ABSTRACT

Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network entails various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal connection weights. This paper presents the application of hybrid model that integrates Genetic Algorithm and Back Propatation network(BPN) where GA is used to initialize and optmize the connection weights of BPN. Significant feactures identified by using two methods :Decision tree and GA-CFS method are used as input to the hybrid model to diagonise diabetes mellitus. The results prove that, GA-optimized BPN approach has outperformed the BPN approach without GA optimization. In addition the hybrid GA-BPN with relevant inputs lead to further improvised categorization accuracy compared to results produced by GA-BPN alone with some redundant inputs.

Keywords

Back Propagation Network, Genetic algorithm, connection weight optimisation.

REFERENCES

[1] S. Haykin,(1994), Neural Networks- A comprehensive foundation, Macmillan Press, New York.

[2] D.E. Rinehart, G.E. Hinton, and R. J. Williams, (1986), Learning internal representations by errorpropagation, In D.E. Rumelhart and J.L. McClelland, editors, Parallel Distributed Processing, Cambridge, MA: MIIT Press.

[3] H. Lu, R. Setiono and H. Liu, (1996),”Effective data mining using neural networks”, IEEE Trans. On Knowledge and Data Engineering.

[4] A. Roy, (2000),Artificial neural networks – a science in trouble, SIGKDD Explorations.

[5] D. Goldberg, (1989)Genetic Algorithms in Search, Optimization , and Machine learning, Addison Wesley.

[6] http://www.myreaders.info/09_Genetic_Algorithms.pdf

[7] Berson Alex, Smith Stephen J. (1999) ,Data Warehousing, Data Mining, &OLAP., McGraw-Hill Book Co.

[8] Jihoon Yang, Vasant G. Honavar , (1998)”Feature Subset Selection Using a Genetic Algorithm”, Journal IEEE Intelligent Systems, Volume 13 Issue 2.

[9] Brill, F., Brown, D., & Martin, W. (1992). “Fast Genetic Selection of Features for Neural Network Classifiers”, IEEE Transactions on Neural Networks, 3(2), pp324-328.

[10] Arena P, Caponetto R, Fortuna L, Xibilia M G (1992), “Genetic algorithm to select optimal neural network topology”, Proceedings of the 35th Midwest Symposium on Circuits and Systems 2: pp1381– 1383.

[11] Maniezzo V,(1994) ,Genetic evolution of the topology and weight distribution of neural networks. IEEE Neural Network. 5: pp39–53

[12] Sexton R S, Dorsey R E, Johnson J D (1998) Toward global optimization of neural networks: A comparison of the genetic algorithm and back propagation. Decis. Support Syst. 22: pp171–185

[13] Rajasekaran, S and G. A Vijayalakshmi Pai (1996), Genetic Algorithm based Weight Determination for Backpropogation Networks, Proc of the Fourth Int Conf on Advanced Computing, pp 73-79).

[14] Osman Ahmed, Mohd Nord, Suziah Sulaiman, Wan Fatimah,(2009), ”Study of Genetic Algorithm to Fully-automate the Design and Training of Artificial Neural Network”,International Journal of Computer Science and Network Security, VOL.9 No.1.

[15] H. Paul S., G. Ben S., T. Thomas G., W. Robert S.,(2004), ” Use of genetic algorithms for neural networks to predict community-acquired pneumonia”, Artificial Intelligence in Medicine, Vol. 30, Issue 1, pp.71-84.

[16] D.Shanti, G. Sahoo , N. Saravanan, (2009), “ Evolving Connection Weights of ANN using GA with application to the Prediction of Stroke Disease”, International Journal of Soft Computing 4(2):pp95- 102, Medwell Publishing.

[17] R.V. Murali, Member, IAENG, A.B.Puri, and G.Prabhakaran ,(2010), “GA-Driven ANN Model for Worker Assignment into Virtual Manufacturing Cells”,Proceedings of the World Congress on Engineering 2010 Vol III, London, U.K.

[18] H. Salehi, S. Zeinali Heris*, M. Koolivand Salooki and S. H. Noei,(2011),” Designing a NN for closed Themosyphon with Nanofluid using a GA”, Brazilian Journal of Chemical Engineering ,Vol. 28, No. 01, pp. 157 – 168.

