8. F. Wibowo, S. Sugiyanto, H. Mustafidah, 2013, “Data Pattern Recognition Accuracy Level on Neural Network Improved Training Algorithm Method in Batch Mode”, JUITA, ISSN 2086-9398, Vol. II No. 4, November 2013, pp. 259 – 264.
9. H. Mustafidah, S. Hartati, R. Wardoyo, A. Harjoko, 2013, “Prediction of Test Items Validity Using Artificial Neural Network”, Proceeding International Conference on Education, Technology, and Science (NETS) 2013, “Improving The Quality Of Education To Face The Impact Of Technology”. December 28th, 2013. University Muhammadiyah of Purwokerto.
10. T. Taniredja, H. Mustafidah, 2011, “Quantitative Research (an Introduction)”, Alfabeta, Bandung.

46-50

9.

Authors:

Md. Kamrul Hasan, Md. Osman Goni Nayeem, Md. Asif Ahamed, Maung Ning Wan, Mohiuddin Ahmad

Paper Title:

Randomness Prediction of Brain Tumor by Analyzing EEG Signal Using Approximate Entropy and Regression Analysis

Abstract: Brain activity commonly known as the Electroencephalographic (EEG) signal is the measure of the brain state either normal or abnormal condition of the human brain. The brain contains about 10 Billion or more working brain cells. Brain tumor is life frightening disease of human brain. The brain tumor is the disease which neutralize the neuron day by day on the brain. The detection of brain tumor is one of the major problem by analyzing the brain signal (EEG Signal). The more the age of the tumor in the brain indicates the more randomness that is more unpredictable. In our research, we tried to find out the solution for the detection of tumor level that exist in the human brain. To complete this research, EEG data of the tumor patients having different age of tumor growth is analyzed and regression equation is determined for the prediction of the randomness. By using this regression equation, clinical person may provide the treatment for the tumor affected persons.

Keywords: EEG Signal, Approximate Entropy (ApEn), Brain Tumor, Regression Analysis

References:
1. K. Lehnertz,F. Mormann, T. Kreuz, R. Andrzejak,C. Rieke, P. David , andC. Elger. Seizure prediction by nonlinear EEG analysis. IEEE Engineering in Medicine and Biology Magazine. 2003.
2. P. K. Nayak, and N. U.Cholayya. Independent componentanalysis of Electroencephalogram. IEE Japan Papers of Technical Meeting on Medical and Biological Engineering. 2006.
3. National Brain Tumor Society (April 2010). Symptoms & Diagnosis. April 2010. [Online]
4. http://www.braintumor.org/patientsfamily-friends/about-brain-tumors/symptoms-and-diagnosis/.
5. J. D.Bronzino. Biomedical Engineering Handbook. New York: CRC Press LLC. Vol. I. 2nd edition. 2000.
6. American Brain Tumor Association. Brain Tumor Primer – A Comprehensive Introduction to Brain Tumor. ABT Press. 9th edition. 2012.
7. M. Murugesan, R. Sukanesh. Automated Detection of Brain Tumor in EEG Signals Using Artificial Neural Networks. International Conference on Advances in Computing, Control, & Telecommunication Technologies. 2009..
8. The Musella Foundation for Brain Tumor Research &Information,Brain Tumor Symptoms Survery Results, April 2010.[Online]. http://www.virtualtrials.com/braintumorsymptomssurvey/.
9. M. K. Rosenblum. The 2007 WHO Classification of Nervous System Tumors: Newly Recognized Members of the Mixed Glioneuronal Group. International Society of Neuropathology. 2007.
10. L. S. Yew and G. A. Leng. The Diagnosis of brain tumor. Singapore Medical Journal. Vol 9. 1968.
11. L. R.Schad, R.Boesecke, W. Schlegel, G. H. Hartmann, V. Sturm, L. G. Strauss, W. J. Lorenz. Three dimensional image correlation of CT, MR, and PET studies in radiotherapy treatment planning of brain tumors. J Comput Assist Tomogr. 1987.
12. Boutros, Nashaat .Standard EEG: A Research Roadmap for Neuropsychiatry.
13. H.Ocak. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Elsevier journal on Expert Systems with
Applications. March 2009.
14. C.Cobelli, E Carson. Introduction to Modeling in Physiology and Medicine. Elsevier. 2007.
15. M. L.Steyn-Ross, D. A. Steyn-Ross, J. W. Sleigh, D. T. J.Liley. Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: Evidence for a general anesthetic-induced phase transition. Phys Rev E Stat Phys Plasmas Fluids RelatInterdiscip Topics.1999.
16. Pincus, M. Steven, I. M. Gladstone, and A. R.Ehrenkranz. A regularity statistic for medical data analysis. Journal of Clinical Monitoring. October 1991.
17. M. A.Riheen, M. W. Rahman, A. B. M. A. Hossain. Selection of Proper Frequency Band and Compatible Features for Left and Right Hand Movement from EEG Signal Analysis. 16th International Conference on Computer and Information Technology (ICCIT). 2013. DOI: 10.1109/ICCITechn.2014.6997366.
18. Measurement of error. Available at: www.socialresearchmethods.net/kb/measerr.php
19. H. T.Ocbagabir, K. A. I.Aboalayon, M.Faezipour. Efficient EEG analysis for seizure monitoring in epileptic patients. Systems, Applications and Technology Conference (LISAT). 2013.DOI: 10.1109/LISAT.2013.6578218
20. Regression analysis. Available at:
21. en.wikipedia.org/wiki/Regression_analysis
22. D. A. Freedman. Statistical Models: Theory and Practice. Cambridge University Press. 2005.
23. EEG Recording. Available at:
24. http://www.aha.ru/~geivanit/EEGmanual/Recording.htm

51-55

10.

Authors:

Cyrus Babu Ong`ondo, Githae Wanyona, Abednego Gwaya

Paper Title:

An Investigation into the Factors that Influence Project Control Process in the Implementation of Construction Projects in Kenya

Abstract: The aim of project control process is to ensure projects are delivered on-time, within-budget, desired quality amongst other performance measures (Jackson, 2004). In the construction industry of Kenya, many technological developments have occurred just like other developing countries leading to emergency of project control techniques to aid on the effectiveness of the control process, they include Gantt, Bar charts, Program evaluation and review techniques (PERT) and critical path method (CPM).In addition, many software packages have also been developed to support these techniques. Further, the Government of Kenya (GoK) uses the Building organization and operations manual (BOOM) as an official document to guide implementation of projects (Munano, 2012). Despite the wide use of these control methods and techniques, many projects still fail during implementation. Pointing to a potential gap on what influences the effectiveness of the control process in management of construction projects. This study therefore sought to investigate factors influencing project control process in an effort to enhance effectiveness in project controls. This cross-sectional research adopted a mixed-method design consisting of analysis of a questionnaire survey administered to active 67No. (NCA1, NCA2, NCA3 and NCA4) contractors selected by way of stratified random sampling. A similar approach was also used to select 53No.Consultants with a response rate of 78% and 81% respectively. Data analysis techniques employed include descriptive statistics and thematic analysis. The study established thirty six (36No.) factors that influence project control process. These factors were clustered into seven (7No.) groups. They include; Pre-construction planning (RII=0.786), Project communication (RII=0.801), Commitment to project (RII=0.763), Project administration (RII=0.817) and factors related to Monitoring & Evaluation (RII=0.785).It’s recommended that project managers should enhance their pre-construction planning strategies and establish a good enabling environment for the execution of construction projects by constituting a competent project team, clearly defining the performance benchmarks, outlining the project scope, establish a sound communication plan for the project and receive commitment from all the project participants.

