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Volume-1 Issue-3, July 2011, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

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Subhransu Padhee, Abhinav Gautam, Yaduvir Singh, and Gagandeep Kaur

Paper Title:

A Novel Evolutionary Tuning Method for Fractional Order PID Controller

Abstract:   PID controller is a well known controller which is used in most control applications. Around 90% control applications use PID controller as the controlling element. The tuning of PID controller is mostly done using Zeigler-Nichols tuning method. But there are some inherent drawbacks of Ziegler-Nichols based tuning. For the optimal tuning of controller, the tuned values have to be changed using computer simulation to meet the process needs. In PID controller the derivative and the integral order are in integer. Fractional order PID (FOPID) is a special kind of PID controller whose derivative and integral order are fractional rather than integer. The key challenge of designing FOPID controller is to determine the two key parameters λ (integral order) and μ (derivative order) apart from the usual tuning parameters of PID using different tuning methods. Both λ and μ are in fraction which increases the robustness of the system and gives an optimal control. This paper proposes a novel tuning method for tuning λ and μ of FOPID using genetic algorithms.

 Fractional order PID, genetic algorithms, PID, Ziegler-Nichols


1.        J S Saini, M Gopal and A P Mittal, “Genetic algorithm based PID tuner,” IE(I) Journal, vol. 85, pp. 216-221, 2005
2.        Jun-Yi Cao, Jin Liang and Bing-Gang Cao, “Optimization of fractional PID controllers based on genetic algorithms,” Proc. 4th Int. Conf. Machine Learning and Cybernetics, 2005, pp. 5686-5689

3.        F Gao and H Q Tong, “A novel optimal PID tuning and online tuning based on particle swarm intelligence,” Proc. Int. Conf. Sensing, Comput. and Automation, 2006, pp. 182-186

4.        Jun-Yi Cao and Bing-Gang Cao, “Design of fractional order controller based on particle swarm optimization,” Int. J. Control, Automation and Systems, vol. 4, no. 6, 2006, pp. 775-781

5.        Schlegel Milos , C Ech Martin, “The fractional order PID controller outperforms the classical one,” Proc. 7th Int. Sci. & Tech. Conf.- Process Control, 2006, pp. 1-7

6.        Dingyu Xue, Chunna Zhao, YangQuan Chen, “Fractional order PID control of a DC motor with elastic shaft: Acase study,” Proc. 2006 american control conference, 2006, pp. 3182-3187

7.        Majid Zamani, Masoud Karimi-Ghartemani, Naseer Sadati, “FOPID controller design for robust performance using particle swarm optimization,” Fractional Calculus and Applied Analysis, vol. 10, no. 2, 2007, pp. 169-187

8.        Deepyaman Maiti, Sagnik Biswas and Amit Konar, “Design of a fractional order PID controller using particle swarm optimization technique,” Proc. ReTIS’08, 2008.

9.        Chuang Zhao, Xiangde Zhang, “The application of fractional order PID controller to position servomechanism,” Proc. 7th World Congress Intelligent Control and Automation, 2008, pp. 3380-3383

10.     Tushar Jain and M.J Nigam, “Optimization of PD-PI controller using swarm intelligence,” Intl. J.Computational Cognition, vol. 6, no. 4, 2008, pp. 55-59

11.     Andrezej Dzielinski and Dominik Sierociuk, “Simulation and experimental tools for fractional order control education,” Proc. IFAC’08, 2008.

12.     Varsha Bhambhani and YangQuan Chen, “Experimental study of fractional order PI controller for water level control,” Proc. IEEE Conf. Decis. Cont., 2008, pp. 1791-1796.

13.     Ramiro S Barbosa, J A Tenreiro Machado, Isabel S Jesus, “On the fractional PID control laboratory servo system,” Proc. 17th world congress IFAC, 2008, pp. 15273

14.     Varsha Bhambahani, Yangquan Chen, Dingyu Xue, “Optimal fractional order proportional integral controller for varying time delay systems,” Proc. 17th world congress IFAC, 2008, pp. 4910-4915

15.     Mithun Chakraborty, Deepyaman Maiti and Amit Konar, “The application of stochastic optimization algorithms to the design of a fractional-order PID controller,” 2008 IEEE region 10 colloquium, 3rd ICIIS, 2008, pp. 1-6

16.     Hyo-Sung Ahn, Varsha Bhambhani and YangQuan Chen, “Fractional-order integral and derivative controller design for temperature profile control,” in Proc. 2008 CCDC-2008, pp. 4767-4771

17.     D. Devaraj and B. Selvabala, “Real coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral-derivative controller in automatic
voltage regulator system,” IET Gener. Transm. Distrib., vol. 3, issue 7, 2009, pp. 641-649

17.Bijoy K. Mukherjee and Santanu Metia, “Fractional order modeling and GA based tuning for analog realization with lossy capacitors of a PID controller,” Proc. Int. Multi Conf. of Engg. Comput. Sci., vol II, 2009

18.     Ying Luo and YangQuan Chen, “Fractional order [PD] controller for robust motion control: Tuning procedure and validation,” 2009 American control conf., 2009, pp. 1412-1417

19.     Li Meng, Dingyu Xue, “Design of an optimal fractional-order PID controller using multi-objective GA optimization,” Proc. Chinese Contr. Desi. Conf., 2009, pp. 3849-3853

20.     Ivo Petras, “Fractional-order feedback control of a DC motor,” J. Elect. Engg., vol. 60, no. 3, 2009, pp. 117-128

21.     Arijit Biswas, Swagatam Das, Ajith Abraham, Sambarta Dasgupta, “Design of fractional order PIλDμ controller with improved differential evolution,” Engineering Applications of Artificial Intelligence, 22, 2009, pp. 343-350

22.     Venu Kishore Kadiyala, Ravi Kumar Jathoth, Sake Pothalaiah, “Design and implementation of fractional order PID controller for aerofin control system,” Proc. 2009 World Congress NaBIC, 2009, pp. 696-701

23.     Mohamed Karim Bouafoura, Naceur Benhadj Braiek, “PID controller design for integer and fractional plants using piecewise orthogonal functions,” Commun Nonlinear Sci Number Simulat 15, 2010, pp. 1267-1278

24.     Ammar A Aldair, and Weiji J Wang, “Design of fractional order controller based on evolutionary algorithm for a full vehicle nonlinear active suspension system,” Int. J. Contr Automation, vol. 3, no. 4, 2010, pp. 33-46

25.     Juan J. Gude and Evaristo Kahoraho, “analysis and performance comparison of PID and fractional PI controllers,” Proc. 9th Int. Symps. Dynamics and Control of Process Systems, 2010, pp. 503-508

26.     Fabrizio Padula, Antonio Visioli, “Tuning rules for optimal PID and fractional order PID controllers,” Journal of Process Control, 21, 2011, pp. 69-81






Abhishek Katariya, Amita Yadav, Neha Jain

Paper Title:

Performance Elevation Criteria for OFDM under AWGN Fading Channel using IEEE 802.11a

Abstract:   In this paper, the BER performance of OFDM-BPSK 16 –QAM and 64–QAM system over AWGN fading channel has been reported. Orthogonal Frequency Division Multiplexing (OFDM) is a key technique for achieving high data rates and spectral efficiency requirements for wireless communication systems. However in fading environments the bit error rate (BER) increases. The performance can be improved by using some kind of channel coding. This form of OFDM is called coded-OFDM (COFDM). This paper presents a modeling and simulation of OFDM based on IEEE 802.11a standard. The flexibility of this model is because fading parameters compared to only one in AWGN fading model, which helps to analyze the severity of fading more deeply and this model is versatile enough to represent fading. Finally simulations of OFDM signals are carried with AWGN faded signal to understand the effect of channel fading .The performance of OFDM is compared in terms of BER vs SNR for different modulation formats.

  BER, OFDM Additive white Gaussian noise (AWGN), BPSK ,QAM


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Archana Shende, Subhash Mishra , Shiv Kumar

Paper Title:

Comparison Of Different Parameters Used In Gmm Based Automatic Speaker Recognition

Abstract:   The performance of Speaker recognition systems has improved due to recent advances in speech processing techniques but there is still need of improvement. In this paper we present the comparison of different parameters used in automatic speech recognition system to increase the accuracy of the system. The main goal here is a detailed evaluation of the parameters used in Automatic speech recognition system such as window type, MFCC frame size, number of Gaussian mixtures and GMM & VQ/GMM technique .In this paper we propose a decision function by using vector quantization techniques to decrease the training model for GMM in order to reduce the processing time.