[19] Jennifer G. Dy, (2004),Feature Selection for Unsupervised Learning, Journal of Machine Learning, pp845-889.

[20] M.A.Jayaram, Asha Gowda Karegowda,(2007),” Integrating Decision Tree and ANN for Categorization of Diabetics Data”, International Conference on Computer Aided Engineering, IIT Madras, Chennai, India.

[21] Asha Gowda Karegowda and M.A.Jayaram, (2009),”Cascading GA & CFS for feature subset selection in Medial data mining”, IEEE International Advance Computing Conference, Patiyala, India

[22] Asha Gowda Karegowda, A. S. Manjunath & M.A.Jayaram,(2010), Comparative study of attribute selection using Gain ratio and correlation based feature selection, International Journal of Information Technology and Knowledge Management, Volume 2, No. 2, pp. 271-277.

[23] Editorial, ( 2004),Diagnosis and Classification of Diabetes Mellitus, American Diabetes Association, Diabetes Care, vol 27, Supplement 1.

ABSTRACT

A drawback of the error-back propagation algorithm for a multilayer feed forward neural network is over learning or over fitting. We have discussed this problem, and obtained necessary and sufficient Experiment and conditions for over-learning problem to arise. Using those conditions and the concept of a reproducing, this paper proposes methods for choosing training set which is used to prevent over-learning. For a classifier, besides classification capability, its size is another fundamental aspect. In pursuit of high performance, many classifiers do not take into consideration their sizes and contain numerous both essential and insignificant rules. This, however, may bring adverse situation to classifier, for its efficiency will been put down greatly by redundant rules. Hence, it is necessary to eliminate those unwanted rules. We have discussed various experiments with and without over learning or over fitting problem.

Keywords

Neural Network, learning, Hidden Neurons, Hidden Layers

REFERENCES

[1] Carlos Gershenson , “Artificial Neural Networks for Beginners”

[2] Vincent Cheung ,Kevin Cannons, “An Introduction to Neural Networks”, Signal & Data Compression Laboratory, Electrical & Computer Engineering University of Manitoba, Winnipeg, Manitoba, Canada

[3] “Artificial Neural Networks” ocw.mit.edu

[4] Guoqiang Peter Zhang , “Neural Networks for Classification: A Survey”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 30, NO. 4, NOVEMBER 2000

[5] V.P. Plagianakos, G.D. Magoulas, M.N. Vrahatis, “Learning rate adaptation in stochastic gradient descent”, ,Department of Mathematics, University of Patras,

[6] Wen Jin-Wei Zhao, Jia-Li Luo Si-Wei and Han Zhen “ The Improvements of BP Neural Network Learning Algorithm”, Department of Computer Science & Technology,Northem Jiaotong University ,BeiJing, 100044, P.R.China,

[7] Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu, “Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms”, IEEE TRANSACTIONS ON NEURAL NETWORKS 2002

[8] “A Detailed Comparison of Backpropagation Neural Network and Maximum-Likelihood Classifiers for Urban Land Use Classification”,IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 33, NO. 4, JULY 199.5

[9] Z. J. Liu C. Y. Wang Z. Niu A. X. Liu ”Evolving Multi-spectral Neural Network Classifier Using a Genetic Algorithm”. Laboratory of Remote Sensing Information Sciences, the Institute of Remote Sensing Applications,

[10]Fiszelew, A., Britos, P., Ochoa, A., Merlino, H., Fernández, E., García-Martínez “Finding Optimal Neural Network Architecture Using Genetic Algorithms”, R.Software & Knowledge Engineering Center. Buenos Aires Institute of Technology.Intelligent Systems Laboratory. School of Engineering. University of Buenos Aires.

[11]M.P.Craven, “A FASTER LEARNING NEURAL NETWORK CLASSIFIER USING SELECTIVE BACKPROPAGATION” Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems

[12]Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu, “Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms”, IEEE TRANSACTIONS ON NEURAL NETWORKS 2002

[13]Teresa B. Ludermir, Akio Yamazaki, and Cleber Zanchettin, “An Optimization Methodology for Neural Network Weights and Architectures” IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 6, NOVEMBER 2006

[14]S. Rajasekaran, G.A Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications” International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011 51

[15]Mrutyunjaya Panda and Manas Ranjan Patra, “NETWORK INTRUSION DETECTION USING NAÏVE BAYES” IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.12, December 2007

[16]S. SELVAKANI1 and R.S.RAJESH2, “Escalate Intrusion Detection using GA – NN”, Int. J. Open Problems Compt. Math., Vol. 2, No. 2, June 2009

[17]Nathalie Villa*(1,2) and Fabrice Rossi(3), Recent advances in the use of SVM for functional data classification, First International Workshop on Functional and Operatorial Statistics. Toulouse, June 19-21, 2008

[18] KDD Cup’99 Data set , http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

ABSTRACT

Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, ptimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.