Keywords: Project control, construction industry, Project control factors, Kenya

References:
1. Abd El-Razek, M. (2008). Causes of Delay in Building Construction Projects In Egypt. Journal of Construction Engineering & Management, , 134(11),831-841.
2. Akintoye, A. (2007). Collaborative relationships in construction-The UK contractor`s perception. Engineering,Construction and Architectural Management.
3. Atkinson, R. (1999). Project management: Cost,time and quality,two best guesses and a phenomena.its time to accept other success criteria. International Journal of Project Management, Vol 17,Issue 6 December,1999,Pages 337-342..
4. Chandara, P. (2002). Projects Planning,Financing,Implementation and Review. Tata: McGraw-Hill Publishing Company.
5. Chitkara, K. (2002). Construction Project Management Planning,Scheduling and Control. Hill Publishing Company Ltd.
6. Christenson, D. (2008). Using vision as a critical Success element in Project Management. International Journal of Project Management.
7. [Cooke, B. W. (2004). Construction Planning Programming and Control. Oxford: Blackwell Publishing.
8. Cooke-Davies, T. (2002). "The real success factors on projects. International Journal of Project Management.
9. Egbu, C. (1998). "Planning and Control processes and techinques for refurbishment management.". Construction Management and Economics, 16(3),315-325.
10. Fena-Mora, F. (2001). Dynamic Planning and control methodologyfor design/build fast-track construction projects. Journal of Construction Engineering and Management, 127(1),1-17.
11. Floyd, L. (2004). " Application of appropriate control tools for contract type". Cost Engineering, 46(2),25-30.
12. Forsythe, P. (2008). Modelling customer perceived quality in housing. International journal of project management,Elsevier Science Ltd and IPMA.
13. Fortune, J. (2006). Framing of critical success factors by a systems model. International Journal of Project Management,Elsevier Science Ltd and IPMA.
14. Frimpong, Y. (2003). Delay and cost overruns in Construction of Ground water Projects in developing countries. International Journal of Project Management, 21,321-326.
15. George, R. (2008). Critical activities in front End planning process. Journal of Management of Engineering.
16. Gichunge, H. (2000). Risk management in the Building Industry in Kenya. Unpublished PHD.Thesis.University of Nairobi.
17. Goodman, L. (1988). Project Planning and Management-an integrated system for improving productivity. New York: Van Nostrand Reinhold Company Inc.
18. Greer, M. (1999). Handbook of Human performance Technology. San Francisco: Jossey-Bas.
19. Gwaya, A. (2014). Development of appropriate project management factors for the construction industry in Kenya. International Journal of Soft Computing and Engineering (IJSCE), ISSN:2231-2307,Vol 4,Issue 1.
20. Hendrickson. (1999). Causes of Delay in Construction. Journal of Construction Engineering and Management, Vol 134,issue 11,p831.
21. Hillebrandt, P. (2000). Economic theory and the construction Industry.3rd Edition. London: Macmillan.
22. Iyer, K. J. (2005). Factors affecting cost performance evidence from indian construction projects. International journal of project management,, 23 (4),283-295.
23. Jackson, B. (2004). Construction Management Jump Start. CA: Sybex Incorporated Alaneda.
24. Johnson, G. (2006). Exploring Corporate Strategy 7th Edition. London: Pearson Education.
25. Kagiri, N. (2005). Time and Cost overrun in Power projects in Kenya: A case study of Kenya Electricity Generating Company Ltd. Unpublished MBA Thesis.University of Nairobi.
26. Kaming, P. (1997). Factors Influencing Construction Time and Cost Overruns on High-Rise Projects in Indonesia. Journal of Construction Management and Economics, 7,83-94.
27. Kenny, C. (2007). Construction,Corruption and developing countries. World Bank policy Research working paper.
28. Kerzner, H. (2006). Project Management: A systems Approach to Planning,Scheduling and Controlling 9th Edition. John Wiley & Sons publications.
29. Kongere, N. S. (2010). Project Management,From Design to Implementation. Nairobi: Richmond Designers and Printers.
30. Lester, A. (2000). Project Planning and Control. Oxford: Butterworth Heinemann.
31. Lindahl, G. (2007). Client`s goals and the Construction Project Management Process. Journal of Construction Management and Economics.
32. Ling, F. (2009). How Project Managers can better control the performance of design build projects. International Journal of Project Management, 22(6),477-488.
33. Masu, S. (2006). An investigation into the causes and impact of resource mix practices in the performance of construction firms in Kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.
34. Morris, S. (1990). Cost and Time Overruns in Public Sector Projects.
35. Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.
36. Munano, A. (2012). Pre-constrcution Planning: Exploring the factors that influence timelines of project completion for public sectors buildings in Kenya.
Unpublished Master of Construction Management Thesis.Jomo Kenyatta University.
37. Musa, G. (1999). Determination of Factors Influencing Projects Delays in Water Projects in Kenya: The case of Government Funded Projects. Nairobi: Unpublished MBA Thesis University of Nairobi.
38. Mwandali, D. (1996). Analysis of Major Factors that affect Projects Management: A Case of Kenya Railways Projects. Nairobi: Unpublished MBA Thesis,University of Nairobi.
39. Nguyen, A. (2004). A study on Project success factors in large construction projects in Vietnam.
40. Nicholas, J. (2001). Project Management for Business and Technology. New Jersey: Prentice Hall.
41. Olawale, Y. a. (2010). "Cost and time control of construction projects: Inhibitng factors and mitigating measures in practice". Construction Management and Economics, 28 (5),509-526.
42. Pellicer, E. (2005). Cost control in Consulting engineering firms. Journal of Management in Engineering, 21 (4),189-192.
43. Project Management Institute. (2013). PMBOK: A guide to the Project Management Book of Knowledge. Project Management Institute.
44. Rozenes, S. (2006). "Project Control: Literature review". Project Management Journal, 37(4) 4-14.
45. Samuelson, W. (2006). Managerial Economics.5th Edition. New Jersey: John Wiley & Sons.
46. Talukhaba, A. (1998). Time and Cost Performance of Construction Projects. Nairobi: Unpublished M.A.Thesis,University of Nairobi.
47. Tucker, L. A. (1987). Is Construction Project planning really doing its job?.A critical focus,role and progress in the construction management economic. Vol 5,243-266.
48. Wanyona, G. (2005). Risk Managment in the cost planning and control of building projects.The case of quantity Surveying profession in Kenya. Unpublished PhD Thesis.University of Cape Town.
49. White, D. F. (2002). Current practice in project management-An Emperical study. International Journal of Project Management, 20(2),1-11.
50. Yakubu, O. a. (2009). Cost and time control of construction projects: A survey of Contractors and Consultants. Construction Information Quarterly, , 11(2),53-59.
51. Zhen Yu, Z. (2010). Application of innovative Critical Chain Method for project planning and control. Journal of Construction Engineering and Management.

56-63

11.

Authors:

Peter Mwangi Njogu, Alkizim Ahmad, Abednego Gwaya

Paper Title:

Identifying Key Risk Influencing Project Delivery in Kenya from Contractors’ Perspective

Abstract: The construction industry is crucial in the country’s economy growth. The Kenyan construction industry has been contributing immensely towards the Gross Domestic Product (GDP). The statistics by the Kenya Bureau Statistics (Republic of Kenya, 2014), indicate that the industry contributed 4.2%, 4.1%, 4.2%, and 4.4% towards the Gross Domestic Product (GDP) for the years 2010, 2011, 2012 and 2013 respectively. Despite this praise, studies in recent years have shown poor delivery of construction projects in relation to project objectives. This has been attributed to the many risks inherent in the industry (Ehsan et al., 2010). This has provoked an increased interest into the need for risk management in the industry. The main objective of this study was to determine the key construction risk which affects construction project delivery in Kenya in terms of cost, time, quality, environmental sustainability, and health and safety from contractors’ perspective. Response measures to these risks are believed shall enhance project delivery among contractors.This study was conducted through a review of existing literature and through self-administered questionnaires. The study targeted contractors registered in Kenya by the National Construction Authority (NCA). A sample of 190 respondents was selected through stratified random sampling to participate in this study. Sixteen (16) of the respondents were from class NCA 1, 12 from class NCA 2, 22 from class NCA 3, 74 from class NCA 4 and 66 from class NCA 5. Senior managers, project managers, technical managers, architects, quantity surveyors and engineers working with the contractors constituted the sample units for this study. Ninety eight (98) valid questionnaires were returned.The study assessed the likelihood of occurrence of risks and their impact on project objectives in terms of cost, time, quality, environment and health and safety; ranked the risks depending on their significance index score thus determined the key risks. Statistical package for social science (SPSS) analysis software was used to analyze data collected for the purpose of interpretation and conclusions. Descriptive statistic was applied where some measures of distribution, central tendency and dispersion were used. Findings were presented using descriptive statistical tools like tables and radar diagram. Based on a comprehensive assessment of risk probability and impact on the project objectives, 26 key risk factors were identified and ranked. Project time and cost were found to be the project objectives most vulnerable to construction risk. “Delay in payments” had the highest level of impact on both time and cost having a Risk Significance Index Score (RSIS) of 0.5849 and 0.5514 respectively. The second ranked risk was “excessive approval procedures in administrative government departments” The risk had a major impact on both time and cost at RSIS of 0.5641 and 0.5000 respectively. “Information unavailability-details, drawings, sketches” is the third ranked risk. Revised Version Manuscript Received on August 12, 2015. Njogu Peter Mwangi, Masters Student- Construction Project Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya. Ahmad Alkizim, Senior Lecturer- Construction Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya Gwaya Abednego, Lecturer- Construction Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi, Kenya. The risk has a significant impact on project quality having RSIS of 0.5188 and its highest impact on project time having RSIS of 0.5527. “Design variations required by clients” was found to have high impact on both time and cost having RSIS of 0.5474 and 0.5322 respectively. The findings of this study shall be useful not only to contractors but also consultants and policy makers in the construction industry in managing construction risks thereby improving project delivery in Kenya.

Keywords: risk, risk management, construction projects objectives, contractors’ perspective

References:
1. Aibinu, A.A. & Jagboro, G.O. (2002). The Effects of Construction Delays on Project Delivery in Nigeria Construction Industry. International Journal of Project Management, 20(4), 593-599.
2. Al-Bahar, J. & Crandall, K. (1990). Systematic Risk Management Approach for Construction Projects. ASCE Journal of Construction Engineering and Management, Vol. 116, No 3, pp. 533-546.
3. Chileshe, N. & Yirenki-FiankoA.B. (2011). Perceptions of Threat Risk Frequency and Impact on Construction Projects in Ghana: Opinion Survey Findings. Journal of Construction in Developing Countries Vol. 16(2), 115-149
4. Cochran, W. G. (1963). Sampling Techniques, 2nd edition. New York: John Wiley and Sons, Inc.
5. Deviprasadh, A. (2007). Risk Assessment and Management in construction projects. M.E. Construction Engineering and Management Thesis report. Anna University, Chennai.
6. Ehsan, N., Mirza, E., Alam, M., & Ishaque, A. (2010). Risk management in construction industry. In Computer Science and Information Technology (ICCSIT), 2010
3rd IEEE International Conference on (Vol. 9, pp. 16-21). IEEE.
7. Gichunge, H. (2000). Risk Management in the Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi
8. Hayes, R.W., Perry, J. G. Thompson. P.A. & Wilmer. G. (1986). Risk Management in Engineering Construction: A guide to project risk analysis and risk management. SERC Report. Thomas Telford. London.
9. Jomaah, I., Bafail, A., & Abdulaal, R. (2010). Identifying, assessing, and managing risks affecting the construction projects in King Abdulaziz University. Vice Presidency of KAU for Projects, Research project No.: 429/88.
10. Kishk, M. & Ukaga, C. (2008). The impact of effective Risk Management on project success. In: A. DAITY, ed. Proceedings of the 24th Annual ARCON Conference 1-3 September 2008 London: ARCON. Pp. 799 – 808
11. Kothari, C. R. (2004). Research Methodology; Methods & Techniques, New Age International Publishers, New Delhi, India.
12. Mahendra, P. A., Jayeshkumar R. P. & Bhavsar J. J. (2013). A Study of Risk Management Techniques for Construction Projects in Developing Countries, International Journal of Innovative Technology and Exploring Engineering (IJITEE) Volume-3, Issue-5
13. Mark W., Cohen P.E., Glen R.P. (2004). Project Risk Identification and Management, AACE International Transactions, INT. 01, 1-5
14. Mbatha, C. M. (1986). Building contract performance, A case study of Government Projects in Kenya. Unpublished M.A. Thesis. University of Nairobi.
15. Mousa J. H. A. (2005). Risk Management in Construction Projects from Contractors and Owners’ perspectives. Unpublished MSc. Thesis. The Islamic University of Gaza-Palestine.
16. Msafiri A. S. (2015). An Investigation into Factors Causing Delays in Road Construction Projects in Kenya. American Journal of Civil Engineering. Vol. 3(3), 51-63
17. Panthi, K., Ahmed, S. M. & Azhar, S. (2007). Risk Matrix as a Guide to Develop Risk Response Strategies, ASC Proceedings of the 43rd Annual Conference, Northern Arizona University, Flagstaff, Arizona, April 12-14.
18. Project Management Institute (PMI) (2013). Project Management Body of Knowledge (PMBOK), 5th ed. USA: Project Management Institute, Inc.
19. Republic of Kenya (2014). Economic Survey 2014, Nairobi, Central Bureau of Statistics, Ministry of Finance, Government Printer.
20. Rubin, A., & Babbie, E. (2009). Essential research methods for social work, (2nd ed.).Belmont, CA: Brooks/Cole Publishing Co.
21. Shebob, A., Dawood, N. & Xu, Q. (2011). Analyzing construction delay factors: A srudy of building construction project in Libya in: Egbu,C, and Lou, E,C,W, (Eds) Procs 27th Annual ARCOM Conference, 5-7 September 2011, Bristol,UK, Association of Researchers in Construction Management, 1005-1012.
22. Shen L. Y., Wu G.W.C. & Ng, C.S.K. (2001). Risk assessment for Construction Joint Ventures in China, Journal of Construction Engineering and Management, 127(1), 76-81
23. Smith, N.J. & Merna, T. and Jobling, P. (2006). Managing risk in construction projects. Blackwell Science Ltd, Oxford.
24. Talukhaba, A. A. (1999). An Investigation into Factors Causing Construction Delays in Kenya: A case study of High Rise Building Projects in Nairobi. Unpublished PhD. Thesis. University of Nairobi
25. Tipili, G.L. & IIyasu, M.S. (2014). Evaluating the impact of risk factors on construction projects cost in Nigeria. The Internationaljournal of Engineering and Science (IJES). Vol.3 (6) pp. 10-15
26. Zayed T., Amer M., Pan J. (2008). Assessing Risk and Uncertainty Inherent in Chinese Highway Projects Using AHP. The international Journal of Project Management 26 (4), 408 - 419
27. Zou, P., Zhang, G., & Wang, J.Y. (2006). Identifying key risks in construction projects: Life cycle and stakeholder perspectives, Proc. 12th Pacific real estate society conference. Auckland, New Zealand, 22-25 January.

