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Volume-4 Issue-3: Published on July 05, 2014
Volume-4 Issue-3: Published on July 05, 2014

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Volume-4 Issue-3, July 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Ahmad Hamza Al Cheikha

Paper Title:

Matrix Representation of Groups In the Finite Fields GF(pn)

Abstract:     The representation of mathematical fields can be accomplished by binary rows (or columns) of a binary triangular matrix as the Hamming’s matrices, but this representation don’t show the basic product properties of the fields, that is the nonzero elements of the fields forms a cyclic multiplicative  group. In this paper we show that the elements of the fields GF(pn), and their subgroups,  can represent as square matrices by m – sequences, which satisfies the product properties as a cyclic group.

Galois fields, m-sequences, cyclic groups, Orthogonal sequences.


1.             Yang K , Kg  Kim   y  Kumar   l. d ,“Quasi –  orthogonal Sequences for code - Division Multiple Access Systems ,“IEEE Trans .information theory, Vol. 46 NO3, 200, PP 982-993
2.             Jong-Seon No, Solomon. W & Golomb, “Binary  Pseudorandom Sequences For period 2n-1 with Ideal Autocorrelation, ”IEEE Trans. Information Theory, Vol. 44 No 2, 1998, PP 814-817

3.             Lee J.S &Miller L.E, ”CDMA System Engineering Hand Book, ”Artech House. Boston, London,1998.

4.             Yang S.C,”CDMA RF System Engineering, ”Artech House.Boston-London,1998.

5.             LIDL,R.&  PILZ,G.,”Applied Abstract Algebra,” Springer – Verlage New York, 1984.

6.             Lidl, R.& Niderreiter, H., “Introduction to Finite Fields  and Their Application,” Cambridge university USA,  1994.

7.             Thomson W. Judson, “Abstract Algebra: Theory and Applications ,” Free Software Foundation,2013.

8.             FRALEIGH,J.B.,  “A First course In Abstract Algebra, Fourth printing. Addison-Wesley publishing  company USA,1971.

9.             Mac WILIAMS,F.G& SLOANE,N.G.A., “The Theory Of Error- Correcting Codes,” North-Holland, Amsterdam, 2006.

10.          KACAMI,T.&TOKORA, H., “TeoriaKodirovania,”Mir(MOSCOW), 1978.  

11.          David, J., “Introductory Modern Algebra, ”Clark University U. S. A, 2008.

12.          SLOANE,N.J.A., “An Analysis Of The Stricture And Complexity Of Nonlinear Binary Sequence Generators,” IEEE Trans. Information TheoryVol. It 22 No 6,1976, PP 732-736.

13.          Al Cheikha A. H. “ Matrix Representation of Groups in   the finite Fields GF(2n) ,” International Journal of Soft Computing and Engineering, Vol. 4, Issue 2, May 2014, PP 118-125.  




Ketki Thakre, Nehal Chitaliya

Paper Title:

Dual Image Steganography for Communicating High Security Information

Abstract:      The recent growth in computational power and technology has propelled the need for highly secured data communication. One of the best techniques for secure communication is Steganography-a covert writing. It is an art of hiding the very existence of communicated message itself. The process of using steganography in conjunction with cryptography, called as Dual Steganography, develops a sturdy model which adds a lot of challenges in identifying any hidden and encrypted data. Using cryptographic techniques to encrypt data before transmission may forestall any type of security problems. But the camouflaged appearance of encrypted data may arouse suspicion. Therefore using steganography inside steganography, give rise to improved version of dual steganography which will provide better security. This paper presents a technique for hiding data with two level of security to embed data along with good perceptual transparency and high payload capacity. Here the secret data is not restricted to images only but also applicable to any text, audio or video.

  Cryptography, Dual Steganography, LSB Technique, Steganography


1.                 Shailender Gupta, Ankur Goyal and Bharat Bhushan, “Information Hiding Using Least Significant Bit Steganography and Cryptography” International Journal Modern Education and Computer Science, vol. 6, pp. 27-34, 2012
2.                 Kanzariya Nitin K, Nimavat Ashish V., “Comparison of Various Images Steganography Techniques” International Journal of Computer Science and Management Research, vol. 2,pp. 1213-1217, 2013

3.                 Arvind Kumar, Km. Pooja, “Steganography- A Data Hiding Technique” International Journal of Computer Applications, vol. 9, pp. 19-23, 2010

4.                 Pratap Chandra Mandal, “Modern Steganographic technique: A survey”, International Journal of Computer Science & Engineering Technology, vol. 3, pp. 444-448, 2012

5.                 T. Sharp, “An implementation of key-based digital signal Steganography”, Proc. Information Hiding Workshop, Springer, vol. 2137, pp. 13-26, 2001

6.                 Johnson, Neil F., “Steganography”, IRM Conference, 2000

7.                 Johnson, N.F and Jajodia, S., “Exploring Steganography: Seeing the Unseen”, IEEE Computer Journal, vol. 31, pp. 26-34, 1998

8.                 H. Arafat Ali “Qualitative Spatial Image Data Hiding for Secure Data Transmission” International Journal on Graphics, Vision and Image Processing, vol. 7, pp. 35-43, 2007

9.                 Himanshu Gupta, Prof. Ritesh Kumar, Dr. Soni Changlani, “Enhanced Data Hiding Capacity Using LSB-Based Image Steganography Method”, International Journal of Emerging Technology and Advanced Engeneering,vol.3,pp. 212-214,2013

10.              Mr. Vikas Tyagi, Mr. Atul kumar, Roshan Patel, Sachin Tyagi, Saurabh Singh Gangwar  “Image Steganography Using Least Significant Bit With Cryptography” Journal of Global Research in Computer Science, vol. 3, pp. 53-55 , 2012

11.              Shilpa Gupta, Geeta Gujral and Neha Aggarwal, “ Enhanced Least Significant Bit algorithm For Image Steganography”, International Journal of Computational Engineering & Management, vol. 15, pp. 40-42, 2012

12.              T. morkel , J.h.p. eloff , M.s. olivier “An overview of image Steganography”, Information and computer security architecture research group ,pp. 1-11, 2005

13.              Tanmay Bhattacharya, Nilanjan Dey and S. R. Bhadra Chaudhuri, “A Novel Session Based Dual Steganographic Technique Using DWT and Spread Spectrum” International Journal of Modern Engineering Research, vol. 1, pp. 157-161, 2012

14.              K.Sakthisudhan, P.Prabhu, “Dual Steganography Approach for Secure Data Communication” International Conference on Modeling, Optimization and Computing, Elsevier, Procedia Engineering, vol. 38, pp. 412-417, 2012

15.              Rosziati Ibrahim and Teoh Suk Kuan, “Steganography Algorithm to Hide Secret Message inside an Image”, Computer Technology and Application, vol. 2, pp. 102-108, 2011

16.              Weiqi Luo, Jiwu Huang, Fangjun Huang, “Edge Adaptive Image Steganography Based on LSB Matching Revisited”, IEEE Transactions on Information Forensics and Security, vol. 5, pp. 201-214, 2010

17.              Mazen Abu Zaher, “Modified Least Significant Bit (MLSB)” Computer and Information Science, vol. 4, pp. 60-67, 2011

18.              Silman, J., “Steganography and Steganalysis: An Overview”, SANS Institute, pp. 1-8, 2001

19.              Ronak Doshi, Pratik Jain, Lalit Gupta, “Steganography and Its Applications in Security” International Journal of Modern Engineering Research, vol. 2, pp.4634-4638, 2012

20.              Udit Budhiaa, Deepa Kundura. “Digital video steganalysis exploiting collusion sensitivity”, Proc. of SPIE. vol. 5403, pp. 210-221,2004

21.              Shabir A. Parah, Javaid A. Sheikh, Abdul M. Hafiz and  G.M. Bhat, “Data hiding in scrambled images: A new double layer security data hiding technique” Computers and Electrical Engineering, Elsevier,vol. 40,pp. 70-82, 2014

22.              Phad Vitthal S.,Bhosale Rajkumar S.,Panhalkar Archana R., “ A Novel Security for Secret Data using Cryptography and Steganography” International Journal Computer Network and Information Security, vol. 2, pp. 36-42,2012




Srividya M. S., Hemavathy R., Shobha G.

Paper Title:

Underwater Video Processing for Detecting and Tracking Moving Object

Abstract:       In this paper, we present a vision system capable of analyzing underwater videos for detecting and tracking moving object. The video processing system consists of three subsystems, the video texture analysis, object detection and tracking modules. Moving object detection is based on adaptive Gaussian mixture model. The tracking was carried out by the application of the Kalman algorithm that enables the tracking of objects. Unlike existing method, our approach provides a reliable method inwhich the moving object is detected in unconstrained environments and under several scenarios (murky water, algae on camera lens, moving plants, low contrast, etc.). The proposed approach was tested with 20 underwater videos, achieving an overall accuracy as high as 85%.

 Video Processing, Detection, Tracking, Gaussian Mixture Model, Kalman Filtering


1.          SamanPoursoltan, Russell Brinkworth, Matthew Sorell  “Biologically-inspired Video Enhancement Method For Robust ShapeRecognition,” University of Adelaide,
Australia, IEEE, 2013.

2.          Prabhakar C J & Praveen Kumar P U. “Feature Tracking of Objects in Underwater VideoSequences.”, Kuvempu University,India, ACEEE 2012.

3.          SaeedVahabiMashak, BehnamHosseini, S.A.R. Abu-Bakar.“Background Subtraction for Object Detection under VaryingEnvironments.”UniversitiTeknologi Malaysia, IJCISIMA, 2012

4.          M. Weber, M. Welling, and P. Perona. “Detecting ,Tracking And Counting Fish In Low Quality Unconstrained Underwater Videos” University of Edinburgh, Edinburgh, UK.

5.          “Underwater ColorConstancy :Enhancement of Automatic Live Fish Recognition” M. Chambah, D. Semani, A. Renouf, P. Courtellemont, A. Rizzi, Université de La
Rochelle, France, Dept. of Information Technology - University of Milano/Italy.

6.          CodrutaOrnianaAncuti, CosminAncuti, Tom Haber and Philippe Bekaert “FUSION-BASED RESTORATION OF THE UNDERWATER IMAGES,” Belgium, IEEE, 2011.

7.          MohamedAmer, Emil Bilgazyev “Fine-grained Categorization of Fish Motion Patterns in Underwater Videos”, Oregon StateUniversity, ICCV, 2011

8.          PiotrJasiobedzki, Stephen Se, Michel Bondy, and Roy Jakola,“Underwater 3D mapping and pose estimation for ROVoperations”, OCEANS 2008, pp. 1-6, September 2008.