Keywords

IRIS dataset, artificial neural networks, Back-propagation algorithm

REFERENCES

[1] Aqel, M.M., Jena, R.K., Mahanti, P.K. and Srivastava (2009) ‘Soft Computing Methodologies in Bioinformatics’, European Journal of Scientific Research, vol.26, no 2, pp.189-203.

[2] Avcı Mutlu, Tülay Yıldırım(2003) ‘Microcontroller based neural network realization and IRIS plant classifier application’, International XII. Turkish Symposium on Artificial Intelligence and Neural Network

[3] Cho, Sung-Bae.and Dehuri, Satchidananda (2009) ‘A comprehensive survey on functional link neural network and an adaptive PSO–BP learning for CFLNN, Neural Comput & Applic’ DOI 10.1007/s00521-009-0288-5.

[4] Fisher, A. W., Fujimoto, R. J. and Smithson, R. C.A. (1991) ‘A Programmable Analog Neural Network Processor’, IEEE Transactions on Neural Networks, Vol. 2, No. 2, pp. 222-229.

[5] Fu, L.(1991) ‘ Rule learning by searching on adapted nets. In Proceedings of National Conference on Artificial Intelligence’ Anaheim, CA, USA, pp. 590-595.

[6] Han, J. and Kamber, M. (2000)‘ Data Mining: Concepts and Techniques’ , 2nd ed. Morgan Kaufmann.

[7] Dr.Hapudeniya, Muditha M. MBBS(2010),‘Artificial Neural Networks in Bioinformatics’ Medical Officer and Postgraduate Trainee in Biomedical Informatics , Postgraduate Institute of Medicine, University of Colombo ,Sri Lanka, vol.1, no 2,pp.104-111. International Journal on Soft Computing ( IJSC ) Vol.3, No.1, February 2012 89

[8] Kavitha Kannan ‘Data Mining Report on IRIS and Australian Credit Card Dataset’, School of Computer Science and Information Technology, University Putra Malaysia, Serdang, Selangor, Malaysia.

[9] Marček D., ‘Forecasting of economic quantities using fuzzy autoregressive models and fuzzy neural networks’, Vol.1, pp.147-155.1

[10] Pai, G. V and Rajasekaran, S, (2006), ‘Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Applications’, 6th ed, Prentice Hall of India Pvt. Ltd.

[11] Rath, Santanu and Vipsita, Swati (2010) ‘An Evolutionary Approach for Protein Classification Using Feature Extraction by Artificial Neural Network’, Int’l Conf. on Computer & Communication TechnologyıICCCT’10.

[12] Towell, G.G. and Shavlik, J.W. (1993), ‘Extracting refined rules from knowledge-based neural networks’ Mach. Learn, Vol.13, pp.71-101.

[13] Towell, G.G; Shavlik, J.W. (1994) ‘Knowledge-based artificial neural networks’, Artif. Intell. Vol.7, pp.119-165.

ABSTRACT

Alzheimer Disease (AD) is a form of dementia; it is a progressive, degenerative disease. Alzheimer is a brain disease that causes problems with memory, thinking and behavior. It is severe enough to interfere with daily activities. Alzheimer symptoms are characterized by memory loss that affects day-to-day function, difficulty performing familiar tasks, problems with language, disorientation of time and place, poor or decreased judgment, problems with abstract thinking, misplacing things, changes in mood and behavior, changes in personality and loss of initiative. Neuro-Fuzzy Logic explores approximation techniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditional procedure for the medical diagnosis of Alzheimer employed by physician is analyzed using neuro-fuzzy inference procedure. The proposed system is a useful decision support approach for the diagnosis of Alzheimer.