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12.

Authors:

Abubakar S. Umar, Muntaqa D. Alhassan, Kabiru Aminu, Salahuddeen G. Ahmad

Paper Title:

Modelling and Control of Dynamical Systems Using Neural Network – A Review

Abstract: This paper presents a brief review on how artificial neural networks can be used in modelling and control of dynamical systems. The paper is broadly categorized into two; the first part is a short overview on artificial neural networks, particularly its generalization property, as applied to systems identification. The subsequent part contains a review onsome of the typical approaches used in the control of dynamical systems using neural networks which includes model predictive control, NARMA-L2 Control and model reference control. Finally, a comparative conclusion was made to distinguish the performances of the different control methods presented in this paper.

Keywords: Neural Network Controllers; Generalization; Systems Modelling; Control Systems

References:
1. P.J. Antsaklis,'Neural Networks in Control Systems,’ The IEEE Control Systems Magazine, Vol.10, No.3, pp.3-87, April 1990.
2. Soloway, D. and P.J. Haley, ‘Neural Generalized Predictive Control,’ Proceedings of the 1996 IEEE International Symposium on Intelligent Control,277-281 (1996).
3. Narendra, K.S. and S. Mukhopadhyay, ‘Adaptive Control Using Neural Networks and Approximate Models,’ IEEE Transactions on Neural Networks, vol. 8, 475-485 (1997).
4. Narendra, K.S. and K. Parthasarathy, ‘Identification and Control of Dynamical Systems Using Neural Networks,’ IEEE Transactions on Neural Networks, vol. 1, 4-27 (1990).
5. Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd Ed., Prentice-Hall, Englewood Cliffs, NJ, 1999.
6. Zurada, J.M., Introduction to Artificial Neural Networks, 2nd Ed., West Publishing Company, 1992.
7. Hagan, M. T. and H.B. Demuth, ‘Neural Networks for Control,’ Proceedings of the 1999 American Control Conference, San Diego, CA, 1642-1656 (1999).
8. Hunt, K.J., D. Sbarbaro, R. Zbikowski and P.J. Gawthrop, ‘Neural Networks for Control System - A Survey,’ Automatica, vol. 28, 1083-1112 (1992).
9. K ˚ urkov´ a, “Approximation of functions by perceptron networks with bounded number of hidden units”, Neural Networks, vol. 8, pp. 745–750, 1995.
10. Hagan, M.T., Demuth, H.B., Beale, M.H., ‘Neural Network Design,’ 2nd Ed., Campus Publication Service, University of Colorado Bookstore, 2002.
11. Hagan, M.T., Demuth, H.B., O. De Jesus, ‘An Introduction to the Use of Neural Networks in Control Systems,’ International Journal of Robust and Nonlinear Control, John Wiley & Sons, 2002
12. Hagan, M. T., O. De Jesus, and R. Schultz, ‘Training Recurrent Networks for Filtering and Control,’ Chapter 12 in Recurrent Neural Networks: Design and Applications, L. Medsker and L.C. Jain, Eds., CRC Press, 311-340 (1999).
13. Pham, D. T. and X. Liu, ‘Neural Networks for Identification, Prediction, and Control,’ Springer-Verlag, New York, 1995.
14. Omatu, S., M. B. Khalid, R. Yusof, ‘Neuro-Control and its Applications,’ Springer-Verlag, London, 1996.
15. Omidvar, O. and D. Elliott,Neural Systems for Control, Academic Press, New York, 1997.
16. Norgard, M., O. Ravn, N.K. Poulsen, and L.K. Hansen,Neural Networks for Modelling and Control of Dynamic Systems, Springer-Verlag, London, 2000.
17. Liu, G.P., ‘Nonlinear Identification and Control,’ Springer-Verlag, London, 2001.

73-76

13.

Authors:

Harpreet Singh

Paper Title:

Robust Video Watermarking Algorithm Using K-Harries Feature Point Detection

Abstract: In this Paper, An effective, robust and imperceptible video watermarking algorithm using K–harris point detection is proposed. The performance of the proposed algorithm was evaluated with respect to imperceptibility, robustness and data payload. This algorithms showed similar but high level of imperceptibility, however their performance varied with respect to robustness and payload. This paper presents a content-based digital image-watermarking scheme, which is robust against a variety of common image-processing attacks and geometric distortions. The image content is represented by important feature points obtained by our image-texture-based adaptive Harris corner detector. These important feature points are geometrically signficant and therefore are capable of determining the possible geometric attacks with the aid of the Delaunay-tessellation-based triangle matching method

Keywords: Robustness, Feature point, video watermarking, Bit error rate(BER

References:
1. F. Peticolas, R. Anderson, M. Kuhn, Attacks on copyright marking systems, in: Proceedings of the Second Workshop Information Hiding, Portland, OR, April 1998, pp. 218–238.
2. S. Pereira, J.J.K. O’Ruanaidh, F. Deguillaume, G. Csurka, T. Pun, Template based recovery of Fourier-based watermarks using log-polar and log-log maps, in: Proceedings of the IEEE International Conference Multimedia Computing Systems, vol. 1, Florence, Italy, June 1999, pp. 870–874.
3. S. Pereira, T. Pun, Robust template matching for affine resistant image watermarks, IEEE Trans. Image Process. 9 (6) (2000) 1123–1129.
4. Digimarc Corporation, US patent 5,822,436, Photographic Products and Methods Employing Embedded Information.
5. J.J.K. O’Ruanaidh, T. Pun, Rotation, scale, and translation invariant digital image watermarking, in: Proceedings IEEE International Conference Image Processing, Santa Barbara, CA, 1997, pp. 536–539.
6. J.J.K. O’Ruanaidh, T. Pun, Rotation, scale, and translation invariant spread spectrum digital image watermarking, Signal Process. 66 (3) (1998) 303–317.
7. D. Zheng, J. Zhao, A. El Saddik, RST-invariant digital image watermarking based on log-polar mapping and phase correlation, IEEE Trans. Circuits Syst. Video Technol. 13 (8) (2003) 753–765.
8. C.Y. Lin, M. Wu, J.A. Bloom, I.J. Cox, M.L. Miller, Y.M. Lui, Rotation, scale, and translation resilient watermarking for images, IEEE Trans. Image Process. 10 (5) (2001) 767–782.
9. M. Alghoniemy, A.H. Tewfik, Geometric distortion correction through image normalization, in: Proceedings of IEEE International Conference Multimedia Expo, vol. 3, 2000, pp. 1291–1294.
10. M. Alghoniemy, A.H. Tewfik, Image watermarking by moment invariants, in: Proceedings of IEEE International Conference Image Processing, vol. 2, Janual 2000, pp. 73–76.
11. M. Alghoniemy, A.H. Tewfik, Geometric invariance in image watermarking, IEEE Trans. Image Process. 13 (2) (2004) 145–153.
12. H.S. Kim, H.K. Lee, Invariant image watermark using Zernike moments, IEEE Trans. Circuits Syst. Video Technol. 13 (8) (2003) 766–775.
13. Y. Xin, S. Liao, M. Pawlak, Geometrically robust image watermarking via pseudo-Zernike moments, in: Proceedings of the Canadian Conference Electrical and Computer Engineering, vol. 2, May 2004, pp. 939–942.
14. Frank Hartung, Jonathan K. Su and Bernd Girod: Spread Spectrum Watermarking: Malicious Attacks and Counterattacks. Security and Watermarking of Multimedia Contents, 1999.
15. Apple - QuickTime - HD Gallery, http://www.apple.com/quicktime/guide/hd/
16. P. Bas, J.M. Chassery, B. Macq, Geometrically invariant watermarking using feature points, IEEE Trans. Image Process. 11 (9) (2002) 1014–1028.
17. C. Harris, M. Stephen, A combined corner and edge detector, in: Proceedings of Fourth Alvey Vision Conference, Manchester, 1988, pp. 147–151.
18. H. Moravec, Obstacle avoidance and navigation in the real world by a seen robot rover, Robotics Institute, Carnegie- Mellon Univ., Pittsburgh, PA, Tech. Rep. CMU-RI-TR-3, September 1980.
19. Deepa Satish Khadtare(2011), International Journal of Advanced Engineering Research and Studies, A robust video watermarking appror oach for raw video and it’ DSP implementation,pp 1-6

77-83

14.

