Volume-6 , Issue-2 , Feb - 2018 Go Back

Pedestrian re-identification technology has become the current research focus due to its wide range of applications. This study conducted cross dataset pedestrian re-identification to solve the problem that the single dataset’s difficulty for simulating the actual situation and its poor generalization ability. Deep learning has made remarkable achievements in the fields of machine learning recently, so the deep learning technology is integrated into cross datasets pedestrian re-identification system. Here we improved the three-layer convolutional neural network (CNN) structure proposed by Yang Hu in Asia Conference on Computer Vision (ACCV), 2014. The Batch Normalization (BN) layer has been added to reduce the over-fitting degree during training period and the adjusted cosine similarity algorithm is used for pedestrian feature match to solve the defect of cosine similarity algorithm. Finally we implemented the entire cross dataset pedestrian re-identification system and got the experimental results. The Shinpuhkan2014dataset was chosen as training set. We compared the training results before and after adding BN layer and found that test accuracy increased, test loss decreased and over-fitting phenomenon eased. The VIPeR and i_LIDS datasets were chosen as test sets. We evaluated the effects on VIPeR and i_LIDS based on the CNN model that training on Shinpuhkan2014dataset. The cumulative matching rate rank5 increased by 1.7% on VIPeR dataset compared with the current level, the rank10 and rank20 also increased. And the cumulative matching rate rank1 increased by 1.8% on i_LIDS dataset compared with the current level, the rank5 and rank10 also increased.

Cross dataset, Convolutional neural network, Batch normalization, Adjusted cosine similarity

[1] Tulsi Jain, Kushagra Agarwal, Ronil Pancholia, “Sentiment Analysis Based on a Deep Stochastic Network and Active Learning”, International Journal of Computer Sciences and Engineering (IJCSE), Vol.5, Issue.9, pp.1-6, 2017.
[2] Taigman, Y., Yang, M., Ranzato, M., Wolf, L, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification”, IEEE Conference on Computer Vision and Pattern Recognition, pp.1701-1708, 2014.
[3] Krizhevsky A, Sutskever I, Hinton G E, “ImageNet classification with deep convolutional neural networks”, International Conference on Neural Information Processing Systems. Curran Associates Inc. pp.1097-1105, 2012.
[4] Kawanishi, Y., Yang, W., Mukunoki, M., Minoh, M., “Shinpuhkan2014: A Multi-Camera Pedestrian Dataset for Tracking People across Multiple Cameras”, The Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV, 2014.
[5] Gray D, Brennan S, Tao H, “Evaluating appearance models for recognition, reacquisition, and tracking”, Vol. 3, Issue. 5, pp.1-7, 2007.
[6] Zheng W S, Gong S, Xiang T, “Person re-identification by probabilistic relative distance comparison”, In Computer Vision and Pattern Recognition(CVPR), IEEE,Vol. 42, pp.649-656,2011.
[7] Weinberger, K., Blitzer, J., Saul, L., “Distance metric learning for large margin nearest neighbor classification”, Advances in neural information processing systems 18, pp.1473-1480, 2006.
[8] Dikmen, M., Akbas, E., Huang, T. S., Ahuja, N., “Pedestrian recognition with a learned metric”, Lecture Notes in Computer Science, 6495 pp.501-512, 2010.
[9] Davis J V, Kulis B, Jain P, Sra S, Dhillon IS, “Information-theoretic metric learning”, International Conference on Machine Learning, ACM, Vol. 227, pp.209-216, 2007.
[10] Zheng W S, Gong S, Xiang T, “Person re-identification by probabilistic relative distance comparison”, In Computer Vision and Pattern Recognition(CVPR), Vol. 42, pp.649-656, 2011.
[11] Köstinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H, “Large scale metric learning from equivalence constraints”. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp.2288-2295, 2012.