9.          Li Ma, KaihuaWu,L. Zhu, “Fire smoke detection in videoimages Using Kalman filter and Gaussian Mixture Colormodel”, International Conference on Artificial Intelligence andComputational Intelligence, 2010

10.       Li Xu, Feihu Qi, Renjie Jiang, YunfengHao, Guorong Wu,“Shadow Detection and Removal in Real Images: A Survey”Shanghai JiaoTong University, P.R. China, June 2006.

11.       J Shin, S Kim, et all, “Optical flow-based real-time object tracking using non-prior training active feature model”Computer Vision and Image Understanding, June 2005,Volume 98, Issue 3, Pages 462-490.

12.       B.Hosseini, SaeedVahabiMashak and A.S.R Abu Bakar,”Human Movement Based on Rule based classifier" Asia ModellingSymposium, May 2010.

13.       S.S. Beauchemin and J.L. Barron, “The Computation of Optical Flow”, ACM Computing Surveys, 1995 Vol. 27,No.3, PP 43- 467.

14.       S.Y. Chien, S.Y. Ma and L.G. Chen ,” Efficient moving objectsegmentation algorithm using background registrationtechnique” IEEE Trans Circuits Syst. Video Technol, JULY2002

15.       Hu Fuyuan, Zhang Yanning, et all, “A New Method of MovingObject Detection and Shadow Removal” JOURNALOFELECTRONICS (CHINA), July 2007

16.       Dongxiang Zhou, Hong Zhang and NilanjanRay,“TextureBased Background Subtraction”, Proceedings of theInternational Conference on Information and Automation,Zhangjiajie China, IEEE Conference, June 20 -23, 2008.

17.       YunlongGuo, Bo Yang, Yangyang Ming, Aidong Men, “AnEffective Background Subtraction Under the Mixture of MultipleVarying Illuminations”,Second InternationalConference on Computer Modeling and Simulation, IEEEConference, 2010.

18.       V. Brandou, A. G. Allais, M. Perrier, E. Malis, P. Rives, J.Sarrazin, and P. M. Sarradin, “3D Reconstruction oNaturalUnderwater Scenes Using the Stereovision System IRIS”, OCEANS 2007 - Europe , pp. 1-6, 2007.

19.       David G Lowe, “Distinctive image features from scaleinvariantkeypoints”, International Journal of Computer Vision, vol.60(2),pp. 91-110, 2004.

20.       David G Lowe, “Object recognition from local scaleinvariantfeatures”, Proceedings of the International Conference on ComputerVision, vol. 2, pp. 1150-1157, 1997.




Dipali Rojasara, Nehal Chitaliya

Paper Title:

Real Time Visual Recognition of   Indian Sign Language using Wavelet Transform and Principle Component Analysis

Abstract: Sign language is a mean of communication among the deaf people. Indian sign language is used by deaf   for communication purpose in India. Here in this paper, we have proposed a system using   Euclidean distance as a classification technique for recognition of various Signs   of Indian sign Language. The system comprises of four parts: Image acquisition ,pre processing, Feature Extraction and Classification. 31 signs including A to Z alphabets & one to five numbers were considered in this paper.

  Indian Sign Language (ISL), Principle Component Analysis (PCA), Sign Language Recognition (SLR)


1.              Deepika Tewari, Sanjay Kumar Srivastava; “ A Visual Recognition of Static Hand Gestures in Indian Sign Language based on Kohonen Self- Organizing Map Algorithm” ; International Journal of Engineering and Advanced Technology , Vol-2, Issue-2, December 2012.
2.              Kenny Morrison, Stephen J. McKenna; “An Experimental Comparison of Trajectory-Based and History-Based Representation for Gesture Recognition”; International Gesture Workshop,2004.

3.              P. Kakumanu, S. Makrogiannis, N. Bourbakis; “Asurvey of skin-color modeling and detection methods”; Elsevier, The journal of the pattern recognition society, 40 ,1106 – 1122, 2007.

4.              Y. Wang, B. Yuan;”A novel approach for human face detection from color images under complex background”, Pattern Recognition vol.  34 ,2001.

5.              Ketki. P.Kshirsagar, Dharmpal Doye; “ Object Based key Frame Selection for Hand Gesture recognition”; International Conference on Advances in Recent Technologies in Communication and Computing, 2010.

6.              R. Rojas ; “Neural Networks”; Springer-Verlag, Berlin, 1996.

7.              Siddharth S. Rautaray , Anupam Agrawal; “Vision based hand gesture recognition for human computer interaction: a survey”; Springer Science+Business Media Dordrecht, November 2012.

8.              Joyeeta Singha, Karen Das; “ Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique”;International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013

9.              Rashmi D. Kyatanavar, Prof. P. R. Futane;“Comparative Study of Sign Language Recognition Systems”; International Journal of Scientific and Research Publications,Vol. 2, 2012.

10.           Anirudh Garg; “Converting American Sign Language To Voice Using RBFNN”; Master’s Thesis, Computer Science, Faculty of San Diego State University, Summer 2012

11.           Bhawna Gautam; “Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transform”; Master’s Thesis, Computer Science and Engineering, NIT Rourkela, 2010

12.           Henrik Birk, Thomas Baltzer Moeslund;“Recognizing Gestures From the Hand Alphabet Using PrincipalComponent Analysis”; Master’s Thesis, Laboratory of Image Analysis, Aalborg University, Denmark, October 1996

13.           M.K. Bhuyan, “FSM-based Recognition of Dynamic Hand Gestures via Gesture Summarization using Key Video Object Planes”, International Journal of Computer and Communication EngineeringVol.6, 2012.

14.           Vaishali S. Kulkarni, Dr. S.D.Lokhande;“Appearance Based Recognition of American Sign Language Using Gesture Segmentation”; International Journal on Computer Science and

15.           Engineering, Vol. 02, No. 03 ,2010.

16.           Brian L. Pulito, Raju Damarla, Sunil Nariani, " 2-D Shift Invariant image Classification Neural Network, which overcomes Stability, Plasticity Dilemma", International Joint Conference on Neural Network, San Deigo, Vol 2,1990.

17.           Jong Bae Kim,Hye Sun Park,Min Ho Park,Massimo Piccardi,'Background subtraction techniques: a review',Systems, Man and Cybernetics, ,IEEE InternationalConference, vol.4,2004.

18.           Murthy, G. R. S. and Jadon, R. S. A Review of Vision Based Hand Gestures Recognition. International Journal. Of Information Technology and Knowledge Management,Vol. 2,2009.

19.           N. Ibraheem, M. Hasan, R. Khan, P. Mishra, “comparative study of skin color based segmentation techniques”, Aligarh Muslim University, A.M.U., Aligarh, India,2012.

20.           K. Burgers, et al., A Comparative Analysis of Dimension Reduction Algorithms on Hyperspectral Data, 2009

21.           M. A. amin and H. Yan, “Sign Language Finger Alphabet Recognition from Gabor-PCA Representation of hand gestures”, International Conference on Machine Learning and Cybernetics, 2007.

22.           Shikha Gupta , “Static Hand Gesture Recognition Using Local Gabor Filter”, International Symposium on Robotics and Intelligent Sensors,elsevier Procedia Engineering 41 , 2012 .

23.           Moharir PS. Pattern recognition transforms. New York: Wiley; 1992.

24.           S.S. Tamboli1 “Image Compression Using Haar Wavelet Transform”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, 2013




Amit Kumar Rohit, N. G. Chitaliya

Paper Title:

A Novel Approach for Content based Mri Brain Image Retrieval

Abstract:    With increasing amount in neuro patients which increases workload on small group of radiologists, a new system is needed that help radiologists for getting essential information like types of image, extraction of tumor and retrieve the similar images for references to take treatment planning for neuro patient. In this paper, a new content based MRI brain image retrieving method is to be designed using Discrete Wavelet Transform based feature extraction, Support Vector Machine based classifier and Euclidean Distance based matching method. New tumor detection method is to be designed using Incremental Supervised Neural Network and Invariant moments.

    Content Based Image Retrieval (CBIR); Discrete Wavelet Transform (DWT); Euclidean Distance; Incremental Supervised Neural Network (ISNN); Invariant Moment; Support Vector Machine (SVM).


1.                Hatice Cinar Akakin and Metin N. Gurcan, “Content-Based Microscopic Image Retrieval System for Multi-Image Queries”, IEEE Transaction on Information Technology in Biomedicine, Vol. 16, No. 4, pp 758-768, 2012.
2.                R.Guruvasuki, A. Josephine Pushpa Arasi, “MRI Brain Image retrieval using Multi Support Vector Machine Classifier”, International Journal of Advanced Information Science and Technology, Vol. 10, No 10, pp 29-36, 2013.

3.                Mohanpriya S., Vadivel M, “Automatic Retrieval of MRI Brain Image using Multiqueries System”, IEEE Conference, pp 1099-1103, 2013.

4.                Lidiya Xavier, Thusnavis B. , Newton D.R. , “Content Based Image Retrieval Using Texture  Features Based On Pyramid- Structure Wavelet Transform” , IEEE Conference, pp 79-83, 2011.

5.                B.Ramasubramanian, G. Praphakar, S. Murugeswari, “ A Novel Approach for Content Based Microscopic Image Retrieval system Using Decision Tee Algorithm”, International journal of scientific& engineering research, Vol. 4, No 6, pp 584-588, 2013.

6.                Yudong Zhang, Zhengchao Dong, LenanWua, ShuihuaWanga, “A hybrid method for MRI brain image classification”, Elsevier journal Expert system and Application, Vol. 20, No 2, pp 10049-10053 ,2011.

7.                Sandeep Chaplot , L.M. Patnaik , N.R. Jagannathan, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Elsevier journal on Biomedical Signal Processing and Control, Vo.1,No 1,pp 86 -92 ,2006.

8.                Hashem Kalbkhania, Mahrokh G. Shayesteha, BehroozZali-Vargahan , “Robust algorithm for brain magnetic resonance image (MRI)classification based on GARCH variances series”, Elsevier journal on Biomedical Signal Processing and Control, Vol. 8, No 6 , pp  909-919, 2013.

9.                Z. Iscan, Z. DokurandT. Olmez, “Tumor detection by using  Zernike moments on segmented magnetic resonance brain images”, Elsevier Journal of Expert system and Application, Vol. 37, No 3, pp 2540-2549, 2010.