Authors:

Basma M. Hassan, Khaled M. Fouad, Mahmoud F. Hassan

Paper Title:

Hardware Implementation of Dynamics Keystroke Applied for Cloud Computing

Abstract: Cloud computing is a growing technology which provides remote access to computing resources and user data. Due to its core philosophy of enabling the user to access his data from anywhere and at any time, cloud computing has a major issue with security and user authentication. Biometric identification is a very good candidate technology, which can facilitate a trusted user authentication with the minimum constraints on the security of the access point. However, most of the biometric identification techniques require special hardware, thus complicate the access point and make it costly. Keystroke recognition is a biometric identification technique which relies on the user behavior while typing on the keyboard. It is more secure and does not need any additional hardware to the access point. This paper presents a hardware implementation of an algorithm based on keystroke dynamics analysis synthesized, simulated and implemented on FPGA. The authentication process is based on the GP methods to test the ability of the distance measure between keystrokes and how to distinguish users through their typing dynamics keystroke.The proposed architecture achieves maximum delay 0.55 ns

Keywords: Cloud computing, remote access, biometric identification, access point, Keystroke recognition, FPGA, VHDL.

References:
1. N. Antonopoulos, and L. Gillam, “Cloud Computing Principles,” Systems and Applications springer -Verlag London Limited, 2010
2. K. Jeffery, B. Neidecker,"the future of cloud computing," Expert Group Report Public Version 1.0, opportunities for European cloud computing beyond, 2010
3. Cloud Standards Customer Council: Practical Guide to Cloud Computing, Version 2.0, 2014
4. S. Teh, A. Teoh, and S. Yue," A survey of Keystroke Dynamics Biometrics," Hindawi Publishing Corporation, The Scientific World Journal, Volume Article ID 408280,
24 (2013)
5. V.Paranjape andV.Pandey,"An Improved Authentication Technique with OTP in Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol-1, Issue-3, 2013
6. Emam,"Additional Authentication and Authorization using Registered Email-ID for Cloud Computing,"International Journal of Soft Computing and Engineering ISSN: 2231-2307, Volume-3, Issue-2 (2013)
7. H. Chang and E. ChoiKim," User Authentication in Cloud Computing," UCMA Part II, CCIS 151, pp. 338–342. , 2011
8. M. Kim,H. Jeong, and E. Choi," Context-aware Platform for User Authentication in Cloud Database Computing," International Conference on Future Information Technology and Management Science & Engineering, Vol.14, pp. 170-176, 2012
9. Cloud Computing,(2006), Seminar Report and PPT, available athttp://www.seminarsonly.com/computer%20science/Cloud Computing.php
10. Babich," Biometric Authentication," Types of biometric identifiers Bachelor’s Thesis Degree Programme in Business Information Technology , 2012
11. S. Rupinder and R. Narinder," comparison of various biometric methods,"international Journal of Advances in Science and Technology (IJAST), ISSN 2348-5426.Vol 2 Issue I, March 2014
12. Monrose,and D. Rubin, "Keystroke Dynamics as a Biometric for Authentication," Preprint submitted to Elsevier Preprint,1999
13. A., Messerman, Mustafic, T., Camtepe, S., and Albayrak, S.: Continuous and non-intrusive identity verification in real time environments based on free-text keystroke dynamics, Int’l Joint Conf. on Biometrics (IJCB), 2011
14. Peacock, X. K, and M. Wilkerson," Typing patterns: A key to user identification," IEEE Security and Privacy, 2(5):40–47, 2004
15. M. Kaur, and R. Virk, “Security System Based on User AuthenticationUsing Keystroke Dynamics,”International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 5, May 2013, pp 2111
16. Bergadano,D. Gunetti and C. Picardi," User authentication through keystroke dynamics," ACM Transactions on Information and System Security (TISSEC) Volume 5 Issue 4, Pages 367-397, 2002
17. P. Ashenden,"the VHDL cookbook the first addition.
18. Xilinx: Spartan-3 Generation Configuration User Guide. Extended Spartan-3A, Spartan-3E, and Spartan-3 FPGA Families UG332 (v1.7) January 27, 2015
19. MicroBlaze Development Kit Spartan-3E 1600E Edition User Guide. December 5, 2007
20. M. Babaeizadeh, M. Bakhtiari and M. Maarof, "Keystroke Dynamic Authentication in Mobile Cloud Computing,"International Journal of Computer Applications (0975 – 8887) Volume 90 – No 1, March 2014
21. S. Prabhakar, S. Pankantiand K. Jain," Biometric Recognition," Security and Privacy Concerns, IEEE Security and Privacy Magazine, Vol. 1, No. 2, pp. 33-42, 2003
22. Giacometto, M. Vilardy, C. O. Torres, and L.Mattos,"Template characterization and correlation algorithm created from segmentation for the iris biometric authentication based on analysis of textures implemented on a FPGA,"IOP Publishing Journal of Physics, 2011
23. Y. Wakil, S. Gul Tariq, A. Humayun, and N.Abbas, "An FPGA based Minutiae Extraction System for Fingerprint Recognition,"International Journal of Computer Applications (0975 – 8887),Volume 111 – No 12, February 2015
24. S. Gayathri, Dr. V. Sridhar," An Improved Fast Thinning Algorithm for Fingerprint Image,"International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 1, January 2013
25. R. Fatt, Y. Tay, and K. Mok,"Iris Verification Algorithm Based on Texture Analysis and its Implementation on DSP," Int. Conf. on Signal Acquisition and Processing DSP ISBN: 978-0-7695-3594-4, 2009
26. R. Kannavaraand N. Bourbakis,"Iris Biometric Authentication based on Local Global Graphs, An FPGA Implementation," IEEE Proc. Symp. On Computational Intelligence for Security and Defense Applications ISBN: 978-1-4244-3763-4,2009
27. Poinsot, Y. Fan and V. Brost," Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system," HAL Id: hal-00640727, available: https://hal.archives-ouvertes.fr/hal-00640727 Submitted on 14 Nov 2011
28. J. Liu, R. Sanchez, A. Lindosoand O. Hurtado, "FPGA Implementation for an Iris Biometric," Processor IEEE Int. Conf. on Field Programmable Technology ISBN: 0-7803-9729-0, 2006
29. R. Rakvic, B. Ulis, R. Broussard, and R. Ives, "Parallelizing Iris Recognition," IEEE Trans. On Information Forensics and Security vol. 4 no. 4 ISCN: 1556-6013, 2009
30. Vijayalami, B.Obulesu, "Hardware Implementation of Face Detection Using ADABOOSTAlgorithm,"journal of Electronics and Communication Engineering (IOSRJECE) ISSN: 2278-2834 Volume 1, Issue 2, May-June 2012.
31. Zhao, X. , and Xie, M.: A Practical Design of Iris Recognition System Based on DSP Int. Conf. on Intelligent Human-Machine Systems and Cybernetics ISBN: 978-0-7695-3752-8 , 2009
32. M. Vatsa, R. SinghandA. Noore," Improving Iris Recognition Performance Using Segmentation," Quality Enhancement, Match Score Fusion, and Indexing IEEE Trans, On Systems, Man, and Cybernetics Part B: Cybernetics Vol. 38 NO. 4 ISCN: 1083-4419, 2009
33. Z. Hu, and M. Xie," Iris Biometric Processor Enhanced Module," FPGA-based Design Proc, Second International Conference on Computer Modeling and Simulation 259-62 , 2010
34. http://jsfiddle.net/qLap9/355/Created by the paper group

84-89

15.

Authors:

Tivisha Goel

Paper Title:

Rule base-Disturbance Estimation Based Fault Diagnosis for Grid Connected PV System

Abstract: The paper contains a novel online fault diagnosis for distribution feeder with photovoltaic (PV) generation embedded. The aim of the research is to isolate and prevent system faults in Grid connected PV System. Firstly, a dynamic model of distribution feeder for fault diagnosis is built. Design of proposed fault diagnosis has two stages: firstly, an Equivalent Input Disturbance (EID) approach for the fault estimation is formulated; and Fuzzy knowledge base system is designed for analyzing the characteristics of the EID. The faults position can be located and the fault types can be identified. In this study Fault Diagnosis technique obtains fault detection, identification and halting the system. In the meantime graphical user interface (GUI) is opened when fault is detected. GUI shows the measurement value, fault time and fault type. This property gives some information about the system to the personnel. As seen from the simulation results, faults can be detected and identified as soon as fault appears. In summary, if the system has a fault diagnosis structure, system dangerous situations can be avoided.

Keywords: Fault Detection, Distribution network, PV System, Fuzzy Logic, User Interface

References:
1. F. Zidani, D. Diallo, M. El H. and R. Nait-Said, "A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive," IEEE Trans. Ind. Electron., vol. 55, pp. 586-593, Feb. 2008.
2. Sreedevi, M., and P. Jeno Paul. "Fuzzy PI controller based grid-connected PV system." International Journal of Soft Computing 6(1), (2011): 11-15.
3. Sivakumar, R., and N. Suthanthiravanitha. "Grid-Connected PV System for PID Controller Using MATLAB." IUP Journal of Electrical & Electronics Engineering3.4
(2010).
4. Dell'Aquila, R. V., L. Balboni, and R. Morici. "A new approach: modeling, simulation, development and implementation of a commercial grid-connected transformer less PV inverter." 2010 International Symposium on Power Electronics Electrical Drives Automation and Motion (SPEEDAM),.IEEE, 2010.
5. J.H. She, M.X. Fang, Y. Ohyama, H. Hashimoto, and M. Wu," Improving Disturbance-Rejection Performance Based on an Equivalent-Input- Disturbance Approach”, IEEE Trans. on Industrial Electronics, vol. 55, no. 1, Jan. 2008.
6. Manikandan, Pandiyan and Mani Geetha. "Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system."Archives of Control Sciences 24, no. 3,Pages 271–287 (2014).
7. J.H. She, Y. Ohyama, and M. Nakano, "A new approach to the estimation and rejection of disturbances in servo systems," IEEE Trans. Control Syst. Technol., vol. 13, no. 3, pp. 378–385, May 2005.
8. Geetha, M.; Manikandan, P.; Jerome, J., "Soft computing techniques based optimal tuning of virtual feedback PID controller for chemical tank reactor," Evolutionary Computation (CEC), 2014 IEEE Congress on , vol., no., pp.1922,1928, 6-11 July 2014
9. A.M. El-Zonkoly, A. A. Khalil and N.M. Ahmed, “Optimal tuning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization”, Expert Systems with Applications, vol. 36, no. 2 PART 1, 2009, pp. 2097-2106.
10. D. T. Pham, A. Soroka, A. Ghanbarzadeh, E. Koç, S. Otri and M. Packianather, “Optimising neural networks for identification of wood defects using the Bees Algorithm”, In: Proc. of the IEEE Int. Conf. on Industrial Informatics, Singapore, 2006, pp. 1346-1351.
11. Manikandan P, Geetha M, Jubi K, Hariprasath P and Jovitha Jerome, ”Performance Analysis and Control Design of Two Dimension Fuzzy PID Controller”, International Journal of Electrical Engineering and Technology, Vol.4, Issue 5, pp. 47-55, 2013.
12. S. Naidu, E. Zafirou, T.J. McAvoy, Use of neural networks for failure detection ina control system, IEEE Control Syst. Mag., Vol.10, pp. 49–55,1990.
13. Kumagai, T. Liu, P. Hozian, Control of shape memory alloy actuators with a neuro-fuzzy feed forward model element, J. Intell. Manuf. 1 (2006)45–56.
14. Manikandan P, Geetha M, Jubi K, Jovitha Jerome., “Fault Tolerant Fuzzy Gain Scheduling Proportional-Integral-Derivative Controller for Continuous Stirred Tank Reactor”, Aust. J. Basic & Appl. Sci., 7(13), pp.84-93, 2013