[12] Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R, “Learning locally adaptive decision functions for person verification”, In Computer Vision and Pattern Recognition (CVPR), IEEE Conference, pp.3610-3617, 2013.
[13] Hu, Yang, Liao, Shengcai, Lei, Zhen, Yi, Dong, Li, Stan, “Exploring Structural Information and Fusing Multiple Features for Person Re-identification”, Computer Vision and Pattern Recognition Workshops, IEEE, pp.794-799, 2013.
[14] Gheissari N, Sebastian T B, Hartley R, “Person Re-identification Using Spatiotemporal Appearance”, Computer Vision and Pattern Recognition, Computer Society Conference on IEEE, pp.1528-1535, 2006.
[15] Hamdoun O, Moutarde F, Stanciulescu B, Steux B, “Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences”, International Conference on Distributed Smart Cameras, IEEE, pp.1-6, 2008.
[16] Wang X, Doretto G, Sebastian T, Rittscher J, Tu P, “Shape and Appearance Context Modeling”, International Conference on Computer Vision, IEEE, pp.1-8, 2007.
[17] Farenzena, M., Bazzani, L., Perina, A., Murino, V., & Cristani, M., “Person re-identification by symmetry-driven accumulation of local features”, IEEE Computer Vision and Pattern Recognition, Vol.23, pp.2360-2367,2010.
[18] Ma B, Su Y, “Local descriptors encoded by fisher vectors for person re-identification”, International Conference on Computer Vision. Springer-Verlag, pp.413-422, 2012.
[19] Dong S C, Cristani M, Stoppa M, Bazzani L, Murino V, “Custom Pictorial Structures for Re-identification”, British Machine Vision Conference(BMVC), Vol. 68, pp.1-11, 2011.
[20] Zhao R, Ouyang W, Wang X, “Unsupervised Salience Learning for Person Re-identification”, Computer Vision and Pattern Recognition, IEEE, pp.3586-3593, 2013.
[21] Zhao R, Ouyang W, Wang X, “Person Re-identification by Salience Matching”, International Conference on Computer Vision, IEEE, pp.2528-2535, 2014.
[22] Liu Y, Shao Y, Sun F, “Person re-identification based on visual saliency”, Intelligent Systems Design and Applications (ISDA) , Vol. 13,Issue. 6, pp.884-889, 2012.
[23] Gray D, Tao H, “Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features”, Computer Vision - ECCV 2008, European Conference on Computer Vision, Marseille, France, Proceedings. DBLP, pp.262-275, October 12-18, 2008,.
[24] Prosser, B., Zheng, W.S., Gong, S., Xiang, T., Mary, Q., “Person re-identification by support vector ranking”, In British Machine Vision Conference (BMVC), Aberystwyth, UK:BMVA Press, Vol. 2, Issue. 5, pp.1-11, 2010.
[25] Li W, Wang X, “Locally Aligned Feature Transforms across Views”, Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp.3594-3601, 2013.
[26] Liu C, Chen C L, Gong S, Wang G, “POP: Person Re-identification Post-rank Optimization”, IEEE International Conference on Computer Vision, IEEE Computer Society, pp.441-448, 2013.
[27] Ma A J, Yuen P C, Li J, “Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information”, IEEE International Conference on Computer Vision, IEEE, pp.3567-3574, 2014.
[28] Yi D, Lei Z, Liao S, Li SZ, “Deep Metric Learning for Person Re-identification”. International Conference on Pattern Recognition, IEEE, pp.34-39, 2014.
[29] Yang Hu, Dong Yi, Shengcai Liao, Zhen Lei, Stan Z, “Cross Dataset Person Re-identification”, Institute of Automation, Chinese Academy of Sciences (CASIA), ACCV, 9010, pp.650-664, 2014.
[30] Ioffe S, Szegedy C, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, arXiv preprint arXiv:1502.03167, pp.448-456, 2015.
[31] Globerson A, Roweis S T, “Metric Learning by Collapsing Classes”, Advances in Neural Information Processing Systems, Vol. 18, pp.451-458, 2006.
[32] Gray D, Tao H, “Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features”, Computer Vision - ECCV 2008, European Conference on Computer Vision, Marseille, France , Proceedings. DBLP, pp.262-275, October 12-18, 2008.
[33] Xing E P, Ng A Y, Jordan M I, Russell S, “Distance metric learning, with application to clustering with side-information”, International Conference on Neural Information Processing Systems, MIT Press, pp.521-528, 2002.