10.             Bogdam M. ,  “ Neural Network  Architectures and learning ” ,  IEEE Conference , pp 7802-7852 ,2003.

11.             M. M. Rahman, P. Bhattacharya, and B. C. Desai, “A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback”, IEEE Transaction on Information Technology in Biomedicine, Vol. 11, No. 1, pp. 58–69, 2007.

12.             Dr. Fahui long, Dr. Hongjiang Zhang and Prof. David Dagan Feng, “Fundamental of image Retrieval”, Available at:

13.             R.C. Gonzalez, R.E. Woods, “Digital Image Processing, second edition, Pearson Education, Ch. Wavelet and Multi resolution Processing”, pp. 349–408 , 2004.

14.             Dustin Boswell, “Introduction to Support Vector Machines”,    Available at:

15.             Harvard Medical School, Web: data available at:

16.             Mehdi Lofti, Ali Solimani, Aras Dargazany, Hooman Afzal, MojtabaBandarabadi, “Combinig Wavelet Transform and Neural Networks for Image Classification”, IEEE, 41st Southeastern Symposium on System Theory, pp 15-17, 2009. 

17.             ShenFurao, Tomotaka Ogura, Osamu Hasegawa, “An Enhanced Self Organizing Incremental Neural network For Online Unsupervised learning”, Elsevier  Journal on Neural Network, Vol. 20, No 8, pp 893-903, 2007.

18.             M.Kanimozhi, C.H. HimaBindu, “Brain MR Image Segmentation Using Self Organizing map”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No 10,pp 3968-3973, 2013.

19.             El- Shayed A El Dahshan, Tamel Hosny, Abdel- badech M. Salem, “Hybrid intelligent techniques for MRI brain images classification”, Elsevier Journal of Digital Signal Processing, Vol. 20 , No 2 ,pp 433-441,2010.

20.             Amir Ehsan Lashkari, “A Neural network based Method for Brain Abnormality Detection in MR Images using Zernike Moments and Geometric moments”,
International Journal of Computer Appliction, Vol. 4, No 7,pp 1-8, 2010.

21.             Brain Tumor Facts & Statistics:

22.             Cancer mortality in India: a nationally representative survey:





Dalip Singh, Gulshan Taneja

Paper Title:

Reliability and Cost-Benefit Analysis of a Power Plant Comprising two Gas and one Steam Turbines with Scheduled Inspection

Abstract:    A reliability model for a power generating system comprising two gas turbines and one steam turbine is developed wherein scheduled inspection is done at regular interval of time for maintenance. Initially, all the three units i.e. two gas turbines as well as one the steam turbine are operating and working of the system is called the working at full capacity. On failure of one of the gas turbines with steam turbine working, the system works at reduced capacity. If both the gas turbines get failed, the system goes to down state, whereas on failure of steam turbine, the system may be kept in the up state with one of the gas turbines working or put to down state according as the buyer of the power so generated is ready to pay higher amount or not and this is working in single cycle. Three types of scheduled inspection, that is, minor, path and major are done in this order at regular intervals of time for maintenance. System is analyzed by making use of semi - Markov processes and regenerative point technique. Various measures of system effectiveness such as mean time to system failure, availability at full capacity, availability at reduced capacity, availability in single cycle, expected down time, expected time for minor, path and major inspection, busy period for repair and expected number of visits have been obtained. Cost- benefit analysis has been carried out. Graphical study has been made and interesting conclusions are drawn

     Power  Plant comprising Two Gas Turbines and One Steam Turbine, Scheduled Inspection, Reliability, Cost-Benefit


1.              G. Taneja, D.V. Singh and Amit Minocha, “Profit evaluation of a 2-out of -3 unit system for an ash handling plant wherein situation of system failure did not arise,” Pure and Applied Mathematika Sciences , 28(2007),195-204.
2.              S.M. Rizwan, V. Khurana  and G.Taneja, “Reliability analysis of a hot standby industrial system,” International Journal of modeling and Simulation, 30 (2010), 315-322.

3.              N. Padmavathi, Rizwan S.M., Pal Anita and Taneja G., “Reliability analysis of an evaporator of a desalination plant with online repair and emergency shutdowns,” Aryabhatta Journal of Mathematics &Informatics, 4(2012), 1-12.

4.              Sukhbir Singh, Rahul Rishi, G. Taneja and Amit Manocha, “Reliability and availabilty analysis of database system with standby unit provided by the system provider,” Int. J. Of Soft Computing and Engg., 3(2013), 235-237.

5.              Su Baohe, “On a two-dissimilar unit system with three modes and random check,” Microelectronics Reliab. , 37(1997), 1233-1238.

6.              R.K. Tuteja, U. Vashistha and G.Taneja, “Cost-benefit of a system where operation and some times repair of main unit depends on sub-unit,” Pure Appl. Math. Sci., LIII (2001), 41-61.

7.              B. Parashar and G.Taneja, “Reliability and profit evaluation of a PLC hot standby system based on a master slave concept and two types of repair facilities,” IEEE Trans Reliab,  56(2007), 534-539. 

8.              Goyal, D.V.Singh & G.Taneja, “Reliability and cost-benefit  analysis of a system comprising one big unit and two small identical units with priority for operation/repair to big unit,” Mathematical Science,Iran,5(2011), 235-248.

9.              Dalip Singh and Gulshan Taneja, “Reliability analysis of a power generating system through gas and steam turbines with scheduled inspection,” Aryabhatta Journal of Mathamatics & Informatics, 5(2013), 373-380.




P. Saraswathi, M. Prabha

Paper Title:

Multi-Objective Evolutionary Algorithm for Routing in Wireless Mesh Networks

Abstract:     Wireless Mesh Networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self organized networks in places where wired infrastructures are not available. Routing in Wireless Mesh Networks has multi-objective nonlinear optimization problem with some constraints. This problem has been addressed by considering Quality of Service parameters such as bandwidth, packet loss rates, delay, path capacity and power consumption. Multi-objective evolutionary algorithms can find multiple Pareto optimal solutions in one single run. This paper uses multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem. Simulation results show that our proposed algorithm can generate well-distributed Pareto optimal solutions.

      Multi-objective Optimization, Evolutionary Algorithm, NSGA and Routing.


1.                Jose Maria A. Pangilinan and Gerrit K. Janssens. (2007). Evolutionary Algorithms for the Multi-objective Shortest Path Problem. World Academy of Science, Engineering and Technology, 25, 205-210.
2.                Kalyanmoy Deb. (2001). Multi-objective Optimization using Evolutionary Algorithms. New York: John Wiley & Sons., 2001.

3.                Kalyanmoy Deb & Santosh Tiwari. (2008). Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of OperationalResearch, 185(3), 1062-1087.

4.                Omar Al Jadaan, Lakishmi Rajamani, & C. R. Rao. (2008). Non-dominated ranked genetic algorithm for Solving multi-objective optimization Problems: NRGA. Journal of Theoretical and Applied Information Technology, 60-67.

5.                Rath, K. & S. N. Dehuri. (2006). Non-dominated Sorting Genetic Algorithms for heterogeneous Embedded System Design. Journal of ComputerScience, 2 (3),

6.                Salman Yussof & Ong Hang See. (2005). QoS Routing for Multiple Additive QoS Parameters using Genetic Algorithm. Proc. International Conference on Communication, 1, 99-104.

7.                Salman Yussof & Ong Hang See. (2007). Finding Multi-Constrained Path Using Genetic Algorithm. Proc. IEEE International Conference on Telecommunications 
8.                Srinivas, N. & Deb, K. (1994). Multi-objective Optimization using Non-dominated Sorting in Genetic Algorithm. Evolutionary Computation, 2(3), 221-248.
9.                Srinivas, N. & Kalyanmoy Deb. (2007). Multi-objective Optimization Using non-dominated Sorting in Genetic Algorithms, Evolutionary computation, 2(3) 221-248.

10.             Sriram, R., Manimaran, G., & Siva Ram Murthy, C. (1998). Preferred link based delay-constrained least-cost routing in wide area networks. Computer Communications, 21, 1655–1669.

11.             Chang Wook & Ramakrishna, R.S. (2002). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transaction Evolutionary Computation, 6(6), 566-579.

12.             George N. Rouskas & Ilia Baldine. (1997). Multicast Routing with End-to-End Delay and Delay Variation Constraints. IEEE Journal on SelectedAreas in Communications, 15(3),346-356.

13.             Ha Chen & Baolin Sun. (2005). Multicast Routing Optimization Algorithm with Bandwidth and Delay Constraints Based on GA. Journal of Communication and Computer, 2(5), 63-67.




Teressa T. Chikohora

Paper Title:

A Study of the Factors Considered when Choosing an Appropriate Data Mining Algorithm

Abstract:      A lot of data is generated and collected in today’s organizations .Data mining has helped a lot of businesses to extract knowledge from data and use it to make decisions and gain competitive advantage. Businesses now analyse the data to make business decisions. Various algorithms may be used to analyse the data, however some of them do not yield useful knowledge. Choosing the appropriate algorithm remains a problem given the diversity in available algorithms. There are many algorithms, making it difficult for analysts and researchers who may not know which algorithm will be suitable for their needs. As a way of optimizing the chances of extracting useful knowledge, this study focuses on how the data analysts and researchers may choose appropriate algorithms that will yield desired knowledge. A number of factors to be considered when selecting an algorithm are discussed to help analysts in choosing appropriate algorithms.

 algorithm, factors, tool, data mining


1.             Alexander, D. (n.d.): Data Mining [online]  (accessed 13/12/2013  234 p.m.)
2.             Anon (2012): Data Mining Algorithms (Analysis Services – Data Mining) [online] (accessed on 08/01/2014) 2012.

3.             Anon, (2008): Oracle Data Mining Concepts 11g Release 1 (11.1).

4.             Anon, (n. d.): An overview of Data Mining Techniques.

5.             Brown, M. (2012): Data Mining Techniques [online]  (accessed 23/12/2013).

6.             Cheeseman, P. and R. W. Oldford (1994): Selecting models from data. LNStats 89, Springer.

7.             Chung, M.H. and P. Gray (1999): Special Section Data mining. Journal of Management Information Systems Volume 16 No.1.

8.             Fournier-Viger, P. (2013): What are the steps to implement a data mining algorithm? [Online] are the steps to implement a data mining algorithm (accessed 30/01/2014)

9.             Gibert, K., Sànchez-Marrè, M. and V. Codina (2010): Choosing the Right Data Mining Technique: Classification of Methods and Intelligent Recommendation.

