Volume-03 , Special Issue-01 , Feb - 2015 Go Back

Risks and returns are inevitably interlinked in today's work-a-day real world financial transactions. In particular, a financial portfolio illustrates the situation in which a combination of financial instruments/assets describes this interrelation in terms of their correlation in a particular market condition. The field of portfolio management has assumed importance of late, thanks to the need for decision making in investment opportunities in a high-risk scenario. It addresses the risk-reward tradeoff allocation of investments to a number of different assets so as to maximize returns or minimize risks in a given investment period. In this paper, a particle swarm optimization procedure is used to evolve optimized portfolio asset allocations in a volatile market condition. The proposed approach is centered around optimizing the Value-at-Risk (VaR) measure in different market conditions based on several objectives and constraints. Applications of the proposed approach are demonstrated on a collection of several financial instruments.

Portfolio Management; Financial Instruments; Value-at-Risk; Particle Swarm Optimization

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Jhuma Ray and Siddhartha Bhattacharyya, "Value-at-Risk Based Portfolio Allocation Using Particle Swarm Optimization", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.1-9, 2015.

Intelligent automated decision support systems are now found to be very much useful in various fields. In bioinformatics and machine learning in general, there is a large variation in the predictive measures that are used to evaluate intelligent systems. If we do not assess the accuracy of model's prediction, a vital step in model development, its application will have little merit. This work critically discusses different approaches to assess predictive performance and various test statistics. Choice of assessing strategy or validation for a specific application helps in determining the suitability of the model and in comparing the performances of different modeling techniques. The purpose of this paper is to serve as an introduction to various important benchmarking parameters and as a guide for using them in research.

Predictive performance, Confusion Matrix, Receiver operating characteristic (ROC), Akaike information criteria (AIC), Kappa statistic, Lift, Cumulative gain, Probability Threshold

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Rajesh Misir and Malay Mitra and R. K. Samanta, "A Study on Benchmarking Parameters for Intelligent Systems", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.10-17, 2015.

In order to approve bank loans, modern day researchers and bankers are involved in different types of work related to analysis of the behaviour of the loan applicants. Customer data are collected to analyze their behaviour which may predict the possibility of repayment of the EMIs. In this paper, a new approach has been made to make this process fast. The data used to analyze customer behaviour are actually behavioural patterns. Artificial Neural Networks (ANNs) are very good tool to train a system for known patterns and which can later be used to identify unknown patterns. Two dimensional binary pattern matrixes are formed considering different behaviour of customers from different views. Matrixes are further segmented column-wise and each column is presented to Perceptron (ANN) in order to train the ANN for the known patterns. Later on, unknown patterns of customer behaviour can be presented to the net to reach to the decision to provide loan to a customer or not.

Loan, Customer, ANN, Column-wise Segmentation, Perceptron, Behavioural Pattern

[1] Rakesh Kumar Mandal and N R Manna, "Hand Written English Character Recognition using Column-wise Segmentation of Image Matrix (CSIM)", WSEAS Transactions on Computers, Volume 11, Issue 5, May 2012.
[2] G.N. Swamy, G. Vijay Kumar, "Neural Networks", Scitech, India, 2007.
[3] L. Fausett, "Fundamentals of Neural Networks, Architectures, Algorithms and Applications", Pearson Education, India, 2009.
[4] Apash Roy and N R Manna,"Character Recognition using Competitive Neural Network with Multi-scale training", UGC Sponsor National Symposium on Emerging Trends In Computer Science (ETCS 2012) on 20-21 January 2012, pp 17-20.
[5] Apash Roy and N R Manna, "Competitive Neural Network as applied for Character Recognition " - International Journal of advanced research in Computer science and Software Engineering, Volume 2, Issue 3, 2012, pp 06-10.
[6] V Moonasar, Credit Risk Analysis using Artificial Intelligence: Evidence from a Leading South African Banking Institution. Available: www.academia.edu/502093/credit_risk_analysis_using_artificial_intelligence_evidence_from_a_leading_South_African_banking_institution.
[7] Ifeyinwa Ajah, Chibueze Inyiama, Loan Fraud Detection And IT-Based Combat Strategies, Journal of Internet Banking and Commerce, 2011, Vol. 16 No. 2, pp 1-13. Avialable at: www.arraydev.com/commerce/JIBC/2011_08/Ajah.pdf

Rakesh Kumar Mandal and N R Manna, "Loan Customer Analysis System using Column-wise Segmentation of Behavioural Matrix (CSBM)", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.18-22, 2015.