Hongmei Xie，Yanggang Zhou, Qiang Liu, "Deep Learning Feature Representation Applied to Cross Dataset Pedestrian Re-identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.1-11, 2018.

The malwares which are present with subtle with polymorphic techniques like self-mutation and emulation based mostly analysis evasion. Most anti-malware techniques are engulfed by the polymorphic malware threats that self-mutate with completely different variants at each attack. This analysis aims to contribute to the detection of malicious codes, particularly polymorphic malware by utilizing advanced static and advanced dynamic analysis for extraction of a lot of informative key options of a malware through code analysis, memory analysis and activity analysis. Correlation based mostly feature choice rules are rework features; i.e. filtering and choosing best and relevant options. A machine learning technique known as K-Nearest Neighbor (K-NN) are used for classification and detection of polymorphic malware analysis, results are supported the subsequent measuring metrics— True Positive Rate (TPR), False Positive Rate (FPR) and therefore the overall detection accuracy of experiments.

Malware Detection, Static Analysis, Dynamic Analysis, Polymorphic Malware, Machine Learning

[1] Lavasoft, “Detecting Polymorphic Malware.” [Online]. Available: http://www.lavasoft.com/mylavasoft/securitycenter/whitepapers/detecting-polymorphic-malware. [Accessed: 01-Sep-2016].
[3] A. Sharma and S. K. Sahay, “Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey,” International Journal of Computer Applications, vol. 90, no. 2, pp. 7–11, 2014.
[4] S. K. Pandey and B. M. Mehtre, “A lifecycle based approach for malware analysis,” Proceedings - 2014 4th International Conference on Communication Systems and Network Technologies, CSNT 2014, pp. 767–771, 2014.
[5] Y. Prayudi and S. Yusirwan, “the Recognize of Malware Characteristics Through Static and Dynamic Analysis Approach As an Effort To Prevent Cybercrime Activities,” Journal of Theoretical and Applied Information Technology (JATIT), vol. 77, no. xx, pp. 438–445, 2015.
[6] M. Sikorski and A. Honig, Practical Malware analysis: The hands-on guide to dissecting malicious software. San Francisco: No Starch Press, Inc., 2012.
[7] M. Ahmadi, A. Sami, H. Rahimi, and B. Yadegari, “Malware detection by behavioural sequential patterns,” Computer Fraud & Security, vol. 2013, no. 8, pp. 11–19, 2013.
[8] S. Kumar, C. Rama Krishna, N. Aggarwal, R. Sehgal, and S. Chamotra, “Malicious data classification using structural information and behavioral specifications in executables,” 2014 Recent Advances in Engineering and Computational Sciences, RAECS 2014, pp. 1–6, 2014.
[9] S. Cesare, Y. Xiang, and W. Zhou, “Malwise-an effective and efficient classification system for packed and polymorphic malware,” IEEE Transactions on Computers, vol. 62, no. 6, pp. 1193–1206, 2013.
[10] D. Arish and M. Singh, “Behavior Analysis of Malware Using Machine Learning,” in Contemporary Computing (IC3), 2015 Eighth International Conference on, 2015, pp. 481–486.
[11] G. Liang, J. Pang, and C. Dai, “A Behavior-Based Malware Variant Classification Technique,” International Journal of Information and Education Technology, vol. 6, no. 4, pp. 291–295, 2016.
[12] V. Naidu and A. Narayanan, “Needleman-Wunsch and Smith-Waterman Algorithms for Identifying Viral Polymorphic Malware Variants,” 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), no. August, pp. 326–333, 2016.
[13] M. Ahmadi, A. Sami, H. Rahimi, and B. Yadegari, “Malware detection by behavioural sequential patterns,” Computer Fraud and Security, vol. 2013, no. 8, pp. 11–19, 2013.
[14] P. M. Comar, L. Liu, S. Saha, P. N. Tan, and A. Nucci, “Combining supervised and unsupervised learning for zero-day malware detection,” Proceedings - IEEE INFOCOM, pp. 2022–2030, 2013.
[15] J. Park, S. Choi, and D. Y. Kim, “Malware Analysis and Classification: A Survey,” Lecture Notes in Electrical Engineering, vol. 215, no. April, pp. 449–457, 2013.
[16] L. Zeltser, “Malware sample sources for researchers.” [Online]. Available: https://zeltser.com/malware-sample-sources. [Accessed: 28-Feb-2016].
[17] Emmanuel Masabo Makerere ,Kyanda Swaib Kaawaase, Julianne Sansa-Otim Makerere University, Kampala, Uganda Damien Hanyurwimfura University of Rwanda, Kigali, Rwanda
[18] V. Kumar and S. Minz, “Feature Selection: A literature Review,” Smart Computing Review, vol. 4, no. 3, pp. 211–229, 2014.
[19] A. Azab, R. Layton, M. Alazab, and J. Oliver, “Mining malware to detect variants,” Proceedings - 5th Cybercrime and Trustworthy Computing Conference, CTC 2014, pp. 44–53, 2015.

Faiz Baothman, Muzammil H Mohammed, "Polymorphic Malware in Executable Files and the Approaches towards their Detection and Extraction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.12-17, 2018.

Induction motors have a vital influence in the protected and productive running of any modern plant. Faulty condition of these motors because of electrical and mechanical faults may antagonistically affect on line production and can cause unforeseen downtime. Along these lines, discovery of variations from the norm in the motor would maintain a strategic distance from exorbitant breakdowns and especially early location of early rotor bar faults are critical for productive activity of substantial induction motors. In this paper, execution of induction motor under 2D different failure’s modes like broken rotor bars are examined under different loading condition using FEM approach. Maxwell 2D Transient solver is utilized for breaking down the conductor of motor under solid and diverse faulty conditions.