10.          Silltow, J. (2006):  Data Mining 101: Tools and Techniques online]   (accessed on 13/12/2013 at 409pm).

11.          Miquel Sànchez-Marrè, Karina Gibert and Ignasi Rodríguez-Roda (n.d.): GESCONDA: A Tool for Knowledge Discovery and Data Mining in Environmental Databases.

12.          Parthasarathy, S. (n.d.): CIS 674 Introduction to Data Mining.

13.          Witten, I. H. and E. Frank (2005): Data Mining Practical Machine Learning




Tamanna Jagga, Jyoti Mann

Paper Title:

Secure Digital Image Steganography

Abstract:  Steganography is the art of hiding the fact that communication is taking place. It is the science that hides the information in an appropriate cover carrier like image, text, audio and video media. Established businesses have adopted steganography for covert communication, artists have done the same for intellectual property protection from consumers and advertising agencies. This work proposes a DWT and Arnold Transform based Steganographic technique. Arnold transform is a significant technique of image encryption. Results show that proposed algorithm has good perceptual invisibility.

  Alpha Blending, Arnold Transformation, DWT, Steganography


1.              Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of   Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006
2.              Liu Tie-yuan, Chang Liang,  Gu Tian-long, "Analyzing  the  Impact  of  Entity  Mobility Models  on  the  Performance  of  Routing Protocols  in  the  MANET”,  3rd International  Conference  on  Genetic  and Evolutionary  Computing, 14-17  Oct.  2009, pp.56-59.

3.              Bhavyesh  Divecha,  Ajith  Abraham,  Crina Grosan  and Sugata Sanyal, “Analysis of Dynamic  Source  Routing  and  Destination-Sequenced  Distance-Vector  Protocols  for Different  Mobility  models”, First  Asia International  Conference  on  Modeling  and Simulation, AMS2007, 27-30 March, 2007, Phuket,  Thailand.
Publisher:  IEEE  Press, pp. 224-229.

4.              M.F.  Sjaugi,  M.  Othman,    M.F.A.  Rasid, "Mobility  models  towards the  performance of  geographical-based  route   maintenance strategy  in  DSR",  IEEE  International Symposium  on  Information  Technology, ITsim  2008  , Vol.  3, 26-28  Aug.  2008,  pp. 1-5.

5.              S.  Gowrishankar,  S.  Sarkar, T.G.Basavaraju,  "Simulation  Based Performance  Comparison  of  Community Model,  GFMM, RPGM,  Manhattan  Model  and  RWP-SS  Mobility  Models  in MANET," First  International  Conference on  Networks  and  Communications (NETCOM  '09),  27-29  Dec.  2009,  pp.408-413.    

6.              Jonghyun  Kim,  Vinay  Sridhara,  Stephan Bohacek, “Realistic  mobility  simulation  of urban  mesh  networks”, Journal  of  Ad  Hoc Networks,  Vol.  7, Issue  2,  March  2009, Publisher: Elsevier, pp. 411-430.

7.              Kalra, G.S., R. Talwar and H. Sadawarti,” Blind Digital Image Watermarking Robust Against Histogram Equalization”, Journal of Computer Science 8 (8): 1272-1280, 2012.

8.              Zhenjun Tang and Xianquan Zhang,” Secure Image Encryption without Size Limitation Using Arnold Transform and  Random Strategies”,Journal Of Multimedia, Vol. 6, No. 2, April 2011

9.              Lingling Wu, Jianwei Zhang, Weitao Deng, Dongyan He,” Arnold Transformation Algorithm and Anti-Arnold Transformation Algorithm”, The 1st International
Conference on Information Science and Engineering (ICISE2009), pp. 1164- 1167, IEEE 2009

10.           Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006

11.           S.Arivazhagan,  W.Sylvia Lilly Jebarani, M.Shanmugaraj,” An Efficient Method for the Detection of Employed Steganographic Algorithm using Discrete Wavelet Transform”, pp. 1-6, Second International conference on Computing, Communication and Networking Technologies,2010 IEEE.

12.           Nilanjan Dey, Sourav Samanta, Anamitra Bardhan Roy,” A Novel Approach of Image Encoding and Hiding using Spiral Scanning and Wavelet Based Alpha-Blending Technique”, Int. J. Comp. Tech. Appl., Vol. 2 (6), pp. 1970-1974, 2011  

13.           Pratibha Sharma, Shanti Swami,” Digital Image Watermarking Using 3level Discrete Wavelet Transform”, Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), pp. 129- 133, 2013




Virender Kadyan, Ritu Aggarwal

Paper Title:

Performance Analysis and Designing of Technique for Enhancement of Fingerprints based on the Estimated Local Ridge Orientation and Frequency

Abstract:   Fingerprint identification is a growing and popular biometric identification technology. It includes two steps one is fingerprint verification and other is fingerprint recognition. Both of them use minutiae, such as end points and bifurcation points, as features. Therefore, how to appropriately extract minutiae from fingerprint images becomes an important step in fingerprint identification. Extracting features from fingerprints is an essential step in fingerprint verification and recognition. Many algorithms for this issue have been developed recently. on. The goal of this paper is to develop a system that can be used for fingerprint verification through extracting and matching minutiae. To achieve good minutiae, initially, extraction is done in fingerprints with varying quality, then preprocessing in form of image enhancement. Many methods have been joined to build a minutia extractor and a minutia matcher. A fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency, is be implemented in this paper. Performance of the new developed system is then evaluated using visual analysis and goodness index value of enhanced image.

  Enhancement, Fingerprint, Ridges, Valleys


1.             Jianwei Yang, Lifeng Liu, Tianzi Jiang,    Yong  Fan, “A modified Gabor filter design method  for fingerprint image enhancement” Pattern  Recognition Letters 24 (2003) 1805–1817.
2.             L.Hong,., Y.Wan., A.K Jain,., 1998. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. Pattern Anal. Machine Intell. 20 (8), 777–789.

3.             D.Gabor,., 1946. Theory of communication. J. IEE 93, 429–457.

4.             C. Gottschlich, “Fingerprint growth prediction, image preprocessing and multi-level judgment aggregation” Ph.D. thesis, Univ. Göttingen, Göttingen, Germany, 2010.

5.             C. Gottschlich, P. Mihailescu, and A. Munk, “Robust orientation field estimation and extrapolation using semilocal line sensors,” IEEE Trans. Inf. Forensics Security, vol. 4, no. 4, pp. 802–811, Dec. 2009.

6.             C. Gottschlich, T. Hotz, R. Lorenz, S. Bernhardt, M. Hantschel, and A. Munk, “Modeling the growth of fingerprints improves matching for adolescents,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp. 1165–1169, Sep. 2011.

7.             Carsten Gottschlich, “Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012.

8.             Kulvir Singh, Sahil Sharma, Rakesh K. Garg, “Visualization of latent fingerprints using silica gel G:A new technique” ,Egyptian Journal of Forensic Sciences (2013) 3, 20–25.




Marwa Sharawi, E. Emary, Imane Aly Saroit, Hesham El-Mahdy

Paper Title:

Flower Pollination Optimization Algorithm for Wireless Sensor Network Lifetime Global Optimization

Abstract:    As wireless sensor networks still struggling to extend its lifetime, nodes` clustering and nomination, or selection of cluster head node are proposed as solution.  LEACH protocol is one of the oldest remarkable clustering approaches that aim to cluster the network`s nodes and randomly elects a cluster head for each cluster. It selects cluster heads but it is not responsible for proper clustering formation. In this paper we use the Flower Pollination Optimization Algorithm (FPOA) to propose a WSN energy aware clustering formation model based on the intra-cluster distances. The objective is to achieve the global optimization for WSN lifetime. Simulation results and performance analysis show that applying flower pollination optimization on WSNs clustering is more efficient. It is effectively balance power utilization of each sensor node and hence extends WSN lifetime comparatively with the classical LEACH approach.

Wireless Sensor Network; Energy-aware algorithm; Flower Pollination Optimization Algorithm; Hierarchical routing protocol.


1.              W. B. Heinzelman, Application-Specific Protocol Architectures for Wireless Networks, PhD thesis, Massachusetts Institute of Technology, June 2000.
2.              Mishra, Neeraj Kumar, Vikram Jain, and Sandeep Sahu. "Survey on Recent Clustering Algorithms in Wireless Sensor Networks." International Journal of Scientific and Research Publications 3, no. 4 (2013).

3.              Gupta, Abhimanyu Kumar, and Rupali Patro. "Study of Energy Ecient Clustering Algorithms for Wireless Sensor Network." PhD diss., 2013.

4.              Walker, M.: How flowers conquered the world, BBC Earth News, 10 July 2009. news/newsid 8143000/8143095.stm

5.              Oily Fossils provide clues to the evolution of flowers, Science Daily, 5 April2001.

6.              Glover, Beverly J. Understanding flowers and flowering: an integrated approach. Oxford: Oxford University Press, 2007.

7.              Pavlyukevich, Ilya. "Lévy flights, non-local search and simulated annealing." Journal of Computational Physics 226, no. 2 (2007): 1830-1844.

8.              Waser, N.M., Flower constancy: definition, cause and measurement. The American Naturalist, 127(5), 1986, pp. 596-603.

9.              Yang, Xin-She. "Flower pollination algorithm for global optimization." In Unconventional Computation and Natural Computation, pp. 240-249. Springer Berlin Heidelberg, 2012.

10.           Yang, Xin-She. "Flower pollination algorithm for global optimization." In Unconventional Computation and Natural Computation, pp. 240-249. Springer Berlin Heidelberg, 2012.

11.           Reynolds A. M. and Frye M. A., Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search, PLoS One, 2, e354 (2007)

12.           Heinzelman, W.; Chandrakasan, A.; Balakrishnan, H. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences, Hawaii, HI, USA, 2000; pp. 1–10.




R. Balaji, B.Mohamed Faizal

Paper Title:

Design of Shunt Active Filters based on Phase Locked Loop and PI Controller 

Abstract:     This paper presents a active filter topology and its control technique. Active power filter topology is the most efficient way to compensate reactive power and lower order harmonics generated by non linear loads. The shunt active power filter was consider to be the most basic configuration for the APF. Different techniques have been applied to obtain a control signal for the active filters. One technique is Phase Locked Loop controller combined with PI controller, where the current waveform injected by the active filter is able to compensate the reactive power and load current harmonics. Here the simulation has been carried out through the MATLAB SimPowerSystems Toolbox and the results are tabulated.  With the proposed control strategy the total harmonic distortion is reduced to a great level and hence the power factor is also improved there by towards power quality enhancement.