90-94

16.

Authors:

Mark Obegi Kenyatta, Ahmad Omar Alkizim, Titus Kivaa Mbiti

Paper Title:

Recapitulating The Payment Default Effects to Contractors in The Kenyan Construction Industry

Abstract: Cash flow is undoubtedly the bloodline that drives enterprise in the construction industry. Any interference in its smooth flow may therefore lead to severe consequences. “Work first and get paid later”, is the motto of the construction industry. This arrangement inevitably demands the input of several parties in the form of labour, materials, plant and other trade credits as the work progresses. Further, the end product becomes part and parcel of the land, whose legal possession lies squarely with the construction client. The sweat and pain of unpaid parties in the construction pyramid are therefore left at a disadvantaged position as they chase for their claims. This paper recapitulates the effects resulting from payment default to contractors from their construction clients in Kenya. Content analyses of payment dispute cases lodged in the commercial division of the Kenyan courts as well as the standard contracts were employed. The study finds that payment default in the form of late payment of one or several certificates, underpayment or paying in installments and nonpayment have led to cash flow hardships to contractors and their lower tier parties, late completion of projects, construction disputes and even insolvency. To mitigate on these impacts, this paper proposes that the industry players consider legislating on a payment specific regime just like it has happened in other countries.

Keywords: Payment default, contractors, construction industry of Kenya.

References:
1. Abdul-Rahman, H., Wang, C., Takim, R., & Wong, S. (2011). Project schedule influenced by financial issues:Evidence in construction industry. Scientific Research
and Essays, 6(1), 205-212. Retrieved 12 5, 2013, from http://www.academicjournals.org/SRE
2. Abidin, A. (2007). The Profiles of Construction Disputes. KualaLumpure: Unpublished Masters Thesis.
3. Abidin, A. B. (2007). The Profile of Construction Disputes. Kulalumper: Unpublished Masters Thesis.
4. Africa Building. (2013). The challenges of the construction industry in Kenya: Delays in construction projects. Retrieved 2013 йил 30-November from http://www.africanbuilding.com
5. Ali, A. S., Smith, A., Michael, P., & Choon, H. (2010). Contractors' perception of factors contributing to project delay : case studies of commercial projects in Klang Valley, Malaysia. Journal of Design and Built Environment, 7, 43-57. Retrieved 1 21, 2015, from fbe.um.edu.my/images/fab/Files/JDBEVOL7/vol7-04.pdf
6. Ameer, A. N. (2006 йил 3rd Quarter). A “Construction Industry Payment and Adjudication Act: Reducing Payment-Default And Increasing Dispute Resolution Efficiency In Construction. Master Builders Jounal, 3-14.
7. Ansah, S. K. (2011). Causes and Effects of Delayed Payments by Clients on Construction Projects in Ghana. Journal of Construction Project Management and Innovation, 1(1), 27 - 45.
8. Ashworth, A. (2012). Contarctual Procedures (6 ed.). Pearson Education Limited.
9. Aziz, R. F. (2012). Optimizing strategy for repetitive construction projects within multi-mode resources. Alexandria Engineering Journal(52), 67-81.
10. Aziz, R. F. (2013). Ranking of delay factors in construction projects After Egyptian revolution. Alexandria Engineering Journal, 52, 387-406.
11. Bryman, A. (2012). Social research methods (4 ed.). New York: Oxford University Press.
12. Business Daily. (2013, November 26). Ministry to halt new road projects over unpaid bills. Ministry to halt new road projects over unpaid bills. (E. MUTAI, Ed.) Nairobi, Nairobi, Kenya: Business Daily. Retrieved May 29, 2014, from http://www.standardmedia.co.ke/mobile/%3FarticleID%3D2000101541%26story
13. Cantor, A. (2014). Put all construction industry knowledge in one place:Insolvency. Retrieved 8 23, 2014, from http://www.designingbuildings.co.uk/wiki/Insolvency
14. CBK. (2015, 5 23). Banking Sector Performance and Developments Report - 1st Quarter of 2015. Performance and developments in the Kenyan banking sector for the quarter ended 31st March 2015. Retrieved 5 23, 2015, from https://www.centralbank.go.ke/
15. Chitkara, K. K. (2011). Construction Project Management - Planning,Scheduling & Control (2 ed.). New Delhi: Tata McGraw-Hill.
16. CIDB. (2010). Delayed Payments in the Construction Industry. Johannesburg: Construction Industry Development Board of Sourth Africa.
17. cidb. (2013). Subcontracting in the South African Construction Industry; Opportunities for Development. Pretoria.
18. Cunningham, T. (2013 йил 1-March). Will the construction contracts bill improve subcontractor cash-flow? Retrieved 2013 йил 1-December from http://arrow.dit.ie/beschreoth/10
19. D. Manji Construction Limited v C & R Holdings Limited (2014).
20. Danuri, M. S., Munaaim, C. M., Rahman, A. H., & Hanid, M. (2006). Late and Non Payment Issues in the Malaysian Construction Industry-A Contractor's Perspective. Sustainable Development through Culture and Innovation, The Joint International Conference on Construction Culture, Innovation and Management (CCIM), 613-623.
21. Fleming, Q. W., & Koppelman, J. M. (2008). Performance based payments (PBPs). PM World Today, 10(4).
22. Fong, L. C. (2005). The Malaysian Construction Industry - The Present Dilemmas of Unpaid Contractors. Master Builders Jounal, pp. 80-82.
23. Fugar, F. D., & Agyakwah-Baah, A. B. (2010). Delays in Building Construction Projects in Ghana. Australasian Journal of Construction Economics and Building,
10(1/2), 103-116.
24. Jaffar, A., Tharim, A. A., & Shuib, M. N. (2010). Factors of Conflict in Construction Industry: A Literature Review. The 2nd International Building Control Conference 2011. 20, pp. 193-202. Elsevier Ltd. Retrieved 12 20, 2014, from www.sciencedirect.com
25. Kikwasi, G. J. (2012). Causes and effects of delays and disruptions in construction projects in Tanzania. Australasian Journal of Construction Economics and Building, 1(2), 52-59.
26. Kimani, Z. A., & Kimwele, M. (2015). Factors influencing project delays in Kenya. A case study of national Housing Corporation. International Journal of Social Sciences Management and Entrepreneurship, 1, 1-14.
27. KNBS. (2012). Economic Survey Highlights. Nairobi: KNBS.
28. Kundan Singh Construction International Limited v Bank of Africa Kenya Ltd & another (2015).
29. Latham, M. (1994). Constructing the Team. UK: HMSO.
30. Love, P., Davis, P., & Ellis, J. (2010). Dispute causation: identification of pathogenic influences in construction. Engineering, Construction and Architectural Management, 17(4), 404-423.
31. Mahamid, I. (2014). Micro and macro level of dispute causes in residential building projects:Studies of Saudi Arabia. Journal of King Saud University – Engineering Sciences, 3(2).
32. Maritz, M. J., & Robertson, D. C. (2012). What are the legal remedies available to contractors and consultants to enforce payment? Journal of the South African Institution of Civil Engineering, 54(2), 27-35.
33. Marx, H. J. (2012). Construction Industry Indicators Affecting Contractors. Journal for the Advancement of Performance Information and Value, 4(1), 119-132.
34. Marx, H. J. (2014). Results of the 2014 Survey of the CIDB Construction Industry Indicators. University of the Free State.
35. Mbachu, J. (2011). Sources of contractor's payment risks and cashflow problems in the New Zealand construction industry:Project team's perceptions of the risks and mitigation measures. Construction Management and Economics, 29(10), 1027-1041.
36. Mbiti, T. K. (2008). A System Dynamics Model of Construction Output in Kenya. Melbourne: Unpublished PHD Thesis.
37. Mofokeng, T. G. (2012). Assessment of the Causes of Failure among Small and Medium sized Construction Companies in Free State Province. Johannesburg: Unpublished Masters Thesis.
38. Muigua, K. (2011). Dealing with Conflicts in Project Management - A Paper Presented at the Continuous Professional Development Workshop for Architects and Quantity Surveyors. Nairobi. Retrieved 12 15, 2014, from www.chuitech.com/kmco/attachments/article/96/
39. Murdoch, ,. J., & Hughes, W. (2008). Construction Contracts: Law and Management (4 ed.). London: Taylor and Francis.
40. Mururu, N. (2011). The Quantity Surveyor and Construction Claims. the Quantity Surveyor, 13(003), 10-15.
41. NESC. (2014). Credit gurantee schemes: The road to expanding business and investiment in Kenya. Nairobi: National Economic and Social Council with support fro USAID Kenya.
42. Potts, K. (2008). Construction Cost Management - Learning From Case Studies (1 ed.). New York: Taylor & Francis.
43. PPOA. (2006). Public Procurement Oversight Authority. Retrieved 2013 йил 30-November from http://www.ppoa.go.ke
44. Prism. (2013). The Need for Prompt Payment Legislation in the Construction Industry. Ontario: Reed Elsevier Incl.
45. ProInvest. (2011, 7 16). Critical Review of the Kenyan Construction Industry. Nairobi. Retrieved 1 26, 2015, from www.iqskenya.org/THE-KENYAN-CONSTRUCTION-INDUSTRY.pdf
46. Ramachandra, T. (2013). Exploring Feasible Solutions to Payment Problems in the Construction Industry in New Zealand. Auckland University of Technology.
Auckland: AUT. Retrieved September 2, 2014, from http://hdl.handle.net/10292/5554
47. Ramachandra, T., & Rotimi, J. O. (2011). The Nature of Payment Problems in the New Zealand Construction Industry. Australian Journal of Construction Economics and Building, 11(2), 22-33.
48. Rich Field Engineering Limited V Syneresis Limited (2012).
49. Silverman, D. (2010). Doing Qualitative Research (3 ed.). SAGE.
50. Siti, J. S., & Rosli, R. A. (2010). Contractor’s Right Of Action For Late Or Non-Payment By The Employer. Journal of Surveying, Construction & Property, 1(1), 65
95.
51. Teresa, C., Gary, S., Mohan, K., & Wu, J. (2008). Are there ways to ensure fair and prompt payment? Hong Kong: 2008. From http://www.smile-net.hk/hot_topics/20080731lc_seminar.pdf
52. Thomas, R., & Wright, M. (2011). Construction Contract Claims (3 ed.). Hampshire: PALGRAVE MACMILLAN.
53. Tran, H., & Carmichael, D. G. (2013). A contractor’s classification of owner payment practices. Engineering, Construction and Architectural Management, 20(1), 29-45.
54. Uff, J. (2009). Construction Law (10 ed.). London: Thomson Reuters.
55. Wahome, G. W. (2014). Influence of Public Procurement Oversight Authority's Standard Tender Document on Public Building Projects in Kenya. Nairobi: Unpublished Masters Thesis.
56. Wahome, G., Wanyona, G., & Njeri, T. W. (2013). Effects of the Public Procurement Oversight Authority Standard Tender Document on Procurement of Public Works in Kenya. Africa Habitat Review, 557-563.
57. Whitfield, J. (1994). Conflicts in Construction,Avoiding,managing,resolving. London, London, England: MACMILAN PRESS LTD.
58. Wu, J., Kumaraswamy, M., & Soo, G. (2008). Payment Problems and Regulatory Responses in the Construction Industry: Mainland China Perspective. Journal of Professional Issues in Engineering Education and Practice, 399-407.
59. Ye, K. M., & Rahman, H. A. (2010). Risk of Late Payment in the Malaysian Construction Industry. World Academy of Science, Engineering and Technology, 1(41), 538-546.