Keywords

Neural Network, Fuzzy logic, Neuro Fuzzy System & Alzeimer

REFERENCES

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[2]. Akinyokun O.C. (2002), “Neuro-fuzzy expert system for evaluation of human Resource performance”, First Bank of Nigeria Endowment Fund lecture Federal University of technology, Akure, Nigeria.

[3].Aleksander I. and Morton H. (1998), “An introduction to neural computing”2nd Edition Computer Science press.

[4]. Andreas N. (2001), “Neuro-Fuzzy system”, retrieved from http//:Neuro-Fuzzy System.html.

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[6].Beth R. (2002), “Alzheimer’s Disease: A Brief History and Avenues for Current Research”, retrieved from http ://www.jyi.org/volumes/volume6/issue2/features/reger.html -Cached – Similar

[7]. Bishop C.M. (1995), “Neural Networks for pattern Recognition”, Oxford University Press, United Kingdom.

[8].Christos S. and Dimitros S. (2008), “Neural Network” retrieved from http//:docs.toc.com/doc/1505/neural-networks.

[9].CWS.(2000),“Tuberculosis Research Paper”, retrieved from http://writing4students.blogspot.com/2009/12/tuberculosis-research-paper.html.

[10]. Edward C.H. (2010), “Article: The gorilla Connection” retrieved from http//:Nature.com/nature/journal/v467/n7314/full/467404a.html.

[11]. Eklund D. and Fuller R. (1993), “A Neural-Fuzzy Approach to medical Diagnostic”Gedemedic project, Abo Academy University, Development Centres heisnki, pp.210-225.

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[17]. Nauck K. (1996), “Fuzzy Neural Network”, http//:Wikipedia.org.

[18]. Neil and Janet C. (2005), “History of Tuberculosis”, retrieved from http:// www.micklebring.com/Oakwood/ch18.html

[19]. Pao Y.H. (1989), “Adaptive Pattern Recognition and Neural Network”, Addison Wesley. International Journal on Soft Computing ( IJSC ), Vol.2, No.2, May 2011 38

[20]. Peter V. R.; Deborah B.; Berry W.R.; Teresa R.; Lon S.S.Pierre N.T., David M.B. (2011),
“Practice guideline for the treatment of patient with Alzheimer’s and other Dementia”retrieved from http://www.psychiatryonline.com/pracGuide/PracticePDFs/AlzPG101007.pdf

[21]. Ponniyin S.K (2009), “Neural Network”, Icann2007.org/neural.networks.

[22]. Otuorimuo O. (2006), “Prototype of Fuzzy System for the Formulation and Classification of Poultry Feed”, Bachelor of Science (Computer Science) Project, University of Benin, Benin City, Nigeria.

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ABSTRACT

Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author proposes a intelligent network used directional feature and energy density both as inputs to the same network and classifies the signature. Neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of offline signature verification system and this proposed new network is found very effective as compared to the above two methods, specially for less number of training samples, which can be implemented practically.

Keywords

Neural Network, Directional Feature, Energy Density, Neuron, Back propagation, AR, FRR

REFERENCES

1. Bai-ling Zhang, “Off-Line Signature Recognition and Verification by Kernel Principal Component SelfRegression,” Fifth International Conference on Machine Learning and Applications(ICMLA’06) 10.1109/ICMLA.2006.37, p. 28 to 33.

2. Deepthi Uppalapati, “Integration of Offline and Online Signature Verification systems,” Department of Computer Science and Engineering, I.I.T., Kanpur, July 2007.

3. Rejean Plamondon and Sargur N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE transections on pattern analysis and machine intelligence, Vol. 12 No. 1, January 2000, pp. 63 – 84.

4. The IEEE website. [Online]. Available: http://www.ieee.org

5. Department of Computer Science, IIT, Kanpur for precious informations. Available: http://www.cse.iitk.ac.in

6. H. N. Prakash and D. S. Guru, “Off-line signature verification: an approach based on score level fusion,” International journal of computer application, Vol. 1-No.18, 2010.

7. Samaneh Ghandali and Mohsen Ebrahimi Moghaddan, “Off-line Persian Signature identification and verification based on image registration and fusion,” Journal of Multimedia, Vol.-4 No.3, june 2009. International Journal on Soft Computing ( IJSC ), Vol.2, No.1, February 2011 57

8. Imran Siddiqi and Nicole vincent, “A set of chain-code based features for writer recognition,”10th International Conference on document analysis and recognition, 2009.