  Active power filters, harmonics, Power quality, Phase Locked Loop.


1.             Salmerón and S. P. Litrán, “Improvement of the electric power quality using series active and shunt passive filters” IEEE Trans. Power Del.,vol.25, no.2,pp1058,  April 2010. 
2.             F. Z. Peng and D. J. Adams, “Harmonics sources and filtering approaches,” in Proc. Industry Applications Conf., Oct. 1999,  vol. 1, pp.448–455.

3.             J. C. Das, “Passive filters-potentialities and limitations,” IEEE Trans. Ind.  Appli., vol. 40, no. 1, pp. 232–241, Jan. 2004.

4.             Subhashish Bhattacharya, “An universal active Power filter controller system,”IEEE Trans 2009.

5.             H. L. Ginn, III and L. S. Czarnecki, “An optimization based method for selection of resonant harmonic filter branch parameters,” IEEE Trans .Power Del., vol. 21, no. 3, pp. 1445–1451, Jul. 2006.

6.             Liqing Tong, “A new control strategy for series  in series hybrid active power filter,” IEEE trans pp1553-1556 , 2008.

7.             H. Akagi, “Active harmonic filters,” Proc. IEEE, vol. 93, no. 12, pp.2128–2141, Dec. 2005.

8.             Ahad Kazami,” A Reference Detection  algorithm for series active power filters, aimed at current harmonics and reactive power compensation,”Proc. IEEE  pp 1761-1766, 2007

9.             J. W. Dixon, G. Venegas, and L. A. Moran, “A series active power filter based on a sinusoidal current-controlled voltage-source inverter,” IEEETrans. Ind. Electron., vol. 44, no. 5, pp. 612–620, Oct. 1997.

10.          M. Salehifer,” Hybrid active filter for harmonic suppression and reactive power compensation,”IEEE Trans .Oct 1999.

11.          F. Z. Peng, H. Akagi, and A. Nabae, “A novel harmonic power filter,” in Proc. IEEE/PESC, Apr. 1988, pp. 1151–1159.

12.          F. Z. Peng, H. Akagi, and A. Nabae, “A new approach to harmonic compensation in power systems-a combined system of shunt passive and series active filters,” IEEE Trans. Ind. Appl., vol. 26, no. 6,  pp.983–990, Nov./Dec. 1990.

13.          J.G. Pinto” A combined series active filter and passive filters for harmonics, unbalances and flicker compensation,” IEEE Trans.pp  54-59, 2007.
14.          Karuppanan. P and Kamala Kanta Mahapatra ” A Novel Active Power Line Conditioners using PLL synchronization and PI Controller ,”International Conference on  Future Engineering Trends(ICFET-2010) .




Anju Pratap, C. S. Kanimozhiselvi, R. Vijayakumar, K. V. Pramod

Paper Title:

Soft Computing Models for the Predictive Grading of Childhood Autism- A Comparative Study

Abstract:      Artificial intelligence technique is a problem solving method, by simulating human intelligence where reasoning is done from previous problems and their solutions. Soft computing consists of artificial intelligence based models that can deal with uncertainty, partial truth, imprecision and approximation. This article discusses about the performance of some soft computing models for the predictive grading of childhood autism. Now a day’s, childhood autism is a common neuro-psychological developmental problem among children.  Early and accurate intervention is needed for the correct grading of this disorder. Result demonstrates that soft computing techniques provide acceptable prediction accuracy in autism grading by dealing with the uncertainty and imprecision.

   soft computing, autism, naïve bayes model, neural network, classifier combination model.


1.             Cohen, Ira L., Vicki Sudhalter, Donna Landon-Jimenez, and Maryellen Keogh. "A neural network approach to the classification of autism." Journal of autism and developmental disorders 23, no. 3 , 1993 ,pp. 443-466.
2.             Arthi, K., and A. Tamilarasi. "Prediction of autistic disorder using neuro fuzzy system by applying ANN technique." International Journal of developmental neuroscience 26, no. 7, 2008, pp. 699-704.

3.             Florio, T., Einfeld, S., Tonge, B., & Brereton, A.” Providing an Independent Second Opinion for the Diagnosis of Autism Using Artificial Intelligence over the Internet”. Counseling, Psychotherapy, and Health, 5(1), The Use of Technology in Mental Health Special Issue, 5, no.1,2009, pp.232-248..

4.             Kannappan, Arthi, A. Tamilarasi, and Elpiniki I. Papageorgiou. "Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder." Expert Systems with Applications 38, no. 3 ,2011,pp.1282-1292.

5.             Pratap, Anju, and C. S. Kanimozhiselvi. "Application of Naive Bayes dichotomizer supported with expected risk and discriminant functions in clinical decisions—Case study." In Advanced Computing (ICoAC), 2012 Fourth International Conference on, pp. 1-4. IEEE, 2012.

6.             Pratap, Anju, and C. S. Kanimozhiselvi. "Predictive assessment of autism using unsupervised machine learning models." International Journal of Advanced Intelligence Paradigms 6, no. 2, 2014, pp. 113-121.




Le Ngoc Son

Paper Title:

Consistency Test in ANP Method with Group Judgment Under Intuitionistic Fuzzy Environment

Abstract:       The consistency test is one of the critical components both in AHP and ANP. It is necessary to make sure if the judgment result is accuracy and reliable. This paper stated a specific process of consistency test in ANP with group judgment under intuitionistic fuzzy environment. A two steps de-fuzzification technique with intuitionistic fuzzy number reduction and generalized mean computation is proposed to apply in this study. The group consistency is also fully tested in two stages. This proposed process exposes that it is comprehensive and feasible. Besides, the application of maximum eigenvalue threshold method, a new consistency test index to check for the consistency, is an advantage because it reduces a lot of operations.

    Consistency testing, group judgment, analytic network process, intuitionistic fuzzy.


1.              Kwiesielewicz, M., & Van Uden, E. (2004). Inconsistent and contradictory judgements in pairwise comparison method in the AHP. Computers & Operations Research, 31(5), 713-719.
2.              Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87-96.

3.              Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. Fuzzy Systems, IEEE Transactions on, 15(6), 1179-1187.

4.              L. N. Son, D. Ergu, P. X. Kien (2014). A New Approach for Dealing with Uncertain Degree in Group Judgment Aggregation using Triangular Intuitionistic Fuzzy Numbers. International Journal of Soft Computing and Engineering, 2014, 34-39.

5.              Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stochastic Environmental Research and Risk Assessment, 23(1), 75-91.

6.              Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers.Expert Systems with Applications, 39(3), 3000-3011.

7.              Zareinejad, M., Javanmard, H., & Arak, I. (2013). Evaluation and selection of a third-party reverse logistics provider using ANP and IFG-MCDM methodology.Life Science Journal, 10(6s), 350-5.

8.              L. N. Son (2014). A Proposed Model for Firm’s Technological Capability Assessment under Uncertain Environment. International Journal of Innovative Technology and Exploring Engineering, 91-95.

9.              Ergu, D., Kou, G., Peng, Y., & Shi, Y. (2011a). A New Consistency Index for Comparison Matrices in the ANP. In New State of MCDM in the 21st Century (pp. 47-56). Springer Berlin Heidelberg.

10.           Ergu, D., Kou, G., Peng, Y., Shi, Y. (2011b). A Simple Method to Improve the Consistency Ratio of the Pair-wise Comparison Matrix in ANP. European Journal of Operational Research, 213(1) ,246–259.

11.           Herrera-Viedma, E., Herrera, F., Chiclana, F., & Luque, M. (2004). Some issues on consistency of fuzzy preference relations. European journal of operational research, 154(1), 98-109.

12.           Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.

13.           Atanassov, K. T (1999). Intuitionistic fuzzy sets [M]. Physica-Verlag HD, 1-137.

14.           Saaty TL (1991). Some mathematical concepts of the analytic hierarchy process. Behaviormetrika 29:1–9.

15.           Mendel, J. M. (2004). Fuzzy sets for words: why type-2 fuzzy sets should be used and how they can be used. presented as two-hour tutorial at IEEE FUZZ, Budapest, Hongrie.

16.           Lee, E. S., & Li, R. J. (1988). Comparison of fuzzy numbers based on the probability measure of fuzzy events. Computers & Mathematics with Applications, 15(10), 887-896.

17.           Chen, S. J. J., Hwang, C. L., Beckmann, M. J., & Krelle, W. (1992). Fuzzy multiple attribute decision making: methods and applications. Springer-Verlag New York, Inc.




D. Venugopal, A. Jayalaxmi

Paper Title:

Congestion Management by Optimal Choice and Allocation of FACTS Controllers using Genetic Algorithm

Abstract:        Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective optimization approach for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in  power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence, Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. Genetic Algorithms (GA) are best suitable for solution of combinatorial optimization and multi-objective optimization problems. This paper presents optimal location of FACTS controllers considering Branch loading (BL), Voltage Stability (VS) and Loss Minimization (LM) as objectives at  a time using GA. The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered  to create congestion in the system. The developed algorithm show effective locations for the cases considered for single objective optimization studies.

     FACTS, Single objective optimization,SVC, TCSC, real parameter Genetic algorithms.


1.             N. G. Hingorani and L. Gyugyi, “Understanding FACTS Concepts and Technology of Flexible AC Transmission Systems”. Piscataway: IEEE Press, 1999.
2.             F. D. Galiana, K. Almeida, M. Toussaint, J. Griffin, and D. Atanackovic, “Assessment and control of the impact of FACTS devices on power system performance,” IEEE Trans. Power Systems, Vol. 11, No. 4, Nov. 1996.

3.             S. Gerbex, R. Cherkaoui, and A. J. Germond, "Optimal location of multi type FACTS devices in a power system by means of genetic algorithms," IEEE Trans.Power Systems, Vol. 16, pp. 537-544,2001.

4.             S.N.Singh and A.K.David, “Congestion Management by optimizing FACTS devices location”, IEEE Power Engineering Review,September 2000,pp.58-60.

5.             Keshi Reddy Saidi Reddy, Narayana Prasad Padhy, and R.N.Patel, “Congestion Management in Deregulated Power System using FACTS devices”IEEE 2006.

6.             Hadi Saadat, “Power System Analysis”, Mc Graw Hill Companies, 2004.

7.             B.Battaacharya,B.S.K.Goswami,”Optimal Placement of FACTS Devices using Genetic Algorithm for the increeased loadability of a power system”,World Academy Of Science and Technology ,2011.