95-102

17.

Authors:

Mark Obegi Kenyatta, Ahmad Omar Alkizim, Titus Kivaa Mbiti

Paper Title:

A Comparative Study: Multiplier Design using Reversible Gates logic

Abstract: In this paper we propose a new concept for multiplication by using modified booth algorithm, booth multiplier & wellece tree multiplier and reversible logic function. By combining modified booth algorithm with reversible gate logic it will produces further less delay compare to all other. Addition subtraction operation are realized using reversible DKG gate. Reversible logic circuits have theoretically zero internal power dissipation because they do not lose information, the classical set of gates such as AND, OR, and XOR are not reversible. This modified booth multiplier, modified booth multiplier & wellece tree multiplier with reversible gate logic are synthesized and simulated by using Xilinx 13.2 ISE simulator.

Keywords: Reversible logic gates, reversible logic circuit, partial products, adder, multiplier, power analysis, quantum computing, Future computing, simulation outputs.

References:
1. Gordon E. Moore, “Cramming more components onto integrated circuits”, Electronics Volume 38, number 8, April 19, 1965
2. R. Landauer, "Irreversibility and heat generation in t he computing process," IBM Journal. Research and Development, vol. 3, pp. 183-191, July1961
3. C. H. Bennett, "Logical reversibility of computation," IBM J. Research and Development, pp. 525-532, November 1973.
4. DeBenedictis, E. Will Moore’s Law be Sufficient? in proceedings of supercomputing, 2004. ANewVLSIArchitectureof
5. Parallel Multiplier–Accumulator Based on Radix-2 Modified Booth Algorithm “Young-Ho Seo, Member,IEEE,and Dong-Wook Kim, Member, IEEE
6. “A Distinguish Between Reversible And Conventional Logic Gates” B.Raghu Kanth, B.Murali Krishna Sridhar , V.G. Shanti Swaroop,
7. H. Thapliyal and M. B. Srinivas, "Novel reversible TSG gate and its application for designing reversible carry look ahead adder and other adder architectures", Proceedings of the 10th Asia-Paci_c Computer Systems Architecture Conference
8. M. Haghparast and K. Navi, "A Novel Reversible Full Adder Circuit for Nanotechnology Based Systems". Applied Sci., 7 (2007) 3995.
9. M. Haghparast and K. Navi, "Design of a novel reversible multiplier circuit using HNG gate in nanotechnology".
10. Fredkin, E. and T. Toffoli, 1982. Conservative logic. Int ‟l J. Theoretical Physics.

103-106

18.

Authors:

Ayushi Chaudhary

Paper Title:

Exploring of Sliding Window Visualization System to Understand Flow and Error Control Mechanism of Data Link Layer

Abstract: The perspective of this paper is to provide user friendly Visualization System for Sliding Window protocol. The Sliding Window Visualization system (SWV) is designed to understand the flow and error control mechanisms of DLL (data link layer) protocols. The system is interactive and allows the user to modify some parameters of the protocol. In this paper a visualization system has been developed where a user can easily understand the working principle of sliding window protocol and it can be used to compare two algorithms. In the mean time except the visualization of this network protocol also we have sent data packets at the back end. Thus giving an opportunity to the user to understand the mechanism of real time data transfer, where communication is often possible between physically separated machines. The software has major responsibility is to help to visualize newly developed algorithms where a researcher formulates some mathematical model of an algorithm and a developer converts it into a visualization system. This let us to compare two algorithm’s efficiency with respect to some parameter. This paper is designed and developed in such a manner that it provides a vast scope of further development. Number of modules can be added without many modifications in it code for new algorithm and to compare their efficiency with respect to existing algorithms. Since, this visualization system has been designed by taking care of the needs of users, their tasks. This SWV can be used as a teaching tool for a term at the community college level. SWV will be useful in a laboratory or self-study situation after the student has been introduced to DLL protocols. SWV's strong point is in helping to create mental images of the protocol mechanisms, and in allowing easy and painless experimentation with the supported protocols.

Keywords: Sliding window protocol, Interactive animation, visualization

References:
1. Lawrence, A.W., Badre, A.N., & Stasko, J.T., “Empirically evaluating the use of animations to teach algorithms”, Proceedings of the 1994
2. IEEE Symposium on Visual Languages IEEE Computer Society Press, Los Alamitos, CA, 1994, pp. 48-54.
3. David Henery, Yashwant K. Malayia, “A Visualization System forSliding Sliding Windows Protocols” Proceedings of the 2003 IEEE frontiers in Education Conference Boulder, CO ppT2C1-T2C6.
4. Kehoe, C.M., & Stasko, J., “Using Animations to Learn about Algorithms: An Ethnographic Case Study”, Georgia Institute of Technology Technical Report GIT-GVU
96-20., 1996.
5. Tanenbaum, A.S., Computer Networks, 3rd Edition, Prentice Hall PTR, 1996, pp. 212-250.
6. Khuri, S., “Designing Effective Algorithm Visualizations”, Available at http://www.mathcs.sjsu.edu/ faculty/khuri, 2001.
7. Brown, M.H., & Herschberger, J., “Color and Sound in Algorithm Animation”, DEC Systems Research Center Technical Report, 1991.
8. Chi, M.T.H., Bassok, M., Lewis, M., Reimann, P., Glaser, R., “Self Explanations: how students study and use examples in learning to solve problems”, Cognitive Science, #13, 1989, pp. 145 -182.
9. Price, B.A., Baecker, R.M., & Small, I.S., “A principled taxonomy of software visualization”, Journal of Visual Languages and Computing
10. 4, 1993, pp. 211 -266.
11. Cox, K., Roman, G., “Abstraction in Algorithm Animation”, Proceedings of the 1992 IEEE Workshop on Visual Languages, 1992,
12. pp. 18-24.
13. Henry, D., “Master’s Project: A Visualization System for Sliding Windows Protocols”, Colorado State University Technical Report,
14. available at http://www.cs.colostate.edu/testing/, 2002.
15. Hundhausen, C., “Toward effective algorithm visualization artifacts: Designing for course”, Doctoral dissertation, University of Oregon,
16. 1999.
17. Hansen, S.R., Narayanan, N.H., & Schrimpsher, D., “Helping learners visualize and comprehend algorithms”. Interactive Multimedia
18. Electronic Journal of Computer-Enhanced Learning, 2000.
19. Stasko, J., Badre, A., & Lewis, C., “Do Algorithm Animations Assist Learning? An Empirical Study and Analysis”, Proceedings of ACM INTERCHI'93 Conference on Human Factors in Computing Systems, ACM Press, New York, 1993, pp. 61 -66.
20. Lattu, M., Tarhio, J., & Meisalo, V., “How a Visualization Tool Can Be Used - Evaluating a Tool in a Research & Development Project”, Proceedings of the 12th Workshop of the Psychology of Programming Interest Group, 2000.
21. Douglas, S., McKeown, D., & Hundhausen, C., “Exploring Human Visualization of Computer Algorithms”, Graphics Interface ’96, 1996, pp. 9 -16.
22. Michail, A., “Teaching Binary Tree Algorithms through Visual Programming”, University of Washington Technical Report UWCSE-97-05-01, 1996.

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19.