  Gaussian Mixture Model (GMM), Mel Frequency Cepstral Coefficient (MFCC), Speaker Identification (SI), Speaker Verification (SV), Vector Quantization (VQ).

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2002, pp. 4072-4075.

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Bangladesh ISBN 984-32-1804-4 561.

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10.     “Vector Quantization Decision Function for Gaussian Mixture Model Based Speaker Identification”.2008 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS2008) Swissôtel Le Concorde,Bangkok,Thailand .





Vijender Singh, Deepak Garg

Paper Title:

Survey of Finding Frequent Patterns in Graph Mining: Algorithms and Techniques

Abstract:   Graphs become increasingly important in modeling complicated structures, such as circuits, images, chemical compounds, protein structures, biological networks, social networks, the web, workflows, and XML documents. Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing and text retrieval with the increasing demand on the analysis of large amounts of structured data; graph mining has become an active and important theme in data mining.

  Subgraphs, Graph Mining, gSpan


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Abhishek Katariya, Neha Jain, Amita Yadav

Paper Title:

Performance Elevation Criteria Of Rs Coded Ofdm Transmission Over Noisy Channel

Abstract:   OFDM has recently been applied widely in wireless communication system due to its high data rate transmission capability with high bandwidth efficiency. Error control codes are used to protect information from errors that can occur during transmission Reed Solomon (RS) codes is one of the most important and best known classes of non binary [5].In the present study a simulink of RS are done using single carriers transceivers, to observe the performance of RS code and choose the effective parameters that can improve OFDM system. Evaluation of Bit Error Rate (BER) performance for RS code as a function of code rate and block size are tested. This testing is applied to single carrier system with different channel model using QPSK technique. BER performance over Rayleigh channel and Rician channels are done respectively. In this paper, the system throughput of a working OFDM system has been enhanced by adding RS coding. The bit error rate (BER) performance degradation of OFDM system is investigated in different channel. We consider the effects of BER both in Rayleigh and Rician. The environment theoretical approximate calculation method is also derived of BER for binary using quaternary phase shift keying (QPSK) modulation schemes in OFDM systems. In addition, the method of BER calculation is validated by simulinks. In this paper Improvement of performance elevation criteria of RS coded OFDM TRANSMISSION over noisy channel is analyzed using simulink. The Simulink Communication tool and basic library tools are use with standard values in IEEE802.11A.

  BER, OFDM, RS Code, Fading Channels, QPSK, Simulinks


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12.     John G. Proakis, Masoud Salehi, “communication system using MATLAB” Thomson Asia Pvt. Ltd., Singapore, 2003.

13.     T. Rappaport, “ Wireless Communications Principles and Practice”, Prentice Hall, 1996.

14.     Iskander, Cyril-Daniel, “A MATLAB-based Object-Oriented Approach to Multipath Fading Channel Simulation”, a MATLAB Central submission available in www.mathworks.com.





Md. Robiul Hoque, Md. Rashed-Al-Mahfuz

Paper Title:

A New Approach in Spatial Filtering to Reduce Speckle Noise

Abstract:   Speckle noise gives a grainy appearance in radar, SAR (Synthetic Aperture Radar), MRI (Magnetic Resonance imaging) images as well as ultrasound medical images. It reduces the image contrast, which has a direct negative effect on texture-based analysis of the imageries. In this paper, a new approach for speckle reduction technique has been proposed and then its performances on simulated (image with artificial speckle noise) imageries with other existing filtering methods has been compared. It has been found that, the proposed technique has better performance than others.

  Image enhancement, Noise Reduction, Spatial Filter, Speckle Noise

1.        M. Simard, “ Extraction of information and speckle noise reduction in SAR images using the wavelet transform,”Geoscience and Remote Sensing Symposium Proceedings, 1998 IEEE International, Volume 1, p. 4-6, Seattle, WA, USA, Jul 1998.
2.        R. K. Raney, “Rada Fundamentals: Technical Perspective. Chapter 2 in Principles and Applications of Imaging Radar, Manual of Remote Sensing,”, Third Edition, Volume 2, ASPRS, John Wiley and Sons Inc., Toronto, 1998.

3.        Rafael C. Gonzalez and Richard E. Woods “Digital Image Processing,” Second Edition, ISBN 81-7808-6229-8, 2002.

4.        L. Gagnon and F.D. Smaili: 'Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavelets', SPIE Proc. #2759, pp. 1424,1996.

5.        M. Mansourpour, M.A. Rajabi, and J.A.R. Blais, “Effect and Performance of Speckle Noise Reduction Filter On Active RADAR and SAR Images,” ISPRS Volume Number: XXXVI-1/W41, Ankara, Turkey, Feb 2006.

6.        K. Thangavel, R. Manavalan, and I. Laurence Aroquiaraj, “Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study,” ICGST-GVIP Journal, ISSN 1687-398X, Volume (9), Issue (III), June 2009.

7.        L. Gagnon and F.D. Smaili: “Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavele,” SPIE Proc. #2759, pp. 1424,1996.






Supriya Khaitan, Supriya Raheja

Paper Title:

Finding Optimal Attack Path Using Attack Graphs: A Survey

Abstract:   As the traditional methods, the result of vulnerability scanning can’t directly reflect complex attack routes existing in network, so the concept of attack graph comes. After analyzing host computer, devices link relation and the characteristic of attack, the model of network security status was built. Attack graphs are one of the important tools for analyzing security. A lot of research has been done on issues such as scalable and time efficient ways of generation of attack graphs. The intent of this paper is to study different ways to generate an attack graph and to provide future scope for research on these attack graphs.

  Attack Graph, Attack Path, Network Security, optimal attack path.


1.        F.Cuppens and R. Ortalo, ―LAMBDA: A Language to Model a Database for Detection of Attacks, Recent Advances in Intrusion Detection (RAID) 2000, Lecture Notes in Computer Science 1907, H. Debar, L. Me, and F. Wu, Eds., Berlin: Springer Verlag, 2001.
2.        R. Ritchey and P. Amman,Using Model Checking to Analyze Network Vulnerabilities, Proceedings of the IEEE Symposium on Security and Privacy, pp. 156-165, 2000.

3.        L. P. Swiler, C. Phillips, D. Ellis, and S. Chakerian, ―Computer-Attack Graph Generation Tool, Proceedings of the Second DARPA Information Survivability Conference & Exposition (DISCEX II), Los Alamitos, California, vol. II, pp. 307-321, IEEE Computer Society, 2001.

4.        Xiaochun Xiao, Tiange Zhang, Huan Wang, Gendu Zhang ―A Component-Centric Access Graph Based Approach to Network Attack Analysis, in International Seminar on Future Information Technology and Management Engineering pp.171-176, 2008

5.        Jan Magott, Marek Woda ―Evaluation of SOA security metrics using attack graphs, Third International Conference on Dependability of Computer Systems DepCoS-RELCOMEX pp. 277-284 ,IEEE Computer Society , 2008

6.        P. Ammann, D. Wijesekera, and S. Kaushik, ―Scalable, Graph-Based Network Vulnerability Analysis, Proceedings of the 9th ACM Conference on Computer and Communications Security, New York: ACM Press, 2002, 217–224.

7.        Marcel Frigault, Lingyu Wang Measuring Network Security Using Bayesian Network-Based Attack Graphs Computer Software and Applications, 2008. COMPSAC'08. 32nd Annual IEEE International, pp.698-703, 2008

8.        M. Artz, NETspa, A Network Security Planning Architecture, M.S. Thesis, Cambridge: Massachusetts Institute of Technology, May 2002.

9.        Nessus, ―Nessus Security Scanner, [10] O. Sheyner, S. Jha, J. M. Wing, R. P. Lippmann, and J. Haines, Automated Generation and Analysis of Attack Graphs, in 2002 IEEE Symposium on Security and Privacy. Oakland, California, 2002.

10.     B. Schneier, ―Attack Trees, Dr. Dobbs Journal, December, 1999.

11.     T. Tidwell, R. Larson, K. Fitch, and J. Hale, ―Modeling Internet Attacks, Proceedings of the Second Annual IEEE SMC Information Assurance Workshop, United States Military Academy, West Point, New York, June 2001: IEEE Press, 2001, pp. 54–59.