9. Stephane Armand, Michael Blumenstein and Vallipuram Muthukkumarasamy, “Off-line signature verification using enhanced modified direction feature and neural based classification,”

10. Andrew T. Wilson, “Off-line handwriting recognition using neural network,”

11. Miguel A. Ferrer, Gesus B. Alonso and Carlos M. Travieso, “Off-line Geometric parameters for automatic signature verification using fixed point arithematic,” IEEE transections on pattern analysis and machine intelligence, Vol. 27 No.6, June 2005, pp.993-997.

12. Reena Bajaj and Shantanu Choudhari, “Signature Verification using multiple neural classifiers,” Pattern recognition, Vol. 30 No. 1, 1997, pp. 1-7.

13. Emre Ozgunduz, Tulin Sentruk & M. Elif karsligil, “Off-line signature verification and recognition by support vector machine”.

14. Thomas M. Breuel, “Representations and metrics for off-line handwriting segmentation.

15. M. Blumenstein and B. Verma, “An artificial neural network based segmentation algorithm for off-line handwriting recognition”.

16. Jamal Fathi and Abuhasna, “Signature recognition using conjugate gradient neural network”.

17. Minal Tomar & Pratibha Singh, “A Simpler Energy Density method for Off-line. Signature Verification using Neural Network”.

18. Minal Tomar & Pratibha Singh, “An Intelligent network for offline signature verification system using chain code” published in proceeding of The First International Conference on Computer Science and Information Technology.

ABSTRACT

In this paper an algorithm for one-way hash function construction based on a two layer feed forward neural network along with the piece-wise linear (pwl) chaotic map is proposed. Based on chaotic neural networks, a Hash function is constructed, which makes use of neural networks’ diffusion property and chaos’ confusion property. This function encodes the plaintext of arbitrary length into the hash value of fixed length (typically, 128-bit, 256-bit or 512-bit). Theoretical analysis and experimental results show that this hash function is one-way, with high key sensitivity and plaintext sensitivity, and secure against birthday attacks or meet-in-the-middle attacks. These properties make it a suitable choice for data signature or authentication.

Keywords

One-way Hash function, Neural network, Chaotic map, Plaintext Sensitivity

REFERENCES

[1] Shiguo Lian, Zhongxuan Liu, Zhen Ren, Haila Wang, “Hash Function Based on Chaotic Neural Networks” IEEE, 2006.

[2] Shiguo Lian,Jinsheng Sun,Zhiquan Wang, “ One-way Hash Function Based on Neural Network” Journal of Information Assurance and Security,2006

[3] Yi Du, Detang Lu, Daolun Li,“An Effective Hash-based Method for Generating Synthetic Well Log” 2008 IEEE.

[4] Qun-ting Yang,Tie-gang Gao,Li Fan,Qiao-lun Gu, “ Analysis of One-way Alterable Length Hash Function Based on Cell Neural Network” Fifth Intenational Conference on Information Assurance and Security,2009

[5] Qinghua Zhang,Han Zhang and Zhaohui Li,”One-way Hash Function Construction Based on Conservative Chaotic Systems” Journal of Information Assurance and Security, 5, pp.171-178, 2010

[6] M.K. Rachel, K. Einat, K. Ido, Wolfgang, “Public Channel 012. Cryptography by Synchronization of Neural Networks and ChaoticMaps,” Physical Review Letters, Vol. 91, No.11, Sep 12, 2003: 118701/1-118701/4. International Journal on Soft Computing ( IJSC ), Vol.1, No.1, November 2010 8

[7] L.P. Yee, D. L.C. Silva, “Application of Multilayer Perceptron Network as a One-way Hash Function” International Joint Conference on Neural Networks, Vol. 2,2002.

[8] Li, C., S. Li, D. Zhang and G. Chen,”Cryptanalysis of a chaotic neural network based multimedia encryption scheme”. Advances in Multimedia Information Processing PCM ,2004 .

[9] Khalil Shihab, “A Backpropagation Neural Network for Computer Network Security” Journal of Computer Science 2 (9): 710-715, 2006.