Maxwell Equations, Eddy Effect, Fault Detection, Broken rotor bar fault

[1] A. H. Bonnet, “Analysis of rotor failures in squirrel cage induction machines,” IEEE Trans. Ind. Appl., vol. 24, no. 6, pp. 1124–1130,
Nov./Dec. 1988.
[2] A. H. Bonnet and G. C. Soukup, “Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors,” IEEE Trans. Ind. Appl., vol. 28, no. 4, pp. 921–937, Jul./Aug. 1992.
[3] M. L. Sin, W.L.Soong and N.Ertugrul, "Induction Machine On-Line Condition Monitoring and Fault Diagnosis - A Survey", AUPEC2003, Australasian Universities Power Engineering Conference, Christchurch, New Zealand, pp. 1-6, 2003.
[4] W.R. Finley, M.M. Hodowanec and W.G. Holter, “An Analytical Approach to Solving Motor Vibration Problems,” IEEE Trans. on Ind. Appl., vol. 36, no. 5, Sep./Oct. 2000, pp.1467-1480.
[5] N. Arthur and J. Penman, “Induction Machine Condition Monitoring with Higher Order Spectra,” IEEE Trans. on Ind. Electronics, vol. 47, no. 5, Oct. 2000, pp. 1031- 1041.
[6] B.S. Payne, A. Ball and F. Gu, “Detection and Diagnosis on Induction Motor Faults using Statistical Measures,” International Journal of Condition Monitoring and Diagnostics Engineering Management, vol. 5, no. 2, Apr. 2002, pp. 5-19.
[7] M.F. Cabanas, M.G. Melero, G.A. Orcajo, F. Rodriguez Faya and J. Solares Sariego, “Experimental Application of Axial Leakage Flux to the Detection of RotorAsymmetries, Mechanical Anomalies and Interturn Short-Circuits in Working Induction Motors,” Int. Conf. on Elect. Machines, 1998, pp.420-425.
[8] Sergio CRUZ, Flavio GASPAR, “A new method to diagnose rotor faults in 3- phase induction motors coupled to time varying loads,” PRZEGLAD ELEKTROTECHNICZNY (Electrical Review), ISSN 0033-2097, R.88 NR 1a/ 2012, pp. 202-206
[9] Manuel Pineda- Sanchez, Martin Riera-Guasp, Jose Alfonso Antonino-Daviu, Jose Roger-Folch, Juan Perez- Cruz and Ruben Puche-Panadero, “Instantaneous frequency of the left sideband harmonic during the start –up transient: A new method for diagnosis of broken bars,” IEEE Trans. on Industrial Electronics, Vol.56, No. 11, Nov 2009, pp. 4557-4570.
[10]Ahmed, N. Ertugrul, and W. L. Soong, "A Study on the Detection of Fault Frequencies for Condition Monitoring of Induction Machines,” Australian Universities Power Engineering Conference, Tasmania, Australia 2005.
[11] R. Fiser and S. Ferkolj, “Application of Finite Element Method to Predict Damaged Induction Motor Performance,” IEEE Trans. on Magnetics, Vol. 37, no 5,Sep. 2001, pp. 3635-3639.
[12] W. T. Thomson and A. Barbour, “On-Line Current Monitoring and Application of a Finite Element Modelling of Induction Motor Faults with Finite Element Method to predict the Level of Static Airgap Eccentricity in Three-Phase Induction Motors,” IEEE Transactions on energy Conversion, Vol. 13, no 4, December 1998, pp. 347-352.
[13] R. J. Tallam, T. G. Habetler and R. G. Harley, “Transient Model for Induction Machines With Stator Winding Turns Faults,” IEEE Transactions on I8ndustry Applications, Vol. 38, no 3, May/June 2002, pp. 632-637.
[14] E. Schaeffer, E. L. Carpentier and M. E. Zaim, “Failure Detection in Induction Machine by Means of Parametric Identification,” CESA’98, IEEE Conference on Computational Engineering in Systems Applications, Nabeul-Hammament, Tunisia, 1-4 April 1998.
[15] R. Beguenane and M.E.H. Benbouzid, “Induction Motors Thermal Monitoring By Means of Rotor-Resistance Identification”, IEEE Transaction on Energy Conversion, Vol. 14, No 3, September 1999, pp 566 – 570.
[16] J. F. Bangura and N. A. Demerdash, “Diagnosis and Charactization of effects of Broken Rotor Bars and Connectors in Squirrel-Cage Induction Motors by a Time-Stepping Coupled Finite Element-State Space Modeling Approach,” IEEE Transactions on energy Conversion, Vol. 15, December 1999, pp. 1167-1176.
[17] R. J. Povinelli, J. F. Bangura, N. A. Demerdash and R. H. Brown, “Diagnostic of Bar and End-Ring Connector Breakage Faults in Poly-phase Induction Motors Through a Novel Dual Track of Time-Stepping Coupled FE-State Space Modelling,” IEEE Transactions on energy Conversion, Vol. 17, no 1, March 2002, pp. 39-46.
[18] F. Filippetti, G. Franceschini, P. Vas and C. Tassoni “Broken Bar Detection in Induction Machines: Comparison between Current spectrum Approach and Parameter Estimation Approach”, IEEE Industry Application Society, Vol. 1, pp. 95-102, 1994.
[19] F. Filippetti, G. Franceschini and C. Tassoni “Neural Networks Aided On-Line Diagnostics of Induction Motor Rotor Faults”, IEEE Industry Application Society, Vol. 1, pp. 316-323, 1993.
[20] M.L. Sin, W.L. Soong and N. Ertugrul, "Induction Machine On-Line Condition monitoring and Fault diagnosis- A Survey" AUPEC 2003, Christchurch, New Zealand.
[21] F. Filippetti, G. Franceschini, C. Tassoni, G. Gentile, S. Meo, A. Ometto and N.Rotondale, “Deterministic Approach and Neural Network Approach for Stator Short Circuits Diagnosis in Induction Motor,” 7 Industry Application Society, Vol. 1, pp. 316-323, 1993.
[22] M. Y. Chow, “Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection,” World Scientific, Singapore,1997.
[23] W. T. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults”, IEEE Industry Applications Magazine, pp. 26-34, July/August 2001.
[24] Ahmed, R. Supangat, J. Grieger, J, N. Ertugrul, and W.L Soong, "A Baseline Study for On-Line Condition Monitoring of Induction Machines." Australian Universities Power Engineering Conference, Brisbane, Australia 2004,
[25] M.L. Sin, W.L. Soong and N. Ertugrul, "Induction Machine On-Line Condition monitoring and Fault diagnosis- A Survey" AUPEC 2003, Christchurch, New Zealand.
[26] A.A. Da Silva et al., “Rotating machinery monitoring and diagnosis using short-time Fourier transform and wavelet techniques,” in Proc. 1997 Int. Conf. Maintenance and Reliability, vol. 1, Knoxville, TN, pp. 14.01–14.15.
[27] B. S. Payne, A. Ball and F. Gu, “Detection and Diagnosis on Induction Motor Faults using Statistical Measures”, International Journal of Condition Monitoring and Diagnosis Engineering Management, vol. 5, no.2, April 2002, pp.5-19.
[28] A. H. Bonnett and G. C. Soukup, “Analysis of Rotor Failures in Squirrel-Cage Induction Motors,” IEEE Transactions on Industry Applications, vol. 24, no. 6, pp. 1124–1130, November/December 1988