12.     A. Wool, ―A Quantitative Study of Firewall Configuration Errors, IEEE Computer, vol. 37, pp.62–67, 2004. 

13.     D. Turner, S. Entwisle, O. Friedrichs, D. Hanson, M. Fossi, D. Ahmad, S. Gordon, P. Szor, E. Chien, F. Perriot, and P. Ferrie, ―Symantec Internet Security Threat Report, Trends for January 1,2004–June 30, 2004, vol. VI, September 2004.

14.     G. Cohen, M. Meiseles, and E. Reshef, ―System and Method for Risk Detection and Analysis in a

15.     Computer Network, USA: Skybox Security Ltd., 2004.

16.     S. Jajodia, S. Noel, and B. O’Berry, ―Topological Analysis of Network Attack Vulnerability, Managing Cyber Threats: Issues, Approaches and Challenges, Kumar, Kluwer Academic Publisher, 2003.

17.     J. Dawkins and J. Hale, ―A Systematic Approach to Multi-Stage Network Attack Analysis,Proceedings of the Second IEEE International Information Assurance Workshop (IWIA’04), IEEE Computer Society, 2004.

18.     ]T.Zhang, Ming-Zeng, Dong, Liang Sun, An Effective method to generate Attack Graph Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005

19.     Vaibhav Mehta, Constantinos Bartzis, Haifeng Zhu, Ranking Attack Graphs, IEEE Computer Society, 2006.

20.     Somak Bhattacharya, S. K. Ghosh ―An Attack Graph Based Risk Management Approach of an Enterprise LAN Journal of Information Assurance and Security 119-127, 2008

21.     Yue Chen, Barry Boehm Luke Sheppard ―Value Driven Security Threat Modeling Based on Attack Path Analysis Proceedings of the 40th Hawaii International Conference on System Sciences , 2007

22.     Irina Petreska, Igor Tomovski, Eugenio Gutierrez ― Application of modal analysis in assessing attack vulnerabilityof complex networks Commun Nonlinear Sci Numer SimulatScience Direct pp. 1008-1018,2009

23.     N Ghosh, S Nanda, S.K Ghosh ,―A quantative approach towardsdetection of an optimal attack path in wireless network using modified  PSO technique , IEEE proceedings , 2009






Arun Kumar Gandhi , S.S.Tyagi

Paper Title:

Security Enhancement in Elliptic Key Cryptography Using Character Based Method

Abstract:   It is widely recognized that data security will play a central role in the design of future IT system. Elliptic key cryptography recently gained lot of attention in industries when we talk about security. This paper discusses the basic operation how ECC is more secure than other public key cryptosystems and also how security can be enhanced in ECC by using character-based-method.

 Elliptic Key Cryptography, Security


1.        Darrel Hankerson, Julio Lopez Hernandez, Alfred Menezes, Software Implementation of Elliptic Curve Cryptography over Binary Fields, 2000.
2.        M. Brown, D. Hankerson, J. Lopez, A. Menezes, Software Implementation of the NIST Elliptic Curves Over Prime Fields, 2001.

3.        Certicom, Standards for Efficient Cryptography, SEC 1: Elliptic Curve Cryptography, Version 1.0, September 2000.

4.        Certicom, Standards for Efficient Cryptography, SEC 2: Recommended Elliptic Curve Domain Parameters, Version 1.0, September 2000

5.        Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, Handbook of Applied Cryptography, CRC Press, 1996

6.        I. F. Blake, G. Seroussi, and N. P. Smart. Elliptic curves in cryptography, volume 265 of London Mathematical Society Lecture Note Series.Cambridge University Press, Cambridge, 2000. Reprint of the 1999 original.

7.        D. Shanks. Class number, a theory of factorization, and genera. In 1969 Number Theory Institute (Proc. Sympos. Pure Math., Vol. XX, State Univ. New York, Stony Brook, NY, 1969), pages 415440. Amer. Math. Soc., Providence, RI, 1971.

8.        J. H. Silverman. The arithmetic of elliptic curves, volume 106 of Graduate Texts in Mathematics. Springer-Verlag, New York, 1986.

9.        W. Trappe and L.Washington. Introduction to cryptography with coding theory, (2nd ed.). Prentice Hall, Upper Saddle River, NJ, 2006.

10.     L. C. Washington. Elliptic curves. Number theory and cryptography, (2nd ed.). Chapman & Hall/CRC, New York, NY, 2008.

11.     A. S. Tanenbaum, "Modemn Operating Systems", Prentice Hall, 2003.[3] M. J. B. Robshaw, "Block Ciphers", Technical Report, RSA Laboratories, Number TR - 601, July 1994.

12.     H.M. Heys and S.E. Tavares, "On the Security of the CAST Encryption Algorithm," Proceedings of the Canadian Conference on Electrical and Computer Engineering, Halifax, Nova Scotia, Sep 1994, pp. 332-335.

13.     R. Rivest, ―The encryption algorithm, in Fast Software Encryption, ser. LNCS, vol. 1008. Springer-Verlag, 1995, pp. 86–96.

14.     M.A. Viredaz and D.A. Wallach, ―Power Evaluation of a Handheld Computer: A Case Study, WRL Research Report, 2001/1.

15.     P. Ruangchaijatupon, P. Krishnamurthy, ''Encryption and Power Consumption in Wireless LANs-N,‘‘ The Third IEEE Workshop on Wireless LANs - September 27-28, 2001- Newton, Massachusetts.





Priti Narwal, S.S. Tyagi

Paper Title:

Density Based Protocol For Head Selection In Wireless Sensor Networks

Abstract:   In wireless sensor networks nodes have several limitations such as limited battery life, low computational capability, short radio transmission range and small memory space. However, the most severe constraint of the nodes is their limited energy resource because they cease to function when their battery has been depleted. To reduce energy usage in wireless sensor networks, many cluster-based algorithms have been proposed. Among those proposed, LEACH (Low Energy Adaptive Clustering Hierarchy) is a well-known cluster-based sensor network architecture. In this paper, subtractive clustering technique, which is an enhancement in the basic LEACH protocol to deal with its severe shortcomings with respect to handling of node‘s non-uniform and time variant energy distribution.

  Clustering, Density based clustering, LEACH protocol, Subtractive clustering, Wireless Sensor Networks


1.        Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella, Energy Conservation in Wireless Sensor Networks: a Survey, Ad Hoc Networks, 2009.
2.        W. R. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An Application-Specific protocol Architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, October, 2002.

3.        W. R. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks,” in Proceedings of the ACM MobiCom99.

4.        C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Network,” IEEE J. Select. Area Commun., vol 15, pp. 1265-1275, Sept 1997.

5.        J. H. Ryu, S. Song, and D. H. Cho, “Energy-Conserving Clustering Scheme for Multicasting in Two-tier Mobile Ad-Hoc Networks,” Elec-tron. Lett., vol. 37, pp. 1253- 1255, Sept 2001.

6.        T. C. Hou and T. J. Tsai, “Distributed Clustering for Multimedia Support in Mobile Multihop Ad Hoc Network,” IEICE Trans. Commun., vol. E84B, pp. 760- 770, Apr 2001.

7.        Wendi Rabiner Heinzelman, Anantha Chanfrakasan, and Hari Balakrishnam, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” 2000 IEEE The Hawaii International Conference on System Sciences Proceeding, January 2000.

8.        A. Manjeshwar and D. P. Agarwal, “APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks,” Proc.Intl. Parallel and Distrib. Proc. Symp., 2002, pp. 195– 202.

9.        P. Tillapart, T. Thumthawatworn, P. Pakdeepinit, T. Yeophantong, S. Charoenvikrom and J. Daengdey, “Method for Cluster Heads Selection in Wireless Sensor Network,” Proc. of the 2004 IEEE Aerospace Conference, Big Sky, Montana, USA, 2004.

10.     C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” in Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM), 2000.

11.     D. Ganesan et al., „„Highly-Resilient, Energy-Efficient Multipath Routing in Wireless Sensor Networks,‟‟ Mobile Computing and Communications Review, Vol. 1, No. 2, 2002.

12.     R. Kannan, et al., „„Sensor-Centric Quality of Routing in Sensor Networks,‟‟ Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom03), Vol. 1, Apr. 2003.

13.     B. Krishnamachari, D. Estrin, S. Wicker, „„Modelling Data-Centric Routing in Wireless Sensor Networks,‟‟ Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom02), New York, June 2002.