[10] C.-K. Chan and L.M. Cheng. The convergence properties of a clipped Hopfield network and its application in the design of key stream generator, IEEE Transactions on Neural Networks, Vol.12, No. 2,pp. 340-348, March 2001.

[11] D.A. Karras and V. Zorkadis. On neural network techniques in the secure management of communication systems through improving and quality assessing pseudorandom stream generators.Neural Networks, Vol. 16, No. 5-6, June – July, 2003: 899-905

[12] S.G. Lian, G.R. Chen, A. Cheung, Z.Q. Wang. A Chaotic-Neural-Network-Based Encryption Algorithm for JPEG2000 Encoded Images. In: Processing of 2004 IEEE Symposium on Neural Networks (ISNN2004), Dalian, China, Springer LNCS, 3174 (2004) 627-632.

[13] Liew Pol Yee and L.C. De Silva. Application of multilayer perception networks in symmetric block ciphers. Proceedings of the 2002 International Joint Conference on Neural Networks, Honolulu, HI, USA, Vol. 2, 12-17 May 2002: 1455 – 1458.

[14] N. Masuda, K. Aihara, “Cryptosystems With Discretized Chaotic Maps,” IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 49, No. 1, Jan 2002.

[15] H.P. Lu, S.H. Wang, G. Hu, “Pseudo-random number generator based on coupled map lattices,”International Journal of Modern Physics B 2004; 18(17-19): 2409-2414.

[16] William Stallings. Cryptography and Network Security, Third Edition.

[17] NIST.Secure Hash Standard. Federal Information Processing Standard. FIPS-180-1.April 1995

[18] S Sivanandam .S Sumathi Introduction to Neural Networks Using MATLAB 6. 0 (Computer Science Series)

[19] Rudra Pratap Getting Started With MATLAB 7 – A Quick Introduction For Scientists And Engineers

[20] www.mathwork.com

ABSTRACT

Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations along with the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN) has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.

Keywords

Artificial Neural Networks, Multilayer Perceptron, Back Propagation, Stock market prediction & Prediction accuracy.

REFERENCES

[1] M. Majumder and A. Hussian, MD, “Forcasting of Indian Stock Market Index Using Artificial Neural Network,” nseindia.com, pp. 1-21, 2010.

[2] D. V. Setty, T. M. Rangaswamy, and K. N. Subramanya, “A review on Data Mining Applications to the Performance of Stock Marketing,” International Journal of Computer Applications, vol. 1, no. 3, pp. 33-43, Feb. 2010.

[3] P. Falinouss, “Stock trend prediction using news articles,” Master’s thesis, Lulea University of Technology, pp. 1653–0187, 2007.

[4] D. Zhang and L. Zhou, “Discovering golden nuggets: data mining in financial application,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 34, no. 4, pp. 513–522, 2004.

[5] J. Kutsurelis, “Forecasting financial markets using neural networks: An analysis of methods and accuracy,” 1998.

[6] R. Dase and D. Pawar, “Application of Artificial Neural Network for stock market predictions: A review of literature,” International Journal of Machine Intelligence, ISSN, vol. 2, no. 2, pp. 0975– 2927, 2010.

[7] E. W. Saad, D. V. Prokhorov, and D. C. Wunsch, “Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks,” Neural Networks, IEEE Transactions on, vol. 9, no. 6, pp. 1456–1470, 1998.

[8] T. Quah, “DJIA stock selection assisted by neural network,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 50-58, Jul. 2008.

[9] P. R. Charkha, “Stock Price Prediction and Trend Prediction Using Neural Networks,” 2008 First International Conference on Emerging Trends in Engineering and Technology, pp. 592-594, 2008.

[10] M. Naeini, H. Taremian, and H. Hashemi, “Stock market value prediction using neural networks,” 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 132-136, Oct. 2010.

[11] J. H. Wang and J. Y. Leu, “Stock market trend prediction using ARIMA-based neural networks,” in Neural Networks, 1996., IEEE International Conference on, 1996, vol. 4, pp. 2160–2165.

[12] M. Thenmozhi, “Forcasting Stock Index Returns Using Neural Networks,” dbr.shtr.org, vol. 7, no. 2, pp. 59-69, 2006.

[13] A. Al-Luhaib, K. Al-Ghoneim, and Y. Al-Ohali, “Dynamic Targets for Stock Market Prediction,” in Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on, 2007, no. November, pp. 1019–1022.