Authors:

George Moroni Teixeira Batista, Mayu Urata, Mamoru Endo, Takami Yasuda

Paper Title:

Revised Dynamic Teaching Materials Concept for Community Learning

Abstract: for teachers, handling information technology is not easy because it is not their specialty. This is even more apparent when they have to use it to create interactive and/or multimedia teaching materials. How can they be assisted in learning the necessary information technologies and in creating, sharing, and teaching e-Learning content? New technologies and the students represent an important part of the continuously changing learning environment, with students usually already knowing the technologies that teachers are trying to learn. Therefore, perhaps there is a way to allow students to help teachers to learn the technologies. This may be better than having a separate technical support team for the teachers, as the teachers end up depending on the support team and are thus unable to handle the technologies by themselves. This paper introduces a revised version of the dynamic teaching materials concept, based on the results of the evaluation tests done up to now. It aims to create a learning community system that allows students and teachers to help each other to learn and to improve the learning environment based on their own necessities.

Keywords: Community learning, e-Learning, education, teaching materials, version management.

References:
1. E. Boggs and D. Jones, “Lessons learnt in connecting schools to the internet”, Australian Educational Computing, Vol. 9, No. 2, 1994, pp.29–32.
2. Jones, D. and Buchanan, R. "The design of an integrated online learning environment, making new connections", Proceedings of ASCILITE ‘96, Adelaide, Allan Christie, Patrick James, Beverley Vaughan, 1996, pp 331–345.
3. G. M. T. Batista, M. Urata, T. Yasuda, "The dynamic teaching materials system: a way to make teaching materials evolve", International Journal of Knowledge and Web Intelligence, Vol. 3, No. 4, 2012, pp.343-360.
4. G. M. T Batista, M. Urata, M. Endo, T. Yasuda and K. Mouri. "Using patterns to guide teachers and teaching materials evolution", The Seventh International Conferences on Pervasive Patterns and Applications, France, March 2015, IARIA, 2015, pp.8-14.
5. Jones, D. and Lynch, T. "A model for the design of web-based systems that supports adoption, appropriation, and evolution", Murugesan, S. and Deshpande, Y. (Eds.): Proceedings of the 1st ICSE Workshop on Web Engineering, Los Angeles, 1999, pp.47–56.
6. K. Iwazaki, et al. "Possibility and the trial based on "connection" between the Science Museum and Universities or Visitors: Development of Exhibitions for the 50th Anniversary Event of the Nagoya City Science Museum", Journal of the Japan Information-culture Society, Vol.20, No. 1, pp.10-17, May 2013.
7. T. Roberts, C. Romm, and D. Jones, “Current practice in web- based delivery of IT courses”, APWEB 2000, Xi’an, China, 2000, pp.27–29.
8. S. Alexander, "E-Learning developments and experiences", Technological Demands on Women in Higher Education: Bridging the Digital Divide, Cape Town, February 2001.
9. B. Gillani, “Learning theories and the design of e-Learning Environments”, University Press of America, United States of America, 2003.
10. Jones, D. "Computing by distance education: problems and solutions", Integrating Technology into Computer Science Education, Association for Computing Machinery, Barcelona, Gordon Davies, 1996, pp.139–146.
11. Koehler M. J., and Mishra, P. "What is technological pedagogical content knowledge?", Contemporary Issues in Technology and Teacher Education, Vol.9, No. 1, 2009, 60-70.

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20.

Authors:

Umar Farooq, Sajid Bashir, Tauseef Tasneem, A.Saboor, A.Rauf

Paper Title:

Migration from Copper to Fiber Access Network using Passive Optical Network for Green and Dry Field Areas of Pakistan

Abstract: Passive Optical Networks (PON) technology brings an evolution in the industry of Telecommunication for the provisioning of High Speed Internet (HSI) and Triple Play bundled Services that includes Voice, Data, and Video Streaming throughout the world. In Pakistan most of the service providers are offering broadband services on traditional copper OSP (Outside Plant) network since 2000. Demand for the high speed internet and broadband is increasing rapidly, it is desired with great need to migrate from traditional copper based OSP network to PON – FTTx (Fiber To The x) infrastructure. Considering the geographical requirements in Pakistan a scalable fiber network is required which can be optimized as per the user’s requirements and demands with high speed bandwidth efficiency, involving the minimum losses and with ideal capital expenditure (CAPEX). In this work a platform for migration from copper to fiber access network with a scalable and optimized PON – FTTx infrastructure in green field and dry field areas of Pakistan have been proposed using Geographic Information system (GIS). In any developing country like Pakistan having the same cultural and geographical topology, this platform can be used to migrate from copper to fiber access network to provide the PON based telecom services. The developed platform for migration from copper to PON based fiber has been studied, planned, and then simulated on a selected geographical area of Pakistan with physical execution that showed better and efficient results with reduction in capital and operational expenditures. A factual plan without ambiguities assists the operators of Pakistan to analyze/forecast bandwidth requirements of an area, optimized network planning along with the in time and efficient deployment.

Keywords: FTTx, GIS, HSI, OSP, PON

References:
1. Cale, I., Salihovic, A., Ivekovic, “Gigabit Passive Optical Network – GPON.”in proc.29th Int. Conf. M. Information Technology Interfaces – ITI, pp 679-684, 2007.
2. ITU “Impact of broadband on the economy” Research to Date and Policy Issues April 2012
3. Rob Frieden “Lessons from broadband development in Canada, Japan,Korea and the United States” Telecommunications Policy,vol. 29 pp 595–613, 2005
4. Bourne, J. “Fiber to the Home: Practically a Reality”, Communications, 1988. In IEEE int. conf.Digital Technology - Spanning the Universe. pp 890-892, 1988.
5. Selmanovic, F., Skaljo, E.; “GPON in Telecommunication Network”, in int. cong. Ultra-Modern Telecommunications and Control Systems and Workshops – ICUMT, pp 1012-1016, 2010.
6. Ouali, A., Kin Fai Poon, Chu, A. “FTTH network design under power budget constraints”,), 2013 in Proc. IEEE Int. Symp.integrated Network Management (IM 2013), pp 748 – 751, 2013.
7. SilveirinhaFélix, H., de Oliveira Duarte, A.M. “FTTH - GPON access networks: Dimensioning and optimization” in21th Telecommunications Forum -TELFOR, pp 164-167, 2013.
8. T Gilfedder, “Deploying GPON technology for backhaul applications”, BT Technology Journal, Vol 24 No 2, pp 20-25, 2006.
9. PTA, Annual Report 2014. Available http://urdu.pta.gov.pk/annual-reports/ptaannrep2013-14.pdf
10. Oliver Johnson, “Mapping broadband worldwide: examples, challenges and results.”In ITU Tech. Symp. Telecom World -ITU WT, pp 35-38, 2011.
11. S.P. van Loggerenbergy, M.J. Groblery, S.E. Terblanchez, “Optimization of PON Planning for FTTH Deployment Based on Coverage”, unpublished
12. Segarra, J., Sales, V., Prat, J., “Planning and designing FTTH networks: Elements, tools and practical issues”, in proc.14th Int. Conf. Transparent Optical Networks - ICTON, pp 1 – 6, 2012.
13. Wi-Tribe, “Broadband Market in Pakistan”2008 Availablehttp://www.wi-tribe.pk/media-center/news/broadband-market-in-pakistan-an-insight-2/
14. Taylor, T.M., Willis, H.L., Engel, M.V. “New considerations for distribution network planning and design”, in 14th Int. Conf. Electricity Distribution. Part 1: Contributions - CIRED. pp 6.1.1 – 6.1.5, 1997.
15. Roxana-Mariana BEIU, Constantin D. STANESCU, “Optical Power Budgets for Fiber-Optic (FO)s”,Fascicle of Management and Technological Engineering,pp – 723-728.
16. P. Kourtessis (chapter editor), C. Almeida, C.-H. Chang, J. Chen, S. Di Bartolo, P. Fasser, M. Gagnaire, E. Leitgeb, M. Lima, M. Löschnigg, M. Marciniak, N. Pavlovic, Y. Shachaf (assistant editor), A.L.J. Teixeira, G.M. TosiBeleffi, and L. Wosinska, “Evolution of Optical Access Networks”, I. Tomkos et al. (Eds.): COST 291 – Towards
Digital Optical Networks, 2009.
17. Senior, John M. Optical fiber communications: principles and practice.Vol. 2. UK: Prentice Hall, 1992.
18. Wikipedia, Passice Optical Networks, Available http://en.wikipedia.org/wiki/Passive_optical_network
19. ITU-T Recommendation G.984.1 (2003).
20. ITU-T L.86 (07/2010)
21. ITU-T G.984.2 Amendment 1 (02/2006)
22. M. Sawada, Daniel Cossette, Barry Wellar, Tolga Kurt, “Analysis of the urban/rural broadband divide in Canada: Using GIS in planning terrestrial wireless deployment”, Science Direct, Government Information Quarterly 23, pp 454-479, 2006.
23. Tony H. Grubesic, Alan T. Murray, “Geographics of imperfection in telecommunication analysis” Science Direct,Telecommunications Policy 29, pp 69 – 94, 2005.
24. Saifullah Khan, Syed Asif Kamal, “GIS as a Planning tool for the Rural Telecom, E-Services and Broadband projects in Pakistan”, in 13th Int. Conf. Computational Science and Its Applications, pp 182 – 188, 2013.
25. Rong-Show Kuo, Pei-Chun Chen, I-Ling Huang, Chang-Ho Chen, Shih-Wei Lai, Y.-c. Lin, Kuan-Hsiung Liang, “Implementation of the Management of an Optical Distribution Network in a Geographic Information System”, Network Operations Laboratory.
26. Mathieu Tahon, Jan Van Ooteghem, Koen Casier, SofieVerbrugge, Didier Colle, Mario Pickavet, Piet Demeester, “Improving the FTTH business case–A joint telco-utility network rollout model”, Telecommunications Policy, pp 1 – 12, 2013.
27. Bruno Van Den Bossche, RafMeersman, JeroenVanhaverbeke and Abram Schoutteet, “Maximizing the Return on Investment for FTTx-rollout through the use of GIS Street Maps and Geomarketing data”, in 9th conf. Telecommunications Internet and Media Techno Economics – CTTE, 2010
28. Gerson Mizuta Weiss and ElianeZambonVictorelli Dias, “Moving Telecom Outside Plant Systems towards a Standard Interoperable Environment”, J.N. de Souza et al. (Eds.): ICT 2004, LNCS 3124, pp. 1246–1251, 2004.
29. ManisaPipattanasomporn, Saifur Rahman, “The telecommunication infrastructure: A model for optimum voice-data coverage”, Utilities Policy Vol.14, Issue 4, pp 278 – 287, 2006.
30. Garfias, P.; Univ. Politec. deCatalunya, Barcelona, Spain ; Gutierrez, L. ; Sallent, S. “Enhanced DBA to provide QoS to coexistent EPON and 10G-EPON networks”.Journal of Optical Communications and Networking Vol. 4, Issue 12, pp. 978-988 , 2012
31. Duo Peng ;Comput. &Commun. Coll., Lanzhou Univ. of Technol., Lanzhou, China ; Peng Zhang “Design of optical integrated access network based on EPON” Unpublished.
32. Salleh, M.S. ; TMR&D SdnBhd, TM Innovation Center, Cyberjaya, Malaysia ; Manaf, Z.A. ; Khairi, K. ; Mohamad, R. more authors “The challenge for active and passive components design in CWDM PON system co-exist in GEPON and 10 GEPON architecture”, in IEEE 2nd Int. conf. Photonics -ICP, pp 1 – 5, 2011.
33. Penze, R.S. ; Convergence Network Dept., CPqD - R&D Center in Telecommun., Campinas, Brazil ; Rosolem, J.B. ; Duarte, U.R. ; Filho, R.B. “Passive optical network upgrading by using In-band WDM overlay”.In Proc. Int. Conf. Microwave & Optoelectronics Conference - IMOC, pp 40 – 43, 2011.
34. Wei Ji ; Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China ; Yonghui Liu ; Wei Cui “The design of Home Gateway which used in FTTH”.In Proc. Int Conf. Networking and Digital Society – ICNDS, Vol – 2, pp 157 – 160, 2010.
35. Nikitin, A. ;Pyattaev, V. ; Kim, B.W. “Technological aspects of the triple play service on fixed access networks” In 11th Int. Conf. Proc. Advanced Communication Technology - ICACT , pp 1980 – 1983, 2009.
36. Kostadinova, S. ;Dimova, R. ; Stoyanov, G. “Performance parameters evaluation in broadband access network”.In Int. Conf. Optimization of Electrical and Electronic Equipment - OPTIM, pp 790 – 795, 2014.
37. Çatalbaş, C. ;Elektron. veHaberlesmeMuhendisligiBolumu, YildizTeknik Univ., Istanbul, Turkey ; Ünverdi, N.O. “Performances of some applications in passive optical networks”.In 22nd Int. Conf. Signal Processing and Communications Applications Conference - SIU, pp 2261 – 2264, 2014.
38. Medgyes, B. ; Dept. of Electron. Technol., Budapest Univ. of Technol. & Econ., Budapest, Hungary ;Illes, B. “Contradictory electrochemical migration behavior of copper and lead”.In 34th Int. Spring Seminar, Electronics Technology - ISSE, pp 206 – 211, 2011.
39. Timmers, M. ;Guenach, M. ; Nuzman, C. ; Maes, J. “G.fast: evolving the copper access network”.Communications Magazine, IEEE Vol.51, Issue.8, pp 74 – 79, 2013.
40. Svedek, V.; HAKOM, Zagreb, Croatia ;Jurin, G. ; Weber, M. “Increasing availability of broadband access over copper network infrastructure”.In 34th proc. Int. Conv. MIPRO, pp 407 – 412, 2011
41. Jensen, M.; Center for Network Planning, Aalborg Univ. ; Nielsen, R.H. ; Madsen, O.B. “Comparison of Cost for Different Coverage Scenarios between Copper and Fiber Access Networks”.In 8th Int. Conf. Advanced Communication Technology – ICACT, pp 2015 – 2018, 2006.
42. Alshaer, H., Alyafei, M. “An end-to-end QoS scheme for GPON access networks”,In GCC Conference and Exhibition - GCC, pp – 513 – 516, 2011.
43. Yinghui Qiu, “Availability Estimation of FTTH Architectures Based on GPON”, In7th Int. Conf. Wireless Communications, Networking and Mobile Computing – WiCOM. pp 1 – 4, 2011.