Kalpana Sheokand, Neelam Turk, "Behaviour Analysis of Induction Motor Under Various Fault Conditions of Rotor bar at Different Loading," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.18-24, 2018.

There is a great need for Artificial Intelligence and Nature Inspired Metaheuristic Algorithms for real world problems like Traveling Salesman Problem (TSP) belonging to NP-Hard Optimization problems which are hard to solve using mathematical formulation models. They are also a requirement for fast and accurate algorithms, specifically those that find out a node from start to the goal with the minimum cost, distance, time, money, energy etc. The Traveling Salesman Problem (TSP) is a combinatorial optimization problem which in it’s the purest form has a respective application for instance planning, logistics, and manufacture of microchips, military and traffic and so on. Metaheuristic techniques are general algorithmic frameworks including nature-inspired designs to solve complex optimization problems and they are a fast-growing research domain since a few decades. This paper proposes to solve this problem using hybridization of ACO (Ant Colony Optimization) and SA (Simulated Annealing). Ant Colony Optimization (ACO) is a population-based metaheuristic that can be used to find out appropriate approximate solutions to understand difficult NP-Hard optimization problems. Simulated Annealing (SA) is also a population-based metaheuristic that is inspired by annealing process proceeded with higher level temperature rate; it starts position on a first solution to maximum temperature, where the exchange states are accepted with a desired global extreme point is out of sight among many, poor temperature and probability density function, local update extrema. Moreover, MATLAB programming is used to implement the algorithms using solved TSP are three benchmarks on the same platform conditions for ACO, SA and Hybrid ACO-SA.

Metaheuristic Hybrids, Ant Colony Optimization (ACO), Simulated Annealing (SA), Traveling Salesman Problem (TSP), NP-Hard Optimization Problems, Global Pheromone Update (GPU), Local Pheromone Update (LPU)

