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Volume-5 Issue-4: Published on September 05, 2015
Volume-5 Issue-4: Published on September 05, 2015
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Volume-5 Issue-4, September 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



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.

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


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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.

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pp. 304-313.

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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.  

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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.  

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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.

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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. 




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.

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


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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).

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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




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.

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


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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.

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


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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.

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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.

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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..

  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).

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).
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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)

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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)

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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)

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


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., April 2000.

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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:

12.     OASIS. (2007). Web Services Business Process Execution Language Version 2.0. Available: 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.




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.

  SAS, BI-Tools, Market-Research


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




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.

  error rate, training algorithm, back propagation, network parameters


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.




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. 

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


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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].

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.

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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.

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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.

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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.

  Project control, construction industry, Project control factors, Kenya


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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.

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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.

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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.




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.

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


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5.          Deviprasadh, A. (2007). Risk Assessment and Management in construction projects. M.E. Construction Engineering and Management Thesis report. Anna University, Chennai.

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7.          Gichunge, H. (2000). Risk Management in the Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi

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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

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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.

 Neural Network Controllers; Generalization; Systems Modelling; Control Systems


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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

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


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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

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


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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)

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34. by the paper group




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.

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


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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.

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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.

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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.

 Payment default, contractors, construction industry of Kenya.


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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.

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


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

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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.

     Sliding window protocol, Interactive animation, visualization


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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.

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


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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.

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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.



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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.

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


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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

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


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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)

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