14.     N. Bulusu et al., „„Adaptive Beacon Placement,‟‟ Proceedings of the 21st International Conference on Distributed Computing Systems (ICDCS21), Phoenix, AZ, Apr. 2001, pp. 489ff.

15.     P. Bergamo, G. Mazzini, „„Localization in Sensor Networks with Fading and Mobility,‟‟ Proceedings of the 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC02), Vol. 2, Sept. 2002.

16.     Q. Li et al., „„Distributed Algorithms for Guiding Navigation Across a Sensor Network,‟‟ Proceedings of the 9th ACM International Conference on Mobile Computing and Networking (MobiCom03), San Diego, CA, Sept. 2003.






Roli Pradhan, KK Pathak, VP Singh

Paper Title:

Application of BPNN in the analysis of SBI’s Credit Capacity

Abstract:   During the existing business scenario much need exists for a system that can predict the failure of any firm with accuracy much before the bankruptcy actually occurs. Credit decisions by commercial banks are based to a large extent on the financial statements provided by corporate borrowers as monitored using financial ratios suggesting their financial position. This paper uses the tailored back-propagation neural network endeavors to predict the financial ratios expressing the position of a firm to regulate the bankruptcy and assess the credit risks. It first estimates the financial ratio for a firm from 2001-2008 to the train the BPNN and uses the estimates of the year 2009 and 2010 values for the validation process. Finally it dwells to draw predictions for the period 2011-2015 and emphasizes the growing role of BPNN application based prediction models for banking sector with a case study of State Bank of India. We conclude with practical suggestions on how best to integrate models and research into policy making decisions. Along with establishing the ratios, analysis regarding the bankruptcy status of the firm is also analysed. The basic Z Score value of the firm from 2001-2008 has been used to predict the Z Score values upto 2015.

  Neural Networks, Credit lending, Credit Capacity, BPNN


1.        ALTMAN, EDWARD [1968]: "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, Vol 13, n.4, September, pp. 589-609.
2.        ALTMAN, EDWARD [1984]: "The success of business failure prediction models: an international survey", Journal of Banking and Finance, pp. 171-198.

3.        ATIYA, AMIR [2001]: ―Bankruptcy prediction for credit risk usingneural networks: a survey and new results, IEEE Transactions on Neural  Networks, Vol. 12, n.4, July, pp. 929-935.

4.        BEAVER, WILLIAM H.; MCNICHOLS, MAUREEN F.; RHIE, JUNG WU [2005]: ―Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy, Review of Accounting Studies, n.10, pp. 93-122.

5.        BHARATH SREEDHAR T., SHUMWAY TYLER, (2008) Forecasting Default with the Merton Distance to Default Model, The Review of Economic Times, Volume21, Issue3,Pp. 1339-1369.

6.        CAMPBELL, STEVEN V. [1996]: ―Predicting bankruptcy reorganization for closely held firms, Accounting Horizons, Vol. 10, n. 3, September, pp. 12-25.

7.        COLLINS, ROBERT A.; GREEN, RICHARD [1982]: ―Statistical methods for bankruptcy forecasting, Journal of Economics and Business, n. 34, pp. 349-354.

8.        DEAKIN, EDWARD [1972]: "A discriminant analysis of predictors of business failure", Journal of Accounting Research, Spring , pp. 167-179. • EDMINSTER, ROBERT O. [1972]: "An empirical test of financial ratio analysis for small business failure prediction", Journal of Financial and Quantitative Analysis, March, pp. 1477- 1493.

9.        GORDON, M. J. [1971]: "Towards a theory of financial distress", Journal of Finance, n. 26, May, pp. 347-356.

10.     HILLEGEIST, STEPHEN; KEATING, ELIZABETH; CRAM, DONALD, LUNDSTEDT, KYLE [2004]: ―Assessing the probability of bankruptcy, Review of Accounting Studies, n.9, pp. 5-34.

11.     KEASEY, KEVIN; MC.GUINESS, PAUL [1990B]: ―The failure of U.K. industrial firms for the period 1976-1984, logistic analysis and entropy measures, Journal of Business, Finance and Accounting, Vol. 17, n. 1, Spring, pp. 119-135.

12.     MENSAH, WAW H. [1983]: ―The differential bankruptcy predictive ability of specific price level adjustments: some empirical evidence, The Accounting Review, Vol. LVIII, n. 2, April, pp. 228-246.

13.     MENSAH, YAW H. [1984]: "An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study", Journal of Accounting Research, Vol. 22, n.1, Spring, pp.380-395.
14.     MEYER, PAUL A.; PIFER, HOWARD W. [1970]: "Prediction of bank failures", The Journal of Finance, Vol.25, n.4, September, pp. 853-868.
15.     MERTON, ROBERT C., "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates", Journal of Finance, Vol. 29, No. 2, (May 1974), pp. 449-470

16.     OHLSON, JAMES A. [1980]: "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, Vol 18, n.1, Spring, pp. 109-131.

17.     SCOTT, JAMES [1981]: ―The probability of bankruptcy, Journal of Banking and Finance, n. 5, pp. 317-344.

18.     SHUMWAY, TYLER [2001]: ―Forecasting bankruptcy more accurately: a simple hazard model, Journal of Business, Vol 74, n.1, pp.101-124.

19.     STEVENS, DONALD L. [1973]: ―Financial characteristics of merged firms: a multivariate analysis, Journal of Financial and Quantitative Analysis, March, pp. 149-158.

20.     WILCOX, JARROD W.[1971]: ―A simple theory of financial ratios as predictors of failure. Journal of Accounting Research, Autumn, pp. 389-395.

21.     ZHANG, GUOQIANG; HU, MICHAEL Y.; PATUWO, EDDY B.; INDRO, DANIEL C. [1999]: ―Artificial neuronal networks in bankruptcy prediction: general framework and cross validation analysis, European Journal of Operational Research, n. 116, pp. 16-32.

22.     ZMIJEWSKI, MARK E. [1984]: ―Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, Vol. 22, supplement, pp. 59-82.






Anand Tamrakar, Kamal K. Mehta

Paper Title:

Analysis of Effectiveness of Web based ELearning Through Information Technology

Abstract:   Advancements of technology help in providing effective decision-making and problem solving in many aspects of life. Amongst the available technologies, Information Technology (IT) is one of the leading technologies in effectively utilizing the scarce resources to encounter the gap between solutions provided by existing methodologies and demands of society. Effectiveness of education system depends on the degree of quality it exhibits. Traditional learning models like classroom teaching and distance learning have their own limitations. It is increasingly becoming hard to maintain the standards of education due to limitations of infrastructure, finance and other resources including skilled manpower. Learning empowered through Information Technology (IT), to some extent ensures high quality learning by providing necessary information This project , study the effectiveness of e-learning as it relates to the level of e-learning experience It is clearly observed that, e-Learning systems emphasise on quality and effective presentation of information.This paper has a hypothesis (regarding effectiveness of web based e-learning and other learning methods) using a varying statistics and statistical methods performed on data. It includes a hypothesis statement and details for the performance of the hypothesis test on the data. The paper also includes an analysis of variance (ANOVA) computation for the data and an interpretation of the results.

  ANOVA, e-learning, semantic web ,ontologies


1.        Welsh, E.T., C.R. Wanberg; K.G. Brawn and M.J. Simering, 2003. Elearning emerging uses, empirical results and future direction. International J. Training and Develop. 7(4): 245-288.
2.        Janicki, T., Steinberg, G. (2003). Evaluation of a computer-Supported Learning System, Decision Sciences the Journal of Innovative Education, 1, 2 (Sept.), 203-223.

3.        Drucker, P. (2005), “Need to Know: Integrating e-Learning with High Velocity Value Chains”, A Delphi Group White
Paper,http://www.delphigroup.com/pubs/whitepapers/20001213-e-learningwp. pdf

4.        European Journal of Open, Distance and e-Learning EURODL, Date of publication: 28.05.2009. http://www.eurodl.org/article=359 The Influence of Experience, Ability and Interest on e-learning Effectiveness

5.        Vrasidas, C., & McIsaac, M. (2000). Principles of pedagogy and evaluation of Web- based learning, Educational Media International, 37(2), 105-111.

6.        Collis, B (1998). New didactics for university instruction: why and how? Computers & Education, 31 pp. 373-393, Collis, B and Moonen, J (2001).