8.             R.M.Mathur,R.K.Varma,”Thyristor Based FACTS Controllers for Electrical Transmission Systems”, JohnWiley&Sons Inc.,2002.

9.             Enrique A, Fuerte –Esquivel,C.R.Perez,H.A.Camacho,“FACTS Modelling and Simulation in Power Network”,John Wiley &Sons Ltd,2004.

10.          IEEE Special Stability Controls Working Group, “Static VAR Compensator Models for Power flow and Dynamic Performance simulation”, IEEE Transactions on Power Systems, Vol.9, No.1, February 1994, pp 229-240.

11.          H.Ambriz-perez, E.Acha and C.R. Fuerte-Esquivel, “Advanced SVC models for Newton–Raphson load flow and Newton Optimal Power flow studies”, IEEE.

12.          L.J.Cay, I.Erlich, “Optimal choice and allocation of FACTS devices using Genetic Algorithms”, 2004.

13.          M.Saravanam, S.MaryRajaSlochanal, P.V.Venkatesh,Prince Stephen Abraham.J, “Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation”In:Power Engineering Conference,716-721.

14.          J.G.Singh, S.N.Singh and S.C.Srivastava, “Placement of FACTS controllers for enhancing FACTS controllers”,IEEE 2006.

15.          James A.Momoh, M.E.El-Hawary, Rambabu Adapa, “A Review of Selected Optimal Power Flow Literature to 1993 Part-I: Non-linear and Quadratic Programming approaches”, IEEE Transactions on Power Systems, Vol. 14, No.1, February




Pradnya A. Khutade, Rashmi Phalnikar

Paper Title:

QoS Aware Web Service Selection and Ranking Framework  Based on Ontology

Abstract:  Consideration of QoS values for accurate web service Selection process has been highlighted in our previous survey paper. Further to that work, we claim that the best performance can be achieved by considering both functional and QoS properties. In this paper, we highlight the application of ontology to represent the non-functional requirements in web service discovery. We emphasize how Ontology if built and refined by domain experts can be used for web service discovery process with the purpose of reuse and improved design. Considering the importance of QoS and Ontology we present a new framework for web service selection which considers dimensionless matrix for web selection and promises to deliver desired ranking of web services according to user preference QoS.

      Non-Functional Requirements, QoS, Web Services, Service Discovery, Ontology.


1.              Phalnikar, R.; Khutade, P.A.; , "Survey of QoS based web service discovery," Information and Communication Technologies (WICT), 2012 World Congress on , vol., no., pp.657-661, Oct. 30 2012-Nov. 2 2012 doi: 10.1109/WICT.2012.6409157
2.              Yang Zhang, Bing-Yue Liu , Hong Wang,”A Method of Web Service Discovery based on Semantic Message Bipartite Matching for Remote Medical System”, ”, in proc, Journal of Theoretical and Applied Electronic Commerce Research ,2009.

3.              Wen Junhao , GuJianan, Jiang Zhuo, Zhu Yijiao ,”Semantic Web Service Selection Algorithm based on QoS Ontology”, In proc, IEEE 2011

4.              Guangjun Guo,Fei Yu, Zhigang Chen and Dong Xie ,“A Method for Semantic Web Service Selection Based on QoS Ontology”, In Proc, journal of computers, vol. 6, no. 2, February 2011

5.              Demian Antony D’Mello, V.S. Ananthanarayana, Santhi T.A ,“QoS Broker Based Architecture for Dynamic Web Service Selection”, In proc, Second Asia International Conference on Modelling & Simulation IEEE 2008

6.              Eyhab Al-Masri, Qusay H. Mahmoud,” QoS-based Discovery and Ranking of Web Services”,In Proc, International conference, IEEE 2007.

7.              DAML-S Coalition. DAML-S: Web Service Description for the Semantic Web. In Proc. International Semantic Web Conference (ISWC 02), 2002.

8.              Chen Zhou , Liang Tien Chia ,Bu-Sung Lee “Service Discovery and Measurement based on DAML QoS Ontology” May 10–14, 2005, Chiba, Japan.

9.              Alessio Carenini, Dario Cerizza, Marco Comerio, Emanuele Della Valle, Flavio De Paoli, Andrea Maurino, MatteoPalmonari, Andrea Turati,”GLUE2: aWeb Service Discovery Engine with Non-Functional Properties” 2008 IEEE.

10.           Vaneet Sharma, Mukesh Kumar, “Web Service Discovery Research: A Study of Existing Approaches”In Proc. of International Journal on Recent Trends in Engineering & Technology [IJRTET], Volume 5, Issue 1, 2011.

11.           T.R. Gruber, A translation approach to portable ontology specifications. Knowledge Acquisition,. 5(2): p. 199-220, 1993


13.           JyotishmanPathakNeerajKoulDoinaCarageaVasant G Honavar.” A Framework for Semantic Web Services Discovery” Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University. November 5, 2005.

14.           Y. Yue, X. L. Wang, and A. Y. Zhou, “Underlying techniques for web services:  a survey,” in proc, Journal of Software, vol. 15, No. 3, 2004, pp. 428-442.




Dewanshu Jain, Alok Gupta

Paper Title:

Bots Problem in Online Games

Abstract:   In this paper discussion about the bots issues in online games which is a serious threat to the online games business and causes a huge revenue loss to the industry has been highlighted. The behavior of bots, present security mechanisms and the shortcomings of existing technologies has been reviewed and some suggestions to improve the security against bots have been purposed.

 Bots, games, mechanisms, technologies.


1.             Mitterhofer, Stefan, Christopher Kruegel, Engin Kirda, and Christian Platzer. "Server-side bot detection in massively multiplayer online games." Security & Privacy, IEEE 7, no. 3 (2009): 29-36.
2.             Gaspareto, Otavio Barcelos, Dante Augusto Couto Barone, and André Marcelo Schneider. "Neural networks applied to speed cheating detection in online computer games." In Natural Computation, 2008. ICNC'08. Fourth International Conference on, vol. 4, pp. 526-529. IEEE, 2008.

3.             Xiao, Lan, Zhang Yi-Chun, Yang Cheng, and Zhang Ming-Kai. "An Investigation of Online Game Bots in China." In E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on, pp. 1-5. IEEE, 2010.

4.             As listed on the website of Wikipedia “”.

5.             Wu Chun, Zhu Guo-hun, Wu Yong-hua1, Xiang Rong. “The Study of Bot Technology for Online Games” IEEE

6.             R. Heeks, “Current Analysis and Future Research Agenda on ‘Gold Farming’: RealWorld Production in Developing Countries for the Virtual Economies of Online Games,” Working Paper Series, vol. 32, 2008.

7.             K. Warns, “Cheating Detection and Prevention in Massive Multiplayer Online Role Playing Games”, The Seventh Annual Winona Computer Acience Undergraduate Research Symposium,Winona, MN, pp.26-30, April 2007.

8.             J .Yan and H.J. Choi, “Security Issues in Online Games”, The Electronic Library, MCB, UP, Ltd,Vol. 20, No.2, pp. 125-133, 2002.

9.             P. Golle, N.Ducheneaut, ”Preventing bots from playing online games”, Computers in Entertainment, Vol.3(3), New York, ACM, pp.3-12, July  2005.

10.          Philippe Golle , Nicolas Ducheneaut, Keeping bots out of online games, Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, Valencia, Spain, pp.262-265, June 15-17, 2005.

11.          Kuan-Ta Chen , Andrew Liao , Hsing-Kuo Kenneth Pao , Hao-Hua Chu, “Game Bot Detection Based on Avatar Trajectory”, Proceedings of the 7th International Conference on Entertainment Computing, Pittsburgh, pp.94-105, September 25-27, 2008

12.          Gianvecchio, Steven, Zhenyu Wu, Mengjun Xie, and Haining Wang. "Battle of Botcraft: Fighting Bots in Online Games with Human Observational Proofs." (2009).

13.          GauthierDickey, Chris, Virginia Lo, and Daniel Zappala. "Using n-trees for scalable event ordering in peer-to-peer games." In Proceedings of the international workshop on Network and operating systems support for digital audio and video, pp. 87-92. ACM, 2005.

14.          Gaspareto, Otavio Barcelos, Dante Augusto Couto Barone, and André Marcelo Schneider. "Neural networks applied to speed cheating detection in online computer games." In Natural Computation, 2008. ICNC'08. Fourth International Conference on, vol. 4, pp. 526-529. IEEE, 2008.




Magnanil Goswami, Sudakshina Kundu

Paper Title:

Design and Analysis of Semi-Empirical Model Parameters for Short-Channel CMOS Devices

Abstract:    Recently analog circuit designers are interested in structured optimization techniques to automate the process of CMOS circuit design. Geometric programming, which makes use of monomial and posynomial expressions to model MOSFET parameters, represents one such approach. The extent of accuracy in finding a global optimal solution using this approach depends on the formulation of circuit and device equations as monomials and posynomials. Being pivotal in determining device transfer characteristic, transconductance and output conductance cast a direct impact on the overall CMOS circuit behavior.  In this paper we developed and substantiated high fidelity expressions of transconductance and output conductance for short-channel MOSFETs in monomial form

Keywords:   Analog CMOS circuit, geometric programming, global optimal solution, short-channel MOSFET, structured optimization.


1.              D. Stefanovic and M. Kayal, Structured analog CMOS design, Switzerland: Springer, 2008.
2.              Y. Taur, and T. H. Ning, Fundametals of Modern VLSI Devices, 2nd ed.: Cambridge University Press, 2009.

3.              S. Boyd, S. J. Kim, L. Vandenberghe, and A. Hassibi, “A tutorial on geometric programming," Stanford Univ. and Univ. of California, Tech. Rep., 2005. Available:

4.              P. Mandal and V. Visvanathan, “CMOS op-amp sizing using a geometric programming formulation," IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 20, no. 1, pp. 22─38, Jan. 2001.

5.              S. Boyd and L. Vandenberghe, (1997) Introduction to convex optimization with engineering applications. Stanford Univ., Stanford, CA. Available:

6.              M. Hershenson, S. Boyd, and T. Lee, “Optimal design of a CMOS op-amp via geometric programming," IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 20, no. 1, pp. 1─21, Jan. 2001. Available:

7.              Automated circuit design using active set solving process, by Maria del Mar Hershenson; Sunderarajan S. Mohan. (2012, Nov. 6). US 8307309B1. Available:
8.              P. E. Allen and D. R. Holberg, CMOS analog circuit design, 1st ed., U.K.: Oxford, 1987.