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ABSTRACT

Solution of inverse kinematic equations is complex problem, the complexity comes from the nonlinearity of joint space and Cartesian space mapping and having multiple solution. In this work, four adaptive neurofuzzy networks ANFIS are implemented to solve the inverse kinematics of 4-DOF SCARA manipulator. The implementation of ANFIS is easy, and the simulation of it shows that it is very fast and give acceptable error.

Keywords

Inverse Kinematics, Adaptive Neuro-Fuzzy, SCARA Manipulator

REFERENCES

[1] Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, 2006, “ Robot Modeling and Control “ John Wiley & Sons, New York.

[2] E. Sariyildiz, and H. Temeltas, 14-17 July, 2009, “ Solution of Inverse Kinematic Problem for Serial Robot Using Dual Quaterninons and Plucker Coordinates “ IEEE/ASME, Singapore, pp.338-343.

[3] P. Kalra, P.B. Mahapatra, and D.K. Aggarwal, 2003, “ On the Solution of Multimodel Robot Inverse Kinematic Functions using Real-coded Genetic Algorithms “ IEEE, pp. 1840-1845.

[4] YangshengXu and Michael C. Nechgba, 1993,“ Fuzzy Inverse Kinematic Mapping “ IROS Conference.

[5] RamakrishnanMukundan, , 2008, “ A Fast Inverse Kinematics Solution for an n-link Joint Chain” ICITA, pp. 349-354.

[6] Victor H. and etc., October 2010,“ Kinematics for the SCARA and the Cylindrical Manipulators “ICIC, Vol. 4, No. 5, pp. 1-6.

[7] Samuel R. Buss, October, 2009, “ Introduction to inverse Kinematics with Jacobian Transpose, Pseudoinverse and Damped Least Squares methods “ University of California press, pp. 1-19.

[8] Takehiko O. and H. Canada, 2010, “Solution for Ill-Posed Inverse Kinematics of Robot
ArmbyNetwork Inversion “ Hindawi Publishing Corporation, Journal of Robotics, Volume 2010, doi:10.1155/2010/870923.

[9] Panchanand J., 2009, “Novel Artificial Neural Network Application for Prediction of Inverse Kinematics of Manipulator” M.Sc. Thesis, National Institute of Technology, India.

[10] Dr. Bob John “Adaptive Network Based Fuzzy Inference Systems (ANFIS)
“.www.cse.dmu.ac.uk/~hseker/ANFISnotes.doc

ABSTRACT

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to calculate instantaneous frequencies. The 2nd,3rd and 4th IMF’s are used to extract features of epileptic signal. A neural network using back propagation algorithm is implemented for classification of epilepsy. An overall accuracy of 99.8% is achieved in classification.

Keywords

Artificial Neural Networks, Multilayer Perceptron, Back Propagation, Stock market prediction & Prediction accuracy.

REFERENCES

[1] J. Gotman., “Automatic recognition of epileptic seizures in the EEG,” Clinical Neurophysiology, vol. 54, pp. 530–540, 1982

[2] J.Gotman., “Automatic seizure detection: improvements and evaluation,” Clinical Neurophysiology, vol. 76, pp. 317–324, 1990

[3] N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih,Q. Zheng, N.C. Yen, C.C. Tung, and H.H. Liu, “TheEmpirical Mode Decomposition and Hilbert Spectrumfor Nonlinear and Nonstationary Time Series Analysis,” Proc. Roy. Soc., vol. 454, pp. 903 – 995, 1998.

[4] Y.U. Khan, J. Gotman, “Electroencephalogram Wavelet based automatic seizure detection iintracerebral”, Clinical Neurophysiology, vol. 114, pp. 899-908, 2003

[5] Güler NF, Übeyli ED, Güler.”Recurrent neural networksemploying Lyapunov exponents for EEG signal classification”,Expert Syst Appl. 2005; 29(3):506-14

[6] Varun Bajaj, Ram Bilas Pachori “Epileptic Seizure Detection Based on the Instantaneous Area of Analytic Intrinsic Mode Functions of EEG Signals,” Biomed Eng Lett, vol. 3, pp. 17-21, 2013

[7] EEG time timeseries (epilepticdata)(2005,Nov.) [Online],
http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html