Yeast is among the various important components for the formulation of medicine and various chemical products, so yeast data classification is an important bioinformatics task. Yeast data classification has been approached by various machine learning techniques for last few years. In this paper, an artificial neural network system with back propagation training algorithm is presented with different training functions for the classification of yeast dataset. Here an effort has been made to decide the suitable training functions of artificial neural network system for the classification of yeast protein. The training functions that have been used are, respectively, Batch Training, Batch Gradient Descent, Gradient Descent with momentum, Resilience back propagation, One-step secant back propagation, Scaled Conjugate back propagation, Conjugate Gradient back propagation with Polak-Riebre updates (CGP) and Conjugate Gradient back propagation with Fletcher-Reeves updates (CGF), BFGS and Levenberg-Marquardt training algorithm . The yeast dataset used for this purpose has been chosen and from UCI machine learning repository. The performance of the classification network has been tested by various performance measures like correctness of classification, mean square error, and regression analysis.

Yeast Dataset Classification, Back Propagation Artificial Neural Network, Training Function Of Artificial Neural Network

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[12] UCI machine learning repository, :http://archive.ics.uci.edu/ml.

Shrayasi Datta and J. Paulchoudhury, "A Framework for Selection of Neural Network Training Functions towards the Classification of Yeast Data", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.23-29, 2015.

Making a machine understand and recognize a character is a challenge. This is also factored as many researchers have indulged in building worthy hardware and necessary software for character recognition. A well known tool to solve this problem is Artificial Neural Network (ANN). In this paper, Mexican Hat, a fixed-weight net, is used to improve the precision than the previous attempts which already have been introduced. Here 14 X 14 image matrices have been taken as input and then compressed into 7 X 7 matrices, reducing the elements which are of no or little significance. Mexican Hat is used to recognize the alphabet.

ANN, MCAMH, Compression, Mexican Hat

[1] Hand Written English Character Recognition using Column-wise Segmentation of Image Matrix (CSIM), Rakesh Kumar Mandal and N R Manna.
[2] Fundamentals of Neural Networks, Architectures, Algorithms and Applications, Laurene Fausett, Pearson Education
[3] Rakesh Kumar Mandal and N R Manna, Hand Written English Character Recognition using Row-wise Segmentation Technique (RST), International Symposium on Devices MEMS, Intelligent Systems & Communication (ISDMISC) 2011, Proceedings published by International Journal of Computer Applications (IJCA).
[4] Neural Networks, G.N. Swamy, G. Vijay Kumar, SCITECH

Rishav Upadhyay, Prasenjit Biswas, Partha Protim Poddar and Rakesh Kumar Mandal, "Pattern Recognition Using Modified Compression Algorithm With Mexican Hat (MCAMH)", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.30-35, 2015.

Swarm intelligence (SI) is a branch of artificial intelligence that has evolved based on the collective behavior of social insect colonies and other animal societies that have decentralized mode of work control. It is the collective behaviour (intelligence) exhibited by many individual elements for carrying out a common work that coordinate among them using a decentralized control and self-organization both in natural and artificial system. This paper proposes an optimized algorithm based on swarm intelligence algorithms to save people who are stuck in flood in the minimum time when the number of motorized inflatable rescue boats available is comparatively less to the number of people stuck in flood.