[1] Divya Gupta, “Solving TSP using various Meta-heuristic Algorithms”, International Journal of Engineering Science, Vol. 1, pp. 26-30, 2013
[2] Anas Arram, Masri Ayob, “Comparative Study of Metaheuristic Approaches for solving Traveling Salesman Problems”, Asian Journal of Applied Sciences, Vol. 7, pp. 662-670, 2014
[3] Gunther R. Raidl, Jakob Puchinger, “Metaheuristic Hybrids”, International Series in Operations Research and Management Science, Vol. 146, pp. 469-496, 2010
[4] Teodor Gabriel Crainic, Michel Toulouse, “Parallel Meta-Heuristics”, International Series in Operations Research and Management Science, Vol. 146, pp. 497-541, 2010
[5] Marco Dorigo, Luca Maria Gambardella, “Ant Colony System: A cooperative learning approach to the Traveling Salesman Problem”, IEEE Transactions on Evolutionary Computation, Vol. 1, pp. 53-66, 1997
[6] Marcus Randall, Andrew Lewis, “A Parallel Implementation of ACO”, Journal of Parallel and Distributed Computing, Vol. 62, pp. 1421-1432, 2002
[7] Musa Peker, Baha Sen, “An efficient solving of the TSP: The Ant Colony System having parameters optimized by the Taguchi Method”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 21, pp. 2015-2036, 2013
[8] Pan Junjie, Wang Dingwei, “An ACO Algorithm for Multiple Traveling Salesman Problem”, Proceedings of the First International Conference on Innovative Computing, Information and Control, Vol. 1, pp. 210-213, 2006
[9] Shu-Chuan Chu, John F. Roddick, “Ant Colony System with communication strategies”, Information Sciences, Vol. 167, pp. 63-76, 2004
[10] Zar Chi Su Su Hlaing, May Aye Khine, “Solving Traveling Salesman Problem by using Improved ACO Algorithm”, International Journal of Information and Education Technology, Vol. 1, pp. 404-409, 2011
[11] Chiranjib Patra, Pratyush, “Vector Ant Colony Optimization and Traveling Salesman Problem”, Computer Science and Information Technology, Vol. 4, pp. 31-39, 2014
[12] Luca Maria Gambardella, Macro Dorigo, “Solving Symmetric and Asymmetric Traveling Salesman Problems by Ant Colonies”, IEEE Conference on Evolutionary Computation, Vol. 6, pp. 622-627, 1996
[13] Dasaradh R. Mallampati, Pooja P. Mutalik, “A Parallel Multi-Phase Implementation of Simulated Annealing for the Traveling Salesman Problem”, Proceedings of the Sixth Distributed Memory Computing Conference, Vol. 6, pp. 488-491, 1991
[14] Husamettin Bayram, Ramazan Sahin, “A New Simulated Annealing Approach for Traveling Salesman Problem”, Mathematical and Computational Applications, Vol. 18, pp. 313-322, 2013
[15] Parham Azimi, Ramtin Rooeinfar, “A New Hybrid Parallel Simulated Annealing Algorithm for Traveling Salesman Problem with Transporters”, Journal of Optimization in Industrial Engineering, Vol. 15, pp. 1-13, 2014
[16] Nitesh M. Sureja, Bharat V. Chawda, “Random Traveling Salesman Problem”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, pp. 621-624, 2012
[17] David Bookstaber, “Simulated Annealing for Traveling Salesman Problem”, Springer, Vol. 1, pp. 1-9, 1997
[18] M.A. Rufai, R.M. Alabison, “Solution to the Traveling Salesperson Problem using Simulated Annealing Algorithm”, Electronic Journal of Mathematical Analysis and Applications, Vol. 5, pp. 135-142, 2017
[19] Lin Xiong, Shunxin Li, “Solving Traveling Salesman Problem Based on the Improved SA Algorithm with Sequential Access Restrictions”, Advances in Intelligent Systems Research, Vol. 130, pp. 610-616, 2016
[20] Zicheng Wang, Xuitang Geng, “An Effective Simulated Annealing Algorithm for solving the Traveling Salesman Problem”, Journal of Computational and Theoretical Nanoscience, Vol. 6, pp. 1680-1686, 2009
[21] Seyed Ahmad Sheibat Alhamdy, Mohammad Majdara, “Solving Traveling Salesman Problem using Ant Colony Optimization Algorithm and comparing with Tabu Search, Simulated Annealing and Genetic Algorithm”, Journal of Applied Sciences Research, Vol. 8, pp. 434-440, 2012
[22] Adewole A.P., Otubamowo K., “A Comparative Study of Simulated Annealing and Genetic Algorithm for solving the Traveling Salesman Problem”, International Journal of Applied Information Systems, Vol. 4, pp. 6-12, 2012
[23] Mateusz Borowski, Rafal Machon, “A Modified Traveling Salesman Problem: Algorithms and Experimentation System”, Journal of Intelligent Computing, Vol. 6, pp. 59-67, 2015
[24] Hashim Ali, Yasir Shah, “Solving Traveling Salesman Problem through Optimization Techniques using Genetic Algorithm and Ant Colony Optimization”, Journal of Applied Environmental and Biological Sciences, Vol. 6, pp. 55-62, 2016
[25] M. Fikret Ercan, Xiang Li, “Particle Swarm Optimization and its Hybrid”, International Journal of Computer and Communication Engineering, Vol. 2, pp. 52-55, 2013
[26] Muhammed Basheer Jasser, Mohamad Sarmini, “Ant Colony Optimization and a variation of Bee Colony Optimization (BCO) in solving Traveling Salesman Problem, a comparative study”, International Journal of Computer Applications, Vol. 96, pp. 1-8, 2014
[27] Younis Elhaddad, Omar Sallabi, “A New Hybrid Genetic and Simulated Annealing Algorithm to solve the Traveling Salesman Problem”, Proceedings of the world congress on Engineering, Vol. 1, pp. 11-14, 2010
[28] Huiling Bao, “A Two-Phase Hybrid Optimization Algorithm for solving complex optimization problem”, International Journal of Smart Home, Vol. 9, pp. 27-36, 2015
[29] Elena Simona, Nicoara, “Population Based Metaheuristics : A Comparative Analysis”, International Journal of Science and Engineering Investigations, Vol. 1, pp. 84-88, 2012
[30] Rajesh Matai, Surya Prakash Singh, Murari Lal Mittal, “Traveling Salesman Problem: An Overview of Applications, Formulations, and Solution Approaches”, Traveling Salesman Problem, Theory and Applications, pp. 1-24, 2010
[31] Ivan Brezina Jr., Zuzana Cickova, “Solving the Traveling Salesman Problem Using Ant Colony Optimization”, Management Information Systems, Vol. 6, pp. 010-014, 2011
[32] Zar Chi Su Su Hlaing, May Aye Khine, “An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem”, International Conference on Information Communication and Management, Vol. 16, pp. 54-59, 2011
[33] Marco Dorigo, “Ant Algorithms Solve Difficult Optimization Problems”, Springer, Vol. 1, pp. 11-22, 2001
[34] V. Cerny, “Thermodynamical Approach to Traveling Salesman Problem: An Efficient Simulated Annealing”, Journal of Optimization Theory and Applications, Vol. 45, pp. 41-51, 1985
[35] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, “Optimization by Simulated Annealing”, Science, Vol. 220, pp. 671-680, 1983

Supreet Kaur, Kiranbir Kaur, "A Novel Approach to solve Traveling Salesman Problem (TSP) using Metaheursitic Hybrid Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.25-38, 2018.