Imran Ijaz, Muhammad Hasan Islam, Maria Kanwal, Tahreem Yaqoob

Paper Title:

Securing user Authentication Through Customized X.509 in Cloud Computing

Abstract:     Cloud computing, a highly flexible and user friendly technology of the era providing benefits like accessibility, scalability, reliability and cost effeteness but on the other hand many security and privacy issues arises with the rapid increase of users. Weak authentication process is one of the biggest problem towards breach and most of the time it happened when credentials openly travel over the public internet.  To overcome this issue many authentication schemes have been made or modified but problem still persists. In our previous scheme which was based on tunneling using PPTP, effectively works in the scenario [1]. This work is the extension of previous scheme with a customized PKI based certificate to enhance security mechanism through encryption up to 2048 bits.

    Cloud Computing, Secure User Authentication, PKI, VPN Server, X.509.


1.                J. Yang and Z. Chen, "Cloud Computing Research and Security Issues," presented at the Computational Intelligence and Software Engineering (CISE), 2010 International Conference, Wuhan, 2010.
2.                T. Chen, H.Yeh, and W.Shih, "An Advanced ECC Dynamic ID-Based Remote Mutual Authentication Scheme for Cloud Computing," presented at the Multimedia and Ubiquitous Engineering (MUE), 2011 5th FTRA International Conference, Loutraki, 2011

3.                J. W. Yang, S. H. Kim, J. H. Kim, J. W. Choi, and C. H. Seo, "A Personalized Service Authentication System in Storage Cloud Computing Based D-CATV," presented at the Information Science and Service Science (NISS), 2011 5th International Conference on New Trends, Macao, 2011.

4.                Z. Shen and Q. Tong, "The Security of Cloud Computing System enabled by Trusted Computing Technology," presented at the Signal Processing Systems (ICSPS), 2010 2nd International Conference, Dalian, 2010.

5.                J. Choudhury, P. Kumar, M. Sain, H. Lim, and H. Jae-Lee, "A Strong User Authentication Framework for Cloud Computing," presented at the Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific, Jeju Island, 2011.

6.                S.Shanmugapriya, J. G. Begam, M. Anitha, and C. Napoleon, "Two Factor Authentication on Cloud," Journal of Computer Applications, vol. 5, 10 February 2012.[1]  J. Yang and Z. Chen, "Cloud Computing Research and Security Issues," presented at the Computational Intelligence and Software Engineering (CISE), 2010
International Conference, Wuhan, 2010.

7.                A.Yassin, H. Jin, A. Ibrahim, W. Qiang, and D. Zou, "A Practical Privacy-preserving Password Authentication Scheme for Cloud Computing," presented at the Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International, Shanghai, 2012.

8.                G. Revar and M. D. Bhavsar, "Securing User Authentication using Single Sign On in Cloud Computing," presented at the Engineering (NUiCONE), 2011 Nirma University International Conference, Ahmedabad, Gujarat, 2011.

9.                Imran Ijaz, "Design and Implementation of PKI (For Multi Domain Environment)," International Journal of Computer Theory and Engineering vol. 4, no. 4, pp. 505-509, 2012.

10.             Z. Zhang, J. Li, J. Xue-Feng, and Z. Zhang    "An Identity-Based Authentication Scheme In Cloud Computing," presented at the Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference, 2012.

11.             Z. Zhi-hua, L. Jian-jun, J. Wei, Z. Yong, and G. Bei, "An New Anonymous Authentication Scheme for Cloud Computing," presented at the Computer Science & Education (ICCSE), 2012 7th International Conference, Melbourne, VIC, 2012.

12.             Z. Javaid and I. Ijaz, "Secure user authentication in cloud computing " in Information & Communication Technologies (ICICT), 2013 5th International Conference, Karachi Pakistan, 2013, pp. 1 – 5.




Ijlal Hyder Rafiqi, Javed Ashraf, Mehboob ul Amin, M.T Beg, G.Mohiudin Bhat

Paper Title:

Performance Evaluation of Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (Mimo-Ofdm) for Alamouti Space Time Block Code using Various Digital Modulation Techniques

Abstract:      With the evolution in the telecommunication generations, more and more research is going on in the field of wireless communications. The purpose of these researches has always been to provide good network coverage across the region with higher data rates, accuracy and better performance. Control on coverage and performance has always been in focus and is achieved using better and better antennas.  Several techniques are used to get good performance out of the antennas system. One impressive way is the usage of multiple antennas techniques. The approach is to transmit and receive more unique data on a single radio channel. To increase the diversity gain with the use of multiple input multiple output (MIMO), OFDM is a good technology used at the physical layer. It provides robustness to frequency selective fading, high spectral efficiency and low computational complexity. So MIMO-OFDM generates a good basis for 3GPP (3rd Generation Partnership Project) and 4G telecommunication technologies as well as other wireless communications systems. With MIMO-OFDM as basis, different standards like WiMAX (Wireless Interoperability for Microwave Access) and LTE (Long Term Evolution) have been implemented now. The use of OFDM has some limitations when it is considered for uplink like high peak to average power ratio (PAPR).High PAPR leads to increase in Bit Error rate of the system, thereby decreasing the system performance. In this paper we investigate the percentage error and capacity comparison by using different digital modulation schemes like: BPSK, QPSK, 16PSK, 4QAM and 8QAM, in the presence of AWGN and Rayleigh Noise over Rayleigh Fading channel.

     Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM) ,Space Time Block Coding (STBC), Signal Error Rate(SER),Signal-to-Interference-noise-  ratio (SINR).


1.             , John A. C. “ Multicarrier Modulation for Data Transmission: An Idea Whose Time has Come “ IEEE Communications Magazine May 1990, pp. 5-14.
2.             Sheperd, Orriss, and Barton “Asymptotic Limits in Peak Envelope Power Reduction by Redundant Coding In Orthogonal Frequency-Division Multiplex Modulation” IEEE Transactions on Communications, Vol 46, No 1, January 1998 pp 5-10

3.             Wilkinson and Jones “ Combined Coding For Error Control and Increased Robustness to System Nonlinearities in OFDM “ Vehicular Technology Conference,
1996. 'Mobile Technology for the Human Race'. IEEE 46th , Volume: 2 , 28 April-1 May 1996, pp: 904 -908

4.             Vahid Tarokh and Hamid Jafarkhani “ On the Computation and Reduction of the Peak-to-Average Power Ratio in Multicarrier Communications “ IEEE Transactions of Communications, Vol 48, No 1, January 200 pp. 37-44

5.             Wilkinson and Jones “ Minimisation of the Peak-to-Mean Envelope Power Ratio of Multicarrier Transmission Schemes by Block Coding “Vehicular Technology Conference, 1995 IEEE 45th, Volume: 2, 25-28 July 1995 pp: 825 -829 

6.             Cimini, L.J., Jr.; Sollenberger, N.R.; “ Peak-to-average power ratio reduction of an OFDM signal using partial transmit sequences” Communications, 1999. ICC '99. 1999 IEEE International Conference on, Volume: 1, 6-10 June 1999  pp: 511 -515

7.             Paterson, Kenneth G. “ On the Existence and Construction of Good Codes with Low Peak-to-Average Power Ratios ” IEEE Transactions on Information Theory, Vol. 46, No. 6, Septermber 2000, pp. 1974-1987

8.             Ochiai, H.; Imai, H.; “Performance of Block Codes with Peak Power Reduction for Indoor Multicarrier Systems “ Vehicular Technology Conference, 1998. VTC 98. 48th IEEE, Volume: 1, 18-21 May 1998 pp: 338 -342

9.             Fan and Darnell Sequence Design for Communications Applications Copyright 1996 by Research Studies Press Ltd. Ch. 13

10.          Davis and Jedwab “ Peak-to-Mean Power Control in OFDM, Golay Complementary Sequences, and Reed-Muller Codes “ IEEE Transactions on Information Theory, Vol 45, No. 7, November 1999, pp. 2397-2417

11.          Paterson “ Generalized Reed-Muller Codes and Power Control in OFDM Modulation “ IEEE Transactions on Information Theory, Vol. 46, No. 1, January 2000, pp. 104-120

12.          MacWilliams and Sloane The Theory of Error-Correcting Codes North-Holland Publishing Company 1977 Chapters 13-15.

13.          Sweeny Error Control Coding John Wiley & Sons Copyright 2002.

14.          Jones and Wilkinson “Performance of Reed-Muller Codes and Maximum Likelihood Decoding Algorithm for OFDM”, IEEE Transactions on Communications, Vol. 47, No 7 July 1999, pp. 949-952

15.          Mehboob ul Amin, J.A Sheikh, S.A Parrah, G.M Bhat e “Performance evaluation of various MIMO based digital modulation techniques using minimum mean square error (MMSE) combining,” International journal of Network Engg, Elixir  63(2013) pp-18512-18517.




Shankhpal S.V., Dhawas N.A.

Paper Title:

New Approaches for Multiclass Classification

Abstract:       Classification is very important in data mining. It is nothing but categorization of data for its most effective and efficient use. In basic approach to storing data, data can be classified according its importance or how often it needs to be accessed decision tree is one of the classification technique. Decision tree is used to clarify and find solution   to complex problem. Structure of decision tree contains multiple possible solutions and displays it in a simple, easy to understand format. There is different algorithm used for classification. In this paper tree is constructed using the geometric structure of data. It builds small decision trees and gives better performance. Now we will use adaptive boosting method for boosting decision tree so it improving the accuracy of decision tree.

  GDT, Multiclass classification, Oblique decision tree, SVM


1.             A. Kołakowska and W. Malina, “Fisher sequential classifiers,” IEEE Trans. Syst., Man, Cybern. B, Cybern, Vol.       35, No. 5, Pp. 988–998, Oct. 2005.
2.             D. Dancey, Z. A. Bandar, and D. McLean, “Logistic model tree extraction from artificial neural networks,” IEEE Trans. Syst., Man, Cybern. B, Cybern, vol. 37, no. 4, pp. 794–802, Aug. 2007.

3.             Erick Cantú-Paz, Chandrika Kamath, ―Inducing Oblique Decision Trees with Evolutionary Algorithms. IEEE Transaction on Evolutionary Computation, Vol. 7, No. 1, February 2003.

4.             Haitang Zhang, Hongze Qiu “Sensitivity degree based fuzzy SLIQ decision tree.” 978-1-4244-7941-2/10/ ©2010 IEEE.