7.        Collis, B.. (1998) New didactics for university instruction: why and how? Computers and Education, 31, 373-393. Collis, B. & Anderson, R.E. (1994).

8.        Barker, Ph. (2000),”Designing Teaching Webs: Advantages, Problems and Pitfalls. Educational Multimedia,

9.        e-learning - A Review of Literature Barron, Tom (2000a) “The future of digital learning.” e-learning May/June 2000. Vol. 1, No.2, pp. 46-7. Barron, Tom (2000b)

10.     5 Jun 2010, Abernathy, D. J. (1998). The WWW of distance learning: Who does what and where? Training and Development 52 (9) p. 29-30.

11.     Reseach in Education ,Ninth Edition by John W.Best,James V.Kahn,second impression 2006.

12.     Testing Group Di®erences using T-tests, ANOVA, and Nonparametric Measures, Department of sychology University of Alabama348 Gordon Palmer Hall,jan 2003 .






R. HariKumar, T.Vijaya Kumar

Paper Title:

Estimation of Drowsiness and Classification of Epilepsy Risk Levels from EEG Signals Using Chaos Theory

Abstract:   Chaos in nonlinear dynamical systems has become a widely-known phenomenon and its presence has been identified in many different systems in virtually all the fields of science. In medical world, analyzing chaos in the brain and explore its dynamics is a challenging task to every individual. In this paper, an effective and a practical method for exploring such brain activities are studied. This paper relates a method to analyze an Electroencephalogram (EEG) using Correlation Dimension (CD) for drowsiness estimation in sleep onset and epilepsy. Dimension is a critical property since, it indicates how many independent state variables are required to reproduce the system dynamics in state space and this in turn indicates how many state variables should be included in a mathematical model of the system. Aside from this practical issue, the dimension is an indicator of the degree of "complexity" of a system and tracking any changes in dimension due to pathology or other manipulations to the system is a useful diagnostic criterion. For many chaotic systems, accurate calculation of the CD from measured data is difficult because of very slow convergence as the scale size is reduced. This paper proposes a method for collecting data at large scales, creating the time series and determining the possibility of constructing an attractor for establishing the deterministic character of dynamics of the underlying system.

  EEG Signals, Chaos, Sleep onset, Epilepsy, Correlation Dimension.


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3.        R.Harikumar, B.Sabarish Narayanan (2003), ―Fuzzy Techniques for Classification of Epilepsy risk level from EEG Signals, Proceedings of IEEE Tencon–2003, Bangalore, India, pp 209-213.

4.        Babloyantz A, Salazar JM, Nicolis C, ―Evidence of chaotic dynamics of brain activity during the sleep cycle, Phys Lett A 1985; 111: 152–156.

5.        R. S. Shaw, ―The Dripping Faucet as a Model Chaotic System, Aerial Press, Santa Cruz, A (1985).

6.        Takens, F. (1981), ― Dynamical Systems and Turbulence, Warwick, Lecture Notes in Mathematics, eds. Rand, D. A. & Young, L. S. (Springer, Berlin), Vol. 898, pp. 366-381.

7.        Mauricee.Cohen, Donnal. Hudson, Prakash C.Deedwania Etal (1996) ―Applying Continuous chaotic modeling to cardiac signal analysis, IEEE EMB magazine, PP 97-102.

8.        Russel , C Eberhart, ―Chaos Theory for Biomedical Engineer, IEEE EMB magazine, pp41-45.

9.        Leonidas D, Iasemidis & J. Chris Sackellares, ―Chaos Theory and Epilepsy, The Neuroscientist 2:118-126, 1996

10.     Hornero R, Alonso A, Jimeno N, Jimeno A., López M, ―Estimation of Correlation Dimension to Evaluate Cognitive Performance in Schizophrenic Patients Using a New Computer Technique, Nonlinear Dynamics, Psychology, and Life Sciences, Volume 03, Number 1, January 1999 , pp. 49-63(15).

11.     R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharthi, Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals, I.E . India Journal of Inter disciplinary panels, May 2005, 86(1):9-17.

12.     J-C Roux, RH Simoyi, HL Swinney (1983), Observation of a strange attractor. Physica D 8:157-266.

13.     M Casdagli, S Eubank, JD Farmer, J Gibson (1991), ―State space reconstruction in the presence of noise, Physica D 51:52-98.

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15.     MB Kennel, R Brown, HD Abarbanel (1992), ―Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A 45:3403-3411.

16.     P Grassberger, I Procaccia (1983) ―Measuring the strangeness of strange attractors, Physica D 9:189-208.

17.     A Babloyantz, A Destexhe (1986), ―Low-dimensional chaos in an instance of epilepsy, Proceedings of the National Academy of Sciences of the USA 83:3513-3517.





H.S. Behera

Paper Title:

Segmentation and Classification using Heuristic HRSPSO

Abstract:   In this paper, a Heuristic Hybrid Rough Set Particle Swarm optimization (HRSPSO) Algorithm is proposed for partitioning a digital image into different segments that is more meaningful and easier to analyze segmentation andClassification. The heuristic HRSPSO algorithm has been implemented with a novel method in MATLAB platform considering 50 iterations and 20 particles. The experimental study and performance evaluation show that Heuristic HRSPSO optimization Algorithm is observed to be having optimal solution with smallest DB (Davies-Bouldin) index and it converges after fifteenth iterations.

   Image Processing, Clustering, Image Segmentation, Classification, Davies-Bouldin index, Rough Set, Particle Swarm Optimization, Hybrid Rough Set Theory, Image Pixel Classification, Fuzzy C- Means (FCM).


1.        Pawlak, Z., Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht, 1991.
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3.        Eberhart, R. C., Shi, Y.: Particle swarm optimization: Developments, applns and resources, In Proceedings of IEEE International Conference on Evolutionary Computation, vol. 1,81-86, 2001.

4.        Swagatam Das1, Ajith Abraham2 and Subir Kumar Sarkar1 “A Hybrid Rough Particle Swarm Algorithm for Image Pixel Clasification”

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6.        Jain, A.K., Murty M.N., and Flynn P.J. (1999): Data Clustering: A Review, ACM Computing Surveys, Vol 31, No. 3, 264-323.

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8.        L.O. Hall, I.B. Özyurt, J.C. Bezdek, Clustering with a genetically optimized approach, IEEE Trans. Evo Comp. 3 (2)(1999) 103–112.

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Sumit Sahu, Bharti Dongre, Rajesh Vadhwani

Paper Title:

Web Spam Detection Using Different Features

Abstract:    Spamming is any deliberate action solely in order to boost a web page’s position in search engine results, incommensurate with page’s real value. Web Spam is the Web pages that are the result of spamming. Web spam is the deliberate manipulation of search engine indexes. It is one of the search engine optimization methods. Implementing web spam on a search engine reduces the redundant and non-desirable results. In our paper we discuss the features which are responsible for web page ranking. We also discuss the results of the different classification techniques on our dataset which we process from the WEBSPAM-UK2006 Dataset. We are also proposing a feature which will help in the web spam detection.

   web spam; feature selection; classification technique; N gram algorithm;


2.        Manuel Egele , Clemens Kolbitsch , Christian Platzer, ―Removing web spam links from search engine results in Springer-Verlag France 2009 J Comput Virol (2011) 7:51–62

3.        Wei Wang , Guosun Zeng , Daizhong Tang ―Using evidence based content trust model for spam detection in Expert Systems with Applications 37 (2010) 5599–5606, Science Direct.

4.        Jun-Lin Lin ―Detection of cloaked web spam by using tag-based methods in Expert Systems with Applications 36 (2009) 7493–7499, Science Direct.

5.        Luca Becchetti, Carlos Castillo, Debora Donato, Stefano Leonardi, and Ricardo Baeza Yates. Link-based characterization and detection of web spam. In Proceedings of the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2006

6.        C. Castillo, D. Donato, A. Gionis, V. Murdock, and F. Silvestri. Know your neighbors: web spam detection using the web topology. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 423–430, 2007.

7.        Alexandros Ntoulas, Marc Najork, Mark Manasse, and Dennis Fetterly. Detecting spam web pages through content analysis. In Proceedings of the 15th International World Wide Web Conference (WWW), pages 83–92, Edinburgh, Scotland, 2006.

8.        Gilad Mishne, David Carmel, and Ronny Lempel. Blocking blog spam with language model disagreement. In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), Chiba, Japan, 2005.