Internet facility along with a web browser has become indispensable needs to do any government as well as non-government task. To devise and evaluate an unbeaten website, web engineers have to consider the two factors, first is the role of website for achieving company or organizational objectives, afterwards, various types of users with their needs. But both of these factors cannot be fully elicited and defined, as the opinions as well as the ambitions of organizers, website users plus IT professionals are entirely different. In order to find the methods along with approaches used for website evaluation, this paper takes a systematic review of the most popular models which are in sphere of website evaluation in distinct domains of websites. Two types of models are studied, one which can be applied to every domain, whereas other which are oriented towards the specific domain with specific mission. It also analyses the practical methods and approaches to find their percentage usage in previous studies of website evaluation. It also investigates the types of assessors involved in these studies. Finally, it winds up with proposed perspectives what a future evaluation study should be endowed with. It is deduced that recent studies have adopted a user judgement method along with certain automation or numerical computation technique. The findings provided by review can benefit the industry readership as well as academicians to evaluate the website for relevance to their own settings in various situations.

Web engineering, Web assessment, Web domains, Website evaluation, Design quality

[1] H. Achour, N. Bensedrine, “An evaluation of internet banking and online brokerage in Tunisia”, In Proceedings of the 1st International Conference on E-Business and E-learning (EBEL), Amman, Jordan, pp. 147-158, 2005.
[2] A. P. Afonso, J. R. Lima, M. P. Cota, “A heuristic evaluation of usability of Web interfaces”, In Information Systems and Technologies (CISTI), 2012 7th Iberian Conference on, pp. 1-6, IEEE, June 2006.
[3] J. Alhelalat, E. M. Ineson, T. Jung, K. Evans, “The Evaluation of Hotel Websites’ Quality, Usability and Benefits: Developing a Testing Model”, In Proceedings of Euro-CHRIE Conference, 2008.
[4] M. Alomari, P. Woods, K. Sandhu, “Predictors for e-government adoption in Jordan: Deployment of an empirical evaluation based on a citizen-centric approach”, Information Technology and People, Vol.25, Issue.2, pp.207-234, 2012.
[5] I. Alsmadi, A.T. Al-Taani, N.A. Zaid, “Web structural metrics evaluation”, In Developments in E-systems Engineering (DESE), pp.225-230, IEEE, September 2010.
[6] M. Asmaran, “Quantitative and Qualitative Evaluation of Three Search Engines (Google, Yahoo, and Bing)”, American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), Vol.26, Issue.2, pp.97-106, 2012.
[7] S. Baloglu, Y.A. Pekcan, “The website design and Internet site marketing practices of upscale and luxury hotels in Turkey”, Tourism management, Vol.27, Issue.1, pp.171-176, 2006.
[8] S.J. Barnes, R.T. Vidgen, “An integrative approach to the assessment of e-commerce quality”, J. Electron. Commerce Res., Vol.3, Issue.3, pp.114-127, 2002.
[9] S.J. Barnes, R. Vidgen, “Interactive e-government: evaluating the web site of the UK Inland Revenue”, Journal of Electronic Commerce in Organizations (JECO), Vol.2, Issue.1, pp.42-63, 2004.
[10] S.J. Barnes, R. T. Vidgen, “Data triangulation and web quality metrics: A case study in e-government”, Information and Management, Vol.43, Issue.6, pp.767-777, 2006.
[11] U. Bastida, T.C. Huan, “Performance evaluation of tourism websites` information quality of four global destination brands: Beijing, Hong Kong, Shanghai, and Taipei”, Journal of Business Research, Vol.67, Issue.2, pp.167-170, 2014.
[12] U. Bauernfeind, N. Mitsche, “The application of the data envelopment analysis for tourism website evaluation”, Information Technology and Tourism, Vol.10, Issue.3, pp.245-257, 2008.
[13] P. Becker, P. Lew, L. Olsina, “Specifying process views for a measurement, evaluation, and improvement strategy”, Advances in Software Engineering, pp. 28, 2012, http://dx.doi.org/10.1155/2012/949746
[14] T. Berners-Lee, “Web Index”, 2014, http://theWebindex.org, Accessed 11 July 2017
[15] E. Bertino, E. Ferrari, A. Perego, “A general framework for web content filtering”, World Wide Web, Vol.13, Issue.3, pp.215-249, 2010.