Artificial Intelligence; Ant Colony Optimization (ACO); Boids; Foraging; Motorized Inflatable Rescue Boats; Swarm Intelligence; Stigmery; Component; Formatting; Style; Styling; Insert

[1] http://en.wikipedia.org/wiki/Swarm_intelligence
[2] http://staff.washington.edu/paymana/swarm/ krin k_01.pdf
[3] http://www.scholarpedia.org/article/Swarm_intell igence
[4] Wang Jian – qun, Guoxu-yang, "Application of particle swarm optimization in flood optimal control of reservoir group", 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp. 856 – 859, 23-26 Sept. 2010
[5] M.Janga Reddy and S.Adarsh, "Overtopping Probability Constrained Optimal Design of Composite Channels Using Swarm Intelligence Technique", http://www.academia.edu
[6] Meraji S.H., Afshar M.H., Afshar A., "Optimal design of flood control systems using particle swarm optimisationalgorithm",International J. Industrial Eng. And Production Management(IJIE), Vol. 19 , No. 8-1, pp. 41 To 53, 2008.
[7] Wei Huang and XingNan Zhang, "Projection Pursuit Flood DisasterClassification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization", Journal of Water Resource and Protection, Vol. 3 No. 6, pp. 415-420. , 2011.
[8] https://www.ncdps.gov/Index2.cfm?a=000003, 0 00010, 000023, 000487, 000597, 001741, 001751
[9] http://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms
[10] www2.fiit.stuba.sk/~pospichal/prednaskaEA_STU…/antcolonyA1.ppt
[11] Morten Goodwin, Ole ChristofferGranmo and JaziarRadianti, "Escape planning in realistic fire scenarios with Ant Colony Optimisation", Springer Science.
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Dilip Roy Chowdhury and Subhajit Bose, "A SWARM Based Approach in Saving Flood Survivors", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.36-42, 2015.

The dimension of Artificial Intelligence is vast and this keeps on increasing when new methodologies are incorporated in it. The main purpose of this paper is to exploit the potential Application of Quantum computing in AI. Quantum Artificial Intelligence helps in solving some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. Using Quantum AI with Bioinformatics, we can get such an automata that will analyze the symptoms of patients and draw better models of how diseases develop and how to cure them. Application of QuAI in Space Research is endless. Quantum AI with Cryptanalysis can help to create more secure algorithms for Information security. So there are endless positive possibilities if application of Quantum Artificial Intelligence is applied correctly.

Quantum Theory; Quantum Computing; Machine Learning; Computer Vision; Artificial Intelligence.

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Dilip Roy Chowdhury and Saurabh Bhattacharya, "A Review on Quantum Vision Intelligent Learning (QuVIL) System", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.43-50, 2015.

Missing data plays a key role in practical fields. How to remove this gap is the main objective of data preprocessing step in data-mining. Many methods such as Statistical and Prediction approaches are generally used for missing data analysis, but unfortunately both approaches have some disadvantages and applicable for serial missing values in column. This paper tries to remove these gaps which are resulting from the two mentioned methods. The proposed algorithm tries to merge up two previously mentioned methods. This modified approach utilizes the potential knowledge and laws suggested by the data in Information System, and some basic mathematical concepts and some concepts from Rough Set Theory. Experimental results show that the proposed algorithm provides better result than the above mentioned two methods.

Data mining; missing data; Data preprocessing; Statistical methods; Prediction methods; Rough Set Theory,Serially

[1] Zaimei Zhang, Renefa Li, Zhongsheng Li,Haiyan Zhang,Gungaxue Yue. “An incomplete Data Analysis approach Based on Rough St Theory and Divide-and-Conquer Idea”, Fourth Int’ Conf On Fuzzy Systems and Knowledge Discovery(FSKD 2007).
[2] Sanjay Gaur and M.S. Dulawat “A Closest Fit Approach to Missing Attribute Values in Data Mining”, International Journal of Advances in Sciences and Technology Vol.2,No.4,2011 .
[3] Weihua Zhou,Wei Zhang,Yunique Fu.”An Incomplete data analysis approach using rough set theory”, Intelligent Mechatronics and animation.2004,pp.332-338.
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Discovery”. In: Knowledge discovery in advanced information systems,Pal,N.R., Jain,L.C., Teodereresku N.eds.Spinger,2002.
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[10] Zhang,S., Zhang,C., and Young,Q., “Data Preparation for data mining”. Applied Artificial Intelligence,Vol.17,pp.375-381,2003.
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Abhijit Sarkar and Kasturi Ghosh, "A Modified Approach for Missing Values in Data Mining Based on Rough Set Theory, Divided –and-Conquer, Closest Fit Approach Idea", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.51-58, 2015.