9.        A. A. Benczúr, I. Bíró, and K. Csalogány. Detecting nepotistic links by language model disagreement. In Proceedings of the 15th International World Wide Web Conference (WWW), 2006.

10.     Guang-Gang Geng, Chun-Heng Wang, Qiu-Dan Li, Lei Xu and Xiao-Bo Jin. Boosting the Performance of Web Spam Detection with Ensemble Under-Sampling Classification.

11.     Manuel Egele, Christopher Kruegel, Engin Kirda ―Removing Web Spam Links from Search Engine Results.

12.     Lourdes Araujo and Juan Martinez-Romo ―Web Spam Detection: New Classification Features Based on Qualified Link Analysis and Language Models in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 3, SEPTEMBER 2010.

13.     Juan Martinez-Romo, Lourdes Araujo. ―Retrieving Broken Web Links using an Approach based on Contextual Information.

14.     J. Abernethy, O. Chapelle, and C. Castillo, ―Webspam identification through content and hyperlinks, in Proc. Fourth Int. Workshop on Adversarial Information Retrieval on the Web (AIRWeb), Beijing, China, 2008, pp. 41–44.

15.     András A. Benczúr, Károly Csalogány, Tamás Sarlós, Máté Uher―SpamRank – Fully Automatic Link Spam Detection Work in progress in Proc. First Int. Workshop on Adversarial Information Retrieval on the Web (AIRWeb, Chiba, Japan, 2005, pp. 25–38

16.     Jay M. Ponte and W. Bruce Croft, ―A Language Modeling Approach to Information Retrieval in Proc. 21st Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR’98), New York, 1998, pp. 275–281, ACM.

17.     C. Castillo, D. Donato, L. Becchetti, P. Boldi, S. Leonardi, M. Santini, and S. Vigna, ―A reference collection for web spam, SIGIR Forum, vol. 40, no. 2, pp. 11–24, 2006.

18.     P. Boldi, B. Codenotti, M. Santini, and S. Vigna ―UbiCrawler: a scalable fully distributed web crawler. Software, Practice and Experience, 34(8):711–726, 2004.

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H. S. Hota, Ramesh Pratap Singh

Paper Title:

A min-max Approach for Improving the Accuracy of Effort Estimation of COCOMO

Abstract:    This study proposed to extend the Constructive Cost Model (COCOMO) by incorporating the concept of min-max approach to estimation. Formal effort estimation models like Constructive Cost Model (COCOMO) are limited by their inability to manage uncertainties and imprecision surrounding software projects early in the development life cycle. A min-max approach is suggested to rectify data uncertainties and modeling errors. The proposed method of min-max is used to improve the accuracy of effort estimation of COCOMO and its result have been compared with the gradient descent, robust fuzzy clustering, k-mean clustering methods of estimation. It has been observed that the proposed method have lowest and steady state absolute estimate error AE(k) and mean absolute estimate error MAE(k) for different value of k(time series) and different step-size s.

  Sugeno fuzzy inference system, min-max method, Constructive Cost Model (COCOMO), effort estimation, Absolute Estimate Error, Mean Absolute Estimate Error.


1.        Steve McConnell. Rapid development: taming wild software schedules. Microsoft Press, 1996.
2.        Mohit Kumar et al, A Min-Max Approach to fuzzy clustering Estimation and Identification, IEEE Transaction on fuzzy system, vol.14, No. 2, PP 248-262, 2006.

3.        B.W. Boehm, Software Engineering Economics, Englewood Cliffs, NJ, Prentice Hall, 1981.

4.        Iman Attarzadeh, Siew Hock Ow, Improving the Accuracy of Software Cost Estimation Model Based on a New Fuzzy Logic Model, World Applied Science, Vol. 8(2), pp. 177-184, 2010.

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8.        M.T. Su, T.C.Ling, K.K.Phang, C.S.Liew, P.Y.Man, Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model, Malaysian Journal of Computer Science, Vol. 20, No. 2, 2007, pp. 199-207.

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10.     X. Huang, Danny Ho, J. Ren, L.F. Capretz, “Improving the COCOMO model using a neuro-fuzzy approach”, Applied Soft Computing , Vol.7, Issue 1, 2007, pp. 29-40.

11.     A. R. Gray, S. G. MacDonell, Applications of Fuzzy Logic to Software Metric Models for Development Effort Estimation, Fuzzy Information Processing Society 1997 NAFIPS’ 97, Annual Meeting of the North American, 21 – 24 September 1997, pp. 394 – 399.

12.     P. Thrift, Fuzzy logic synthesis with genetic algorithms, in proc., 4th Int. Conf. on Genetic Algorithms, pp 509-513, 1991.

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17.     M. Setnes et al, Rule-based modeling precision and transparency, IEEE Trans. Syst. Man. Cybern. C. Apple. Rev., Vol. 28, No. 1, pp 165-169, 1998.

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20.     J. C. Bezdeck, Pattern Recognition with fuzzy objective function algorithms, New York: Plenum, 1981.

21.     L. X. Wang and J. M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. Syst., Man, Cybernetics, Vol. 22, No. 6, pp 1414-1427, 1992.

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23.     Menzies, T., 2005. Promise software engineering repository. http://promise.site.uottawa.ca/SERepository/

24.     B. Boehm, E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B. Steece, A. W. Brown, S. Chulani, and C. Abts, Software Cost Estimation with Cocomo II. Prentice Hall, 2000.






Rita Yadav, Danvir Mandal

Paper Title:

Optimization of Artificial Neural Network for Speaker Recognition using Particle Swarm Optimization

Abstract:    This paper proposes a particle swarm optimization (PSO) based optimization technique for Artificial Neural Network weights optimization for speaker recognition. PSO is a search algorithm, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The particle swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for optimization in that particle swarms will discover best optimized value as they fly within the subset space. Combining the ANN and PSO algorithms improves the performance as compared to that ANN alone.

   Artificial Neural Network (ANN), Feature Extraction, Matlab, Mel Frequency Cepstral Coefficient, Particle Swarm Optimization, Speaker Recognition.


1.        D.O. Shaughnessy, ―Speaker Recognition, ASSP Magazine, IEEE Signal Processing Magazine, Vol. 3, No. 4, Part. 1, pp. 4-17, October 1986.
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4.        H. Gish, and M.Schmidt, ―Text-indepent speaker identification, IEEE Signal Process. Mag., vol. 18, pp.18-32, Oct. 2002.

5.        Sirko Molau, Michael Pitz, Ralf Schlu¨ ter, and Hermann Ney, ―Computing Mel-Frequency Cepstral Coefficients on the Power Spectrum Lehrstuhl fu¨ r Informatik VI, Computer Science Department, RWTH Aachen – University of Technology, 52056 Aachen, Germany.

6.        T. Kohonen, ―Self-organization and Associative Memory Springer-Verlag, Berlin- New York, 1988a.

7.        B.D. Ripley, ―Neural Networks and Related Methods for Classifications Journal of Royal Statistics Society, B56,pp. 409-456,1994.

8.        (PSO Tutorial), Xiaohui Hu. Available: http://www.swarmintelligence.org/tutorials.php

9.        J. Kennedy and R. Eberhart, "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks (ICNN'95), Vol. IV, pp.1942-1948, Perth, Australia, 1995.

10.     Campell J.P. and Jr., ―Speaker recognition: a tutorial Proceeding of the IEEE, Vol 85, pp. 1437-1462, 1997.

11.     Y.-Yan, M. Fanty, and R. Cole, "Speech Recognition Using Neural Networks with Forward-backward Probability Generated Targets", Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Munich, April 1997.

12.     Shukla, Anupam, Tiwari, Ritu, ―A novel approach of speaker authentication by fusion of speech and image features using Artificial Neural Networks, Int. J. of Information and Communication Technology 2008-Vol.1,No.2 pp . 159 – 170.

13.     R. P. Lippmann, ―Review of Neural Networks for Speech Recognition, Neural Computation, Vol. 1, No. 1, pp. 1-38, 1989.

14.     Ehab F., M. F. Badran, Hany Selim “Speaker Recognition Using Artificial Neural Networks Based on Vowel phonemes Electrical Engineeling Department, Assiut University.

15.     Zahorian, S. A., ―Reusable Binary-Paired Partitioned Neural Networks for Text-Independent Speaker Identification, Proc. ICASSP-99, pp. II: 849- 852, 1999.