[8] Hedi Khammari , Ashraf Anwar, “A Spectral Based Forecasting Tool of Epileptic Seizures ” IJCSI International.Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, May 2012

[9] Rami J Oweis and Enas W Abdulhay., “Seizure classification in EEG signals utilizing Hilbert- Huang transform” BioMedical Engineering OnLine 2011, 10:38

[10] lajos losonczi, lászló bakó, sándor-tihamér ,Brassai and lászló-ferenc Márton., “Hilbert-huang transform used for eeg signal analysis ,” The 6th edition of the Interdisciplinarity in Engineering International Conference , “Petru Maior” University of Tîrgu Mure, Romania, 2012

ABSTRACT

The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.

Keywords

Biometrics, Face, Supervised Self Organizing Maps (SOM).

REFERENCES

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[9] de Vel O, Aeberhard S (1999) “Line-based face recognition under varying pose“. IEEE T Pattern Anal 21(10):1081–1088

[10] Alvarado GJ, Pedrycz W, Reformat M, Kwak KC (2006) ” Deterioration of visual information in face classification using eigenfaces and fisherfaces”. Int J Mach Vis Appl 17(1):68–82

[11] Karande KJ, Talbar SN (2009) “Independent component analysis of edge information for face recognition“. Int J Image Proc 3(3): 120–130

[12] Liu C, Wechsler H (1999) ” Comparative assessment of independent component analysis (ICA) for face recognition “. In: Proceedings of the 2nd international conference on audio- and video-based biometric person authentication, pp 211–216

[13] Li J, Zhao B, Zhang H (2009) “Face recognition based on PCA and LDA combination feature extraction”. In: Proceedings of the 1st IEEE international conference on information science and engineering, pp 1240–1243

[14] Zhang H, Deng W, Guo J, Yang J (2010) “Locality preserving and global discriminant projection with prior information“. Int J Mach Vis Appl 21:577–585

[15] Jarillo G, Pedrycz W, Reformat M (2008) “Aggregation of classifiers based on image transformations in biometric face recognition”. Int J Mach Vis Appl 19:125–140

[16] Lu J, Plataniotis KN, Venetsanopoulos AN (2003)” Face recognition using kernel direct discriminant analysis algorithms“. IEEE Trans Neural Netw 14(1):117–126

[17] Bach FR, Jordan MI (2002) “Kernel independent component analysis“. Int J Mach Learn Res 3:41–48.

[18] Shan S, Gao W, Zhao D (2003)” Face recognition based on facespecific subspace“. Int J Imag Syst Technol 13:23–32

[19] Delac K, Grgic M, Grgic S (2005) “Independent comparative study of PCA, ICA, and LDA on the FERET data set“. Int J Imag Syst Technol 15:252–260

[20] Karande KJ, Talbar SN (2009)” Independent component analysis of edge information for face recognition“. Int J Image Proc 3(3): 120–130

[21] Jiang X, Mandal B, Kot A (2009) “Complete Discriminant evaluation and feature extraction in kernel space for face recognition“. Int J Mach Vis Appl 20:35–46

[22] Alvarado GJ, Pedrycz W, Reformat M, Kwak K (2006)” Deterioration of visual information in face classification using eigenfaces and fisherfaces”. Int J Mach Vis Appl 17(1):68–82 39

[23] Oravec M, Pavlovicˇova´ J (2007) “Face Recognition methods based on feedforward neural networks,principal component analysis and self-organizing map“. Radio Eng 16(1):51–57

[24] http://sourceforge.net/projects/opencvlibrary/

[25] Viola, P., Jones, M.:” Rapid object detection using boosted cascade of simple features“. In: Proceedings of IEEE Computer Vision and Pattern Recognition (2001)

[26] Lienhart, R., Maydt, J.:” An extended set of Haar-like features for rapid object detection“. In: Proceedings of IEEE International Conference on ISOMEe Processing, pp. 900–903 (2002)

[27] Face Database Source Link, http://www4.comp.polyu.edu.hk/ csajaykr/

[28] Fischer, M. M. (2001). “Computational neural networks: Tools for spatial data analysisComputational neural networks: Tools for spatial data analysis“. In M. M. Fischer & Y. Leung (Eds.), Geocomputational modelling: Techniques and applications (pp. 79–102). Heidelberg: Springer.