In this paper an authentication technique has been proposed based on G-Let transformation technique. Digital documents are authenticated by embedding Hough transform generated signature, which generated from the original autograph image. Cover image passes through G-Let transformation technique to generate n number of G-Lets. Out of which few selected G-lets are embedded with secret signature, generated by Hough transform technique. Rests of the G-Lets are used for adjustment to minimize the error factor. Experimental results are computed and compared with the existing authentication techniques Li’s method, Region-Based method based on SCDFT, which indicates better performance in AHSG-D4, in terms of better by means of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF).

Steganography; Peak Signal to Noise Ratio (PSNR). G-Lets; Authentication; Hough transform; Mean Square Error (MSE); Image Fidelity (IF)

[1] Sengupta, M., & Mandal, J. K. (2013, September 27–28). Authentication through Hough transformation generated Signature on G-Let D3 Domain (AHSG). Presented in first international conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA- 2013), Proceedings published with Procedia Technology, Elsevier, 10,121-130. ISSN 2212-0173, website: www.sciencedirect.com.
[2] Rajathilagam, B., Rangarajan, M., Soman, K.P., “G-Lets: A New Signal Processing Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 37– No.6, Published by Foundation of Computer Science, New York, USA, doi : 10.5120/4609-6591, January 2012.
[3] Li Yuancheng, Xiaolei Wang, “A watermarking method combined with Radon transform and 2D-wavelet transform”, IEEE, Proceedings of the 7th World Congress on Intelligent Control and Automation, June 25 - 27, Chongqing, China, 2008.
[4] T. T. Tsui, X. –P. Zhang, and D. Androutsos, Color Image Watermarking Usimg Multidimensional Fourier Transformation, IEEE Trans. on Info. Forensics and Security, vol. 3, no. 1, pp. 16-28, 2008.
[5] J. K. Mandal, Madhumita Sengupta, “Steganographic Technique Based on Minimum Deviation of Fidelity (STMDF)”, IEEE, Second International Conference on Emerging Applications of Information Technology (EAIT 2011), print ISBN: 978-1-4244-9683-9, doi: 10.1109/EAIT.2011.24, pp- 298 – 301, 2011.
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Madhumita Mallick, Madhumita Sengupta and J. K. Mandal, "Authentication through Hough Signature on G-Let D4 Domain (AHSG – D4)", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.59-67, 2015.

Automatic Number Plate Recognition (ANPR) is a real time embedded system which identifies the characters directly from the image of the license plate. Since different standard are used in different country, automatic number plate recognition system is different for each country. In this paper, a number plate recognition system for vehicles in India is proposed. This paper introduces a vehicle number plate identification system, which extracts the characters features of a plate, from an image captured by a digital camera. The system uses broad steps like thresholding, image segmentation, thinning and pattern matching for extraction of characters. The pattern matching is done using graph based method. Graph matching techniques are introduced to compute the similarity of characters extracted from number plate with the information of characters store in the database.

APNR, Segmentation, Thinning, Scaling, Pattern Matching, COG, Complete Bipartite Graph, Adjacency Matrix

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Bibek Ranjan Ghosh, Siddhartha Banerjee Prithviraj Pramanik, Soham Dey, "Automatic Number Plate Recognition (ANPR) of Vehicle using Image processing and Graph based Pattern Matching", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.68-75, 2015.