16.     Zebulum, R.S. Vellasco, M. Perelmuter, G. Pacheco, ‗A comparison of different spectral analysis models for speech recognition using neural networks Departamento de Engenharia Eletrica, PUC, Rio de Janeiro, Brazil; IEEE 1996.

17.     A. Abraham, H. Guo, and H. Liu, ―Swarm intelligence: Foundations, perspectives and applications, swarm intelligent systems, in Studies in Computational Intelligence. Berlin, Germany: Springer Verlag, pp. 3-25, 2006.

18.     www23.Homepage.Villanova.edu/Varadarajan…./PSO_meander-line .ppt






Tarun Chopra, Jayashri Vajpai

Paper Title:

Classification of Faults in DAMADICS Benchmark Process Control System Using Self Organizing Maps

Abstract:    This paper presents a new approach for classification of faults in a process control system with complex overlapping fault classes. It is based on the application of Self Organising Maps that possess the capability of efficient unsupervised learning. Using the SOM training process, the proposed approach derives a set of neurons by considering process monitoring dataset comprising of multiple measured attributes. This set of neurons constitutes the multilayered SOM, in which each neuron corresponds to a class of faults. The neurons with similar attribute values are spatially arranged in adjoining localities, to set up an exploratory linkage between the SOM and the fault dataset. The performance of the proposed method is found to be satisfactory for fault diagnosis in the DAMADICS Benchmark Process Control System, even for the overlapping fault classes that pose considerable difficulty to other classification approaches applied by researchers.

 Artificial Neural Networks, DAMADICS Benchmark Process Control System, Fault Diagnosis, Self Organizing Maps.


1.        T. Kohonen, “Self-Organizing Maps,” Springer-Verlag Berlin, Heideberg, 1997.
2.        Michal Bartys et al, “Introduction to the DAMADICS actuator FDI benchmark study” Control Engineering Practice 14 , pp. 577–596, 2006

3.        Bocaniala et al, “On the applicability of state-of-the-art fault diagnosis methodologies to simple and complex systems”, The annals of “DUNAREA DE JOS” University Of Galati Fascicle III, 2005 ISSN 1221-454X, Electrotehnics, Electronics, Automatic Control, Informatics

4.        Dustegor et al, “Structural analysis of fault isolability in the DAMADICS benchmark”, Control Engineering Practice 14 , pp. 597–608, 2006.

5.        Puig et al, “Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 621–633, 2006.

6.        Supavatanakul et al, “Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem”, Control Engineering Practice 14 , pp. 609–619, 2006.

7.        Previdi et al , “Model-free actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 635–644, 2006.

8.        Bocaniala et al , “Application of a novel fuzzy classifier to fault detection and isolation of the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 653–699, 2006.

9.        Uppal et al , “A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 699–717, 2006.

10.     Witczak et al , “A GMDH neural network-based approach to robust fault diagnosis: Application to the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 671–683, 2006.

11.     Calado et al , “FDI approach to the DAMADICS benchmark problem based on qualitative reasoning coupled with fuzzy neural networks”, Control Engineering Practice 14 , pp. 685–698, 2006.

12.     Almeida et al , “Fault Detection and Diagnosis in the DAMADICS Benchmark Actuator System –A Hidden Markov Model Approach” Proceedings of the 17th World Congress, The International Federation of Automatic Control,Seoul, Korea, July 6-11, 2008.

13.     Juha Vesanto,”Data Exploration Process Based on the Self−Organizing Map”, Mathematics and Computing Series No. 115, 1999.

14.     Vesanto J., Himberg J., Alhoniemi E., Parhankangas J., “Self-organizing map in Matlab: the SOM Toolbox”, In Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 35-40, November 16-17, 1999.






Siddesh.G.K, K.N.Muralidhara, Manjula.N.Harihar

Paper Title:

Routing in Ad Hoc Wireless Networks using Soft Computing techniques and performance evaluation using Hypernet simulator

Abstract:    An ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination, due to the limited range of each mobile host’s wireless transmissions. Much effort has gone into mobile ad-hoc network (MANET) research over the past decade. Yet, even today, mobile ad-hoc networking is seen as a relatively new area of research. The reason for this can be traced to the fact that the maturity in truly understanding these networks is still alarmingly low and actual deployment of these networks rare. This paper presents a protocol for routing in ad hoc networks that uses soft computing techniques like neural networks, fuzzy logic and genetic algorithm. The simulation has been performed using hyper net simulator for various existing protocols like proactive routing , reactive routing, power aware routing protocol, hybrid routing . Our protocol uses soft computing techniques protocol for establishing the link between the nodes in minimum time. The results of our experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a large extent. The simulation results are obtained using hypernet simulator and are also compared with the results obtained using NS-2.

   Routing Protocol, MANET, Neural Nets, Fuzzy Logic, Genetic Algorithm, Soft Computing, hyper net.


1.        F. Baker, "An outsider's view of MANET," Internet Engineering Task Force document ,17 March 2002.
2.        C. Barrett et al., "Characterizing the Interaction Between Routing and MAC Protocols in Ad-hoc Networks," Proc. MobiHoc 2002 , pp. 92-103

3.        J. Broch et al., "A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols," Proc. Mobicom '98.

4.        D. Cavin et al., "On the accuracy of MANET simulators," Proc. ACM Workshop on Princ. Mobile Computing (POMC'02), Oct. 2002, pp. 38-43.

5.        K.W. Chin, et al., "Implementation Experience with MANET Routing Protocols," ACM SIGCOMM Computer Communications Review, Nov. 2002, pp. 49-59.

6.        M. S. Corson et al., "Internet-Based Mobile Ad Hoc Networking," IEEE Internet Computing, July-August 1999, pp. 63-70

7.        C. Elliott and B. Heile, "Self-Organizing, Self-Healing Wireless Networks," Proc. 2000 IEEE Int'l Conf. on Personal Wireless Comm., pp. 355-362.

8.        L. M. Feeney, "A Taxonomy for Routing Protocols in Mobile Ad Hoc Networks," Swedish Institute of Computer Science Technical Report T99/07, October 1999.

9.        M. Frodigh, et al, "Wireless Ad Hoc Networking: The Art of Networking without a Network," Ericsson Review, No. 4, 2000.

10.     David F. Bantz and Fr´ed´eric J. Bauchot. Wireless LAN design alternatives. IEEE Network, 8(2):43–53, March/April 1994.

11.     Vaduvur Bharghavan, Alan Demers, Scott Shenker, and Lixia Zhang. MACAW: A media access protocol for wireless LAN’s. In Proceedings of the SIGCOMM ’94 Conference on Communications Architectures, Protocols and Applications, pages 212–225, August 1994.

12.     Robert T. Braden, editor. Requirements for Interne thosts—communication layers. Internet Request For Comments RFC 1122, October 1989.





Tarun Chopra, Jayashri Vajpai

Paper Title:

Fault Diagnosis in Benchmark Process Control System Using Stochastic Gradient Boosted Decision Trees

Abstract:    Decision trees create an easily understood structure for evaluating complex decisions. Tree Boost models often have a degree of accuracy that cannot be obtained using a large, single-tree model. Tree Boost models are adaptable, easy to interpret and often equal to or superior to any other predictive functions including neural networks. In this paper, the performance of the proposed approach based on Stochastic Gradient Boosted Decision Trees based method is demonstrated on the DAMADICS benchmark problem. An attempt has been made to improve the performance of fault diagnosis task on DAMADICS benchmark.

 Fault Diagnosis, Stochastic Gradient Boosted Decision Trees, DAMADICS


1.        Friedman, Jerome H., “Greedy Function Approximation: A Gradient Boosting Machine” Technical report, Dept. of Statistics, Stanford University. 1999.
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3.        L. Breiman, J. H. Friedman, R. A. Olshen, and C. J.Stone. “Classification and Regression Trees”. Pacific Grove: Wadsworth, 1984.

4.        M. Mehta, R. Agrawal, and J. Rissanen.,“SLIQ: A fast scalable classifier for data mining” In Proc. Of EDBT, 1996.

5.        T.-S. Lim, W.-Y. Loh, and Y.-S. Shih, “An empirical comparison of decision trees and other classification methods” TR 979, Department of Statistics, UW Madison, June 1997.