5.             J. Quinlan, ―Induction Of Decision Trees, Mach. Learn., Vol. 1, No. 1, Pp. 81–106, 1986.

6.             K. P. Bennett and J. A. Blue, ―A Support Vector Machine Approach To Decision Trees, In Proc. IEEE World Congr. Computer. Intell, Anchorage, AK, May 1998, Vol. 3, Pp. 2396–2401.

7.             L. Breiman, J. Friedman, R. Olshen, and  C. Stone,      ―Classification and Regression  Trees. Belmont, Ca: Wadsworth and Brooks, 1984, Ser. Statistics/Probability Series.

8.             Lior Rokach and Oded Maimon, “Top-Down Induction of Decision Trees Classifiers – A Survey.” IEEE Transactions on Systems, Man and Cybernetics: Part C, Vol. 1, No. 11, November 2002.

9.             M. F. Amasyali and O. Ersoy, “Cline: A new decision-tree family,” IEEE Trans. Neural Netw., vol. 19, no. 2, pp. 356–363, Feb. 2008.

10.          M. Lu, C. L. P. Chen, J. Huo, and X. Wang, “Multi-stage decision tree based on inter-class and inner-class margin of SVM,” in Proc. IEEE Int. Conf. Syst., Man, Cybern., San Antonio, TX, 2009, pp. 1875–1880.

11.          Naresh Manwani and P. S. Sastry, ―”Geometric Decision Tree”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 42, No. 1, February 2012.




Sonal Dubey, R. K. Pandey, S. S. Gautam

Paper Title:

Dealing with Uncertainty in Expert Systems

Abstract:        The aim of artificial intelligence is to develop tools for representing piece of knowledge and providing inference mechanism for elaborating conclusion of knowledge from stored information. The available knowledge is far from being certain, precise and complete. In Expert systems the word uncertainty is related to the working with inexact data, imprecise information, handling identical situation, reliability of the results etc. An expert system allows the user to assign probabilities, certainty factors, or confidence levels and many more techniques to any or all input data. This feature closely represents how most problems are handled in the real world. An expert system can take all relevant factors into account and make a recommendation based on the best possible solution rather than the only exact solution to handle such problems. This paper describes the various types of uncertainty, its sources and different approaches to handle uncertainty.

 fuzzy logic, soft computing uncertainty management. certainty factor, expert system


1.              Anuradha, Kumar Sachin,  Kumar Anubhav “Analysis of Information Uncertainty Management in Expert Systems” International Journal of Advanced Research in Computer Science and Software Engineering 3(9), September - 2013, pp. 899-903
2.              Liang Yeow Wei and Mahmud,Rohana “A Comparison Model for Uncertain Information in Expert System”  IEEE 2012 International Conference on Uncertainty Reasoning and Knowledge Engineering

3.              Negnevitsky Michael “Artificial Intelligence A Guide to Intelligent Systems” Second Edition Pearson Education Limited 2005.

4.              Ng Keung Chi and Abramson Bruce “Uncertainty Management in Expert Systems”  IEEE EXPERT April 1990

5.              Ramík Jaroslav  Book on ” Soft Computing: Overview and Recent Developments in Fuzzy Optimization” Listopad 2001

6.              Rasal Isram , Wicaksana I Wayan Simri “Rule-Based Expert System For Diagnosing Toddler Disease Using Certainty Factor And Forward Chaining” The Proceedings Of The 7th Icts, Bali, May 15th-16th, 2013 (Issn: 9772338185001)




Pratibha Soni, Prabhakar Sharma

Paper Title:

An Intrusion Detection System Based on KDD-99 Data using Data Mining Techniques and Feature Selection

Abstract:   Internet and internet users are increasing day by day. Also due to rapid development of internet technology, security is becoming big issue. Intruders are monitoring computer network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to prevent computer network from attacks. A comprehensive study of literatures proves that data mining techniques are more powerful technique to develop IDS as a classifier. Performance of classifier is a crucial issue in terms of its efficiency, also number of feature to be scanned by the IDS should also be optimized. In this paper two techniques C5.0 and artificial neural network (ANN) are utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of the five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better results with all most 100% accuracy. Performances were also verified in terms of data partition size.

    Decision tree, Feature Selection, Intrusion Detection System, Partition Size, Performance measures.


1.             S.Y. Wua, and E. Yen, “Data Mining based intrusion detectors”, ExpertSystems with Applications, 36, 2009, 5605-5612.
2.             G. Wang, J. Hao, J. Ma, and L. Huang, “ A new approach to intrusiondetection using Artificial Neural Networks and Fuzzy Clustering”, Expert Systems with Applications, 37, 2010, 6225-6232.

3.             V.B. Canedo, N.S. Marono, and A.A. Betanzos , “Feature Selection and

4.             Classification in Multiple Class Databases: An Application to KDDcup99 dataset”, Expert Systems with Application, 38, 2011, 5947-5957.

5.             R.M. Elbasionary, E.A. Sallam, T.E. Eltobely, and M.M. Fahmi,“A             Hybrid  network intrusion detection framework based on random Forest and weighted k-means”, Aim Shams Engineering Journal,4, 2013,753-762

6.             Z.A Baig., S.M Sait., and A.R. Shaheen, “GMDH-based networks for intelligent intrusion detection”, Engineering Applications of Artificial Intelligence, 26, 2013, 1731-1740.

7.             B. Luo, and J. Xia, “ A novel intrusion detection system based on feature  generation with visualization strategy”,Expert Systems with Applications, 41, 2014, 4139-4147.

8.             P. Giudici, and S. Figini, “Applied Data Mining for Business and Industry”, 2nd ed., John Wiley & Sons, April 2009.

9.             J. Han, and M. Kamber, “Data Mining Concepts and Techniques”, 2nd ed., Morgan Kaufmann Publishers, USA, 2006.

10.          J. Wang. “Data Mining: opportunities and challenge”, Idea Group, USA, 2003.

11.          K, J. Cios, W.Pedrycz, R.W. Swiniarski, and L. Kurgan. “ Data miningmethods for knowledge discovery”, 3rd printing, kluwer academic Publishers, USA, 2000.

12.          SPSS Clementine help file http// last accessed on Oct 2012.UCI Machine Learning Repository of machine learning databases (2010). University of California, school of Information and Computer Science, Irvine. C.A.,?ML.Repositary.html.




Magdi B. M. Amien, Tamadur El-Khansa Japir

Paper Title:

A Reliable Arrhythmias-Recognition Scheme Via Wavelet and Multiclass Support Vector Machine

Abstract:    Heart diseases (HD) are the number one cause of death globally, more people die annually from HDs than from any other cause, according to World-Health-Organization (WHO) 7.3 million were died due to coronary heart disease in 2008. Electrocardiogram (ECG) interpretation is most widely used to detect the abnormality of the heart. A reliable computer programs could lead to enhanced visual interpretation, and significant-increase of diagnosis-efficiency. This study introduced a novel method for ECG classification; fifteen different records of five rhythms from “MIT-BIH” Arrhythmia Database have been used to evaluate the implemented algorithms. The proposed approach consists of three distinct stages. In the first stage a preprocessing of different-steps is done to remove the baseline wander, power line interference and to enhance morphological properties. Secondly Daubechies are chosen and implemented as mother-wavelet-function to extract ten features of ECG signals, in the final stage Support-Vector-Machines (SVM), has been used as Multi-class classifier and decision making algorithm. The performance of the proposed method has bees evaluated in terms of accuracy, and specific accuracy. The experimental results have shown that the proposed system achieves validity as competitive results quality-wise, and the accuracy-rate of classification of Normal sinus Rhythm (N), Bundle Branch Block (RBBB), Atrial Premature Beat (APB), 3Premature Ventricular Contraction (PVC), Fusion Heart Beats (F), and Unclassified Heart Beats (P) were 90.0%, 100%, 66.6%, 100%, 100%, and 100%, respectively.

 Arrhythmias classification, Wavelet, MSVM.


1.             S. Karpagachelv, M.Arthanari, and M.Sivakumar " ECG Feature Extraction Techniques - A Survey Approach" International Journal of Computer Science and Information Security, Vol. 8, No. 1, April 2010
2.             M.H. Kadbi, J. Hashemi, H.R. Mohseni and Maghsoudi "Classification of ECG Arrhythmias Based-on Statistical and Time-Frequency Features".

3.             B. Ramli, and P. A. Ahmad, “Correlation analysis for abnormal ECG signal features extraction,” 4th National Conference on  Telecommunication Technology, 2003. NCTT 2003 Proceedings, pp. 232-237, 2003.

4.             Alexakis, H. O. Nyongesa, R. Saatchi, N. D. Harris, C. Davies, C. Emery, R. H. Ireland, and S. R. Heller, “Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycaemia,” Conference on computers in Cardiology, pp. 537-540, IEEE, 2003

5.             Qibin Zhao, and Liqing Zhan, “ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines,” International Conference on Neural Networks and Brain, ICNN&B ’05, vol. 2, pp. 1089-1092,2005

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

7.             Mazhar B. Tayel, and Mohamed E. El-Bouridy, “ECG Images Classification Using Feature Extraction Based On Wavelet Transformation And Neural Network,” ICGST, International Conference on AIML, June 2006.

8.             F. Sufi, S. Mahmoud, I. Khalil, “A new ECG obfuscation method: A joint feature extraction & corruption approach,” International Conference on Information Technology and Applications in Biomedicine, 2008. ITAB 2008, pp. 334-337, May 2008.

9.             ECG Feature Extraction Techniques - A Survey Approach S.Karpagachelvi, Dr.M.Arthanari, M.Sivakumar, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 1, April 2010

10.          S. Z. Fatemian, and D. Hatzinakos, “A new ECG feature extractor for biometric recognition,” 16th International Conference on Digital SignalProcessing,pp. 1-6, 2009

11.          Branislav Vuksanovic & Mustafa Alhamdi,”AR-based Method for ECG Classification and Patient Recognition”, 2013

12.          Shantha Selva Kumari, R. ; Mepco Schlenk Eng. Coll., Sivakasi ; "Design of Optimal Discrete Wavelet for ECG Signal Using Orthogonal Filter Bank", Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on  (Volume:1 )

13.          National Heart Lung and Blood Institute, "What Is the Heart" 

14.          Duan, K. B.; Keerthi, S. S. (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study". Multiple Classifier Systems. Lecture Notes in Computer Science 3541. p. 278. ISBN 978-3-540-26306-7.

15.          Duan, K. B.; Keerthi, S. S. "Which Is the Best Multiclass SVM Method? An Empirical Study". Multiple Classifier Systems. Lecture Notes in Computer Science 3541. p. 278.