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21.

Authors:

Sanjeev. K, Sivananda Hariprasth, Saranya. M, Sandhya. G

Paper Title:

Design of Area and Speed Efficient Square Root Carry Select Adder Using Fast Adders

Abstract: Area and speed are the most important design objectives in integrated circuits. As addition is the basic operation of all computer arithmetic, adders are one of the widely used components in digital integrated circuit design. Since propagation of carry is of major concern in designing efficient adders, this paper presents different fast adders and their performance analysis. Among all the adders discussed Square root Carry Select Adder (SQCSA) provides a good compromise between cost and performance. As, Conventional SQCSA is still area consuming due to dual Ripple Carry Adder(RCA)structures, modifications are done at gate level to reduce area. Modified SQCSA is designed using fast adders like Carry Skip Adder (CSA) and Carry Look-Ahead Adder (CLA) to increase the speed of operation.

Keywords: (SQCSA), (CLA), (CSA), Conventional, designed, Carry, Adder, concern, adders, Among, Modified

References:
1. Kuldeep rawat, Tarek Darwish, and Magdy Bayoumi, “Alow power and reduced area Carry Select Adder”,45th Midwest Symposium and circuits and systems,vol.1,PP.467-470, March 2002.
2. O.J. Bedrij, “Carry-Select Adder”, IRE transactions on Electronics computers, vol.EC-11, pp.340-346, June 1962.
3. B. Ramkumar, Harish M Kittur and P. Mahesh Kannan, “ASIC implementation of Modified Faster Carry SaveAdder”, European journal of scientific research,vol.42,pp.53-58, 2010.
4. M.Moris Mano, “Digital Design”, Pearson Education, 3rdedition 2002.
5. Singh, R.P.P.; Kumar, P.; Singh, B., “Performance Analysis of Fast Adders Using VHDL”, Advances inRecent Technologies in Communication and Computing, 2009.
6. A Tyagi. “A reduced area scheme for carry selectadders“, IEEE Trans. On computer, vol.42, pp.1163-1170,1993.
7. J.M. Rabaey, “Digital Integrated Circuits-A Design Perspective”, New Jersey, Prentice-Hall, 2001.
8. Abu-Shama and M. Bayoumi, “A New cell for low power adders,” in Proc. Int

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22.

Authors:

Amit J. Modak, H. P. Inamdar

Paper Title:

Design of MLP-NN Classifier Block with PCA- Type of Dimensionality Reduction Technique for Assessment of State of Degradation in Stator Insulation of Induction Motor

Abstract: In the present work, the design of discrete ‘ANN’ simulation model was done for the classification and qualitative assessment of the state of degradation of insulation present in the respective phases of three-phase ac induction motor. The ‘ANN’ simulation model consists of numbers of discrete neural network classifier blocks. The extraction of mathematical parameters of stator current data pattern, which are simulating the specific state of degradation of insulation based on Park’s current transformation model, were presented in the previous research papers. Further, the optimal design specifications of the various neural network classifier blocks, which were realized on the basis of ‘multilayer perceptron’ (MLP) and ‘radial basis function’ (RBF) types of neural network architectures were compared in the same papers. The striking generalizations, which were derived on the basis of the comparative performance analysis resemble that the general optimum design specifications, which are determined on the basis of ‘MLP’ network are preferred as an optimum choice over the ‘RBF’ network. The aim of the present research paper is to explore the possibility of any further reduction in the size of the ‘MLP’ network. The present investigation emphasis the use of ‘principal component analysis’ type of dimensionality reduction technique for the simplification and improvement in the design of discrete neural network classifier blocks, which were already designed on the basis of ‘multilayer perceptron’ (MLP) neural network architecture for the classification and qualitative assessment state of degradation of insulation in three-phase ac induction motor

Keywords: induction motor, stator insulation, dimensionality reduction technique, principal component analysis (PCA), sensitivity analysis (SA), artificial neural network (ANN).

References:
1. G. C. Stone, H. G. Sedding, B.A. Lloyd and B.K. Gupta, “The ability of diagnostic tests to estimate the remaining life of stator insulation,” IEEE Trans. Energy Conversion, vol.3, no.4, Dec.1988, pp. 833 - 841.
2. Yoshida, H. and K. Umemoto, "Insulation Diagnosis for Rotating Machine Insulation," IEEE Trans. Electrical Insulation, vol. 21, no.6, Dec. 1986, pp. 1021-1025.
3. Tsukui, T., M. Takamura and Y. Kako, "Correlations between Nondestructive and Destructive Tests on High-Voltage Coil Insulations for Rotating Machines," IEEE Trans. Electrical Insulation, vol. 15, no. 2, April 1980, pp. 118-124.
4. A.J. Modak and H.P. Inamdar, “Performance Evaluation of Computer Simulated Extracted Features of Induction Motor with Healthy State of Insulation Present in Any Two-Phases,” International Journal of Computer and Electrical Engineering (IJCEE), International Association of Computer Science and Information Technology Press (IACSIT), Singapore, vol.4, no.1, February 2012, pp. 65-74, DOI:10.7763/IJCEE.2012.V4.453, (ISSN: 1793-8163)
5. A.J. Modak and H.P. Inamdar, “Computer Simulated Extracted Parameters of Induction Motor for Equal Variation in State of Degradation of Insulation in all Three-phases,” Proc. 7th IEEE Int. Conf. Industrial Electronics and Applications (ICIEA), Singapore, July 2012, pp. 1349-1350, IEEE Catalog Number: CFP1220A-CDR, (ISBN: 978-1-4577-2119-9/12)
6. A.J. Modak and H.P. Inamdar, “Performance Evaluation of Computer Simulated Extracted Features of Induction Motor for Variation in State of Degradation of Insulation in Any Two-Phases with Healthy State of Insulation in Remaining Phase,” International Journal of Applied Engineering (IJAE),Eureka Press, Singapore, vol.1, no.2, December 2011, pp.104-114 (ISSN: 2010 4391)
7. [XLSTAT, 2008] : Addinsoft: XLSTAT Version 2008, http:// www.xlstat.com, Addinsoft Inc. 28 West 27th Street, Suite 503, New York, NY 10001, USA.
8. Kovach Computing Services, http://www.kovcomp.co.uk, Kovach Computing Services,85,Nant-y-Felin,Pentraeth,Isle of Anglesey LL75 8UY Wales, U.K. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301].
9. [NEUROSOLUTIONS, 5.0]: NEUROSOLUTION Version 5.0, http:// www.neurosolutions.com, Neuro Dimension, Inc. 3701 NW 40th Terrace, Suite 1, Gainesville, FL 32606.

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