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7.        Chen,K.,LU,R.,Wong,C.K.,Sun,G.,Heck,L., and Tseng,B.L. Trada, “Tree based ranking function adaptation”. In CIKM, pp. 1143– –1152, 2008

8.        Zheng,Z.,Chen,K.,Sun,G., and Zha, H. “A regression framework for learning ranking functions using relative relevance judgments”. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval , pp. 287–294,2007.

9.        Friedman, J. H., “Stochastic gradient boosting”. Comput. Stat. Data Anal. 38,4 , pp. 367–378, 2002.

10.     Michal Bartys et al, “Introduction to the DAMADICS actuator FDI benchmark study” Control Engineering Practice 14 , pp. 577–596, 2006

11.     Jan M. Koscielnya, Micha Bartys, Pawe Rzepiejewski Jose Sa da Costa, “Actuator fault distinguishability study for the DAMADICS benchmark problem “, Control Engineering Practice 14 , pp. 645-652, 2006

12.     Phillip H. Sherrod, “DTREG Predictive Modeling Software”, 2003.






Sridevi.V, Jayanthy.T

Paper Title:

Design of Active Filters using CNTFET Opamp

Abstract:    The main objective of this paper is to describe the work on modeling, performance benchmarking for nanoscale devices and circuits using Carbon Nano Tube Field Effect Transistors (CNTFETs), with the aim of guiding nanoscale device and circuit design. First, a compact model has been developed for MOSFET like Carbon Nanotube Field Effect Transistor (CNTFET) which posses high fabrication feasibility and superior device performance. The design aspects of a high performance CNTFET based operational amplifier are presented. This CNTFET op-amp is used for the realization of various active filters and the simulation studies have been done using Hspice.

   Active Filter, Carbon Nanotube Field Effect Transistor, Hspice, MOSFET, Op amp.


1.        Arijit Raychowdhury, Saibal Mukhopadhyay and Kaushik Roy, ―Modeling of Ballistic Carbon Nanotube Field Effect Transistors for Efficient Circuit Simulation, ICCAD’03,pp.487- 490, November 11-13, 2003.
2.        H.-S. P. Wong, ―Beyond the Conventional Transistor, IBM J. Research & Development, March/May, pp. 133-168, 2002.

3.        T. Skotnicki, J. A. Hutchby, T.-J. King, H.-S. P. Wong, F. Beouff, ―The Road to the End of CMOS Scaling, IEEE Circuits and Devices Magazine, pp. 16 – 26, 2005.

4.        Peng, N., H. Li and Q. Zhang, 2009. ―Nano Scale contacts between carbon Nanotubes and metallic Pads, Acsnano, 10: 1016-25. 11.

5.        Tans, S., A. Verschaerea and C. Dekker, ―Room temperature transistor based on a SWCN, Science, 397: 49-52, 1998.

6.        Martel, R., T. Schmidt, H. Shea and T. Avouris, ―S-and M-WCNTFET Si transistors, Applied physics Letter, 73: 24 47-49, 1998.

7.        S. Heinze, J. Tersoff, R. Martel, V. Derycke, J.Appenzeller, and P.Avouris, ―Carbon nanotubes as Schottky barrier transistors, Phys. Rev.Lett., vol. 89, no. 10, pp. 106 801-1–106 801-4, 2002.

8.        Fabien Pr´egaldiny et.al.,―Design Oriented Compact Models for CNTFETs, IEEE Trans. Elec.dev., 2006.

9.        A. Bahari, G. Arasteh and S. Taghizadeh, Nanometer-Scaled Contacts in CNTFETs, World Applied Sciences Journal 7 (Supplement 1):18-21, 2009

10.     J. Deng and H.-S P. Wong, ―A Compact SPICE Model for Carbon Nanotube Field Effect Transistors Including Non-Idealities and Its Application — Part I: Model of the Intrinsic Channel Region, Submitted to IEEE Transactions on Electron Devices, 2007.

11.     J. Deng and H.-S P. Wong, ―A Compact SPICE Model for Carbon Nanotube Field Effect Transistors Including Non-Idealities and Its Application — Part II: Full Device Model and Circuit Performance






Phalgun Pandya, Mandeep Singh

Paper Title:

Morphology Based Approach To Recognize Number Plates in India

Abstract:    Automatic number plate recognition is a real-time embedded system which automatically recognizes the license number of vehicles. Such systems require the localization of number plate area in order to identify the characters present on it. This paper presents an approach based on simple but efficient morphological opening and closing operations for localization of Indian number plates. After localization the skew correction of the number plate is also done for effective segmentation of characters. The number plate skew correction greatly affects the accuracy of the character extraction. The character extraction is based on template matching approach. Our proposed algorithm has been tested on 100 samples and is found to be robust to detect vehicle license plates as well as extracting the numbers with an overall accuracy of 90%.

   Character Recognition, Template Matching, Morphology, LPR, ANPR.


1.        Prathamesh Kulkarni, Ashish Khatri, Prateek Banga, Kushal Shah “A Feature Based Approach for Localization of Indian Number Plates” IEEE International Conference on Information Technology, pp: 157-162, 2009.
2.        Velappa Ganpathy and Wen Lik Dennis LUI “A Malaysian Vehicle License Plate Localization and Recognition System” Journal of Systemics, Cybernetics and Informatics, ISSN 1690-4524, Vol. 6, No. 1, 2008.

3.        P. V. Suryanarayana, Suman K. Mitra, Asim Banerjee and Anil K. Roy “A Morphology Based Approach for car license Plate” IEEE Indicon 2005 Conference, Chennai, India, 11-13 Dec. 2005.

4.        C.Nelson Kennedy Babu, Krishnan Nallaperumal “A License Plate Localization using Morphology and Recognition” In Proceedings of India Conference, Volume 1, pp.34 – 39, 2008.

5.        Yassin M. Y. Hasan and Lina J. Karam, “Morphological text extraction from images” IEEE Trans. Image Process. 9(11), pp. 1978–83, 2000.

6.        Jui-Chen Wu · Jun-Wei Hsieh · Yung-Sheng Chen. “Morphology Based text line Extraction” Machine Vision and Applications, pp.195–207, 2008.

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Abdullah Al Masud, Hossain Md. Shamim, Amina Akhter

Paper Title:

Performance Analysis of AQM Schemes in Wired and Wireless Networks based on TCP flow

Abstract:   TCP is the main protocol that carried the traffic in a reliable way. IP based network always facing congestion because of increasing traffic. To control congestion and implement QOS in TCP we perform a comparative study between different active queue management techniques. Queue management is an important part to provide for better utilization of buffer. Our objective is to perform a comparative study of RED, DropTail, REM and PI in wired and wireless network based on the number of TCP flows and how they perform in congested link.



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M.Suneetha, S. Sameen Fatima

Paper Title:

Corpus based Automatic Text Summarization System with HMM Tagger

Abstract:   The rapid growth of the data in the Internet has overloaded the user with enormous amounts of information which is more difficult to access huge volumes of documents. Automatic text summarization technique is an important activity in the analysis of high volume text documents. Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. In this paper a frequent term based text summarization technique with HMM tagger is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The model consists of four stages. In first stage, the system decomposes the given text into its constituent sentences, assigning the POS (tag) for each word in the text and stores the result in a table. The second stage removes the stop words, stemming the text and applying lemmatization. Feature term identification is done in third stage. Finally each sentence is ranked depending on feature terms. This stage reduced the amount of the sentences in the summary in order to produce a qualitative summary.

  Text Summarization, HMM Tagger, Brown Corpus, POS tagging.

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Tony Gladvin George, N.Malmurugan

Paper Title:

UEP based on Proximity Pilot Subcarriers in OFDM

Abstract:   A novel Unequal-Error-Protection method is proposed that utilizes the subcarrier positions relative to pilot subcarriers in an OFDM multicarrier frame. With the available physical layer techniques, a prioritized encoding strategy based on the characteristics of the channel fading effects on the data subcarriers, those are in close proximity to the pilot subcarriers, for layered video is developed. The strategy is to efficiently map the bit streams of various priorities into the subcarriers with assisted information on their individual error recovery probability. The proposed technology maintains a minimum QoS for all periods outside outage since the high priority layer is guaranteed to be transmitted under BER constraints. At lower SNR scenarios this difference between the pilot proximate data subcarriers are more distinctive.

  Video Transmission, Unequal Error Protection, Proximity Pilot Subcarriers, OFDM.


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