[16] G. Büyüközkan, D. Ruan, O. Feyzioğlu, “Evaluating e‐learning web site quality in a fuzzy environment”, International Journal of Intelligent Systems, Vol.22, Issue.5, pp.567-586, 2007.
[17] S. Cebi, “Determining importance degrees of website design parameters based on interactions and types of websites”, Decision Support Systems, Vol.54, Issue.2, pp.1030-1043, 2013a.
[18] S. Cebi, “A quality evaluation model for the design quality of online shopping websites”, Electronic Commerce Research and Applications, Vol.12, Issue.2, pp.124-135, 2013b.
[19] S.C. Chiemeke, A.E. Evwiekpaefe, F.O. Chete, “The adoption of Internet banking in Nigeria: An empirical investigation”, Journal of Internet Banking and Commerce, Vol.11,Issue.3, pp.1-10, 2006.
[20] K.C. Chinthakayala, C. Zhao, J. Kong, “A comparative study of three social networking websites’, World Wide Web, Vol.17, Issue.6, pp.1233-1259, 2014.
[21] W.C. Chiou, C.C. Lin, C. Perng, “A strategic framework for website evaluation based on a review of the literature from 1995–2006”, Information and management, Vol.47, Issue.5, pp.282-290, 2010.
[22] W.C. Chiou, C.C. Lin, C. Perng, “A strategic website evaluation of online travel agencies”, Tourism Management, Vol.32, Issue.6, pp.1463-1473, 2011.
[23] W. Chmielarz, M. Zborowski, “Comparative Analysis of Electronic Banking Websites in Poland in 2014 and 2015”, In Information Technology for Management, pp.147-161, 2016, Springer International Publishing.
[24] S.C. Chu, Y. Kim, “Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites”, International journal of Advertising, Vol.30, Issue.1, pp.47-75, 2011.
[25] M.A. Corigliano, R. Baggio, “On the significance of tourism website evaluations”, Information and Communication Technologies in Tourism 2006, pp.320-331, 2006.
[26] J. Delfos, T. Tan, B. Veenendaal, “Design of a web-based lbs framework addressing usability, cost, and implementation constraints”, World Wide Web, Vol. 13, Issue.4, pp.391-418, 2010.
[27] P.J. Denning, D.E. Comer, D. Gries, “Computing as a discipline”, Computer, Vol.22, Issue.2, pp.63-70, 1989.
[28] Y. Deshpande, S. Hansen, “Web engineering: creating a discipline among disciplines”, IEEE MultiMedia, Vol.8, Issue.2, pp.82-87, 2001.
[29] Y. Deshpande, S. Murugesan, A. Ginige, “Web engineering”, Journal of Web Engineering, Vol.1, Issue.1, pp.3–17, 2002.
[30] F.S. Effendi, I. Alfina, “Quality evaluation of airline`s e-commerce website, a case study of AirAsia and Lion Air websites”, In Advanced Computer Science and Information Systems (ICACSIS), International Conference on 2014, pp.125-128, IEEE, Oct 18, 2014,
[31] N. Elkhani, S. Soltani, A. Bakri, “An Effective Model for Evaluating Website Quality Considering Customer Satisfaction and Loyalty: Evidence of Airline Websites”, IJCSI International Journal of Computer Science Issues, Vol.10, Issue.2, pp.109-117, 2013.
[32] A. Ellahi, R.H. Bokhari, “Key quality factors affecting users` perception of social networking websites”, Journal of Retailing and Consumer Services, Vol.20, Issue.1, pp.120-129, 2013.
[33] I.J. Gabriel, “Usability metrics for measuring usability of business-to-consumer (b2c) e-commerce sites”, In Proceedings of the 6th Annual ISOnEworld Conference, Las Vegas, NV, pp.74.1-74.19, April 11-13, 2007.
[34] A. Garcia, C. Maciel, F. Pinto, “A quality inspection method to evaluate e-government sites”, Electronic government, pp.198-209, 2005.
[35] J.A. Greene, N.K. Choudhry, E. Kilabuk, W.H. Shrank, “Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook”, Journal of general internal medicine, Vol.26, Issue.3, pp 287-292, 2011.
[36] M. Grimsley, A. Meehan, “e-Government information systems: Evaluation-led design for public value and client trust”, European Journal of Information Systems, Vol.16, Issue.2, pp.134-148, 2007.
[37] S. Gullikson, R. Blades, M. Bragdon, “The impact of information architecture on academic web site usability”, The Electronic Library, Vol.17, Issue.5, pp.293-304, 1999.
[38] A. Henriksson, Y. Yi, B. Frost, M. Middleton, “Evaluation instrument for e-government websites”, Electronic Government, Vol.4, Issue.2, pp.204-226, 2007.
[39] A. Howitt, S. Clement, S. de Lusignan, “An evaluation of general practice websites in the UK”, Family Practice, Vol.19, Issue 5, pp.547-556, 2002.
[40] C.L. Hsu, K.C. Chang, M.C. Chen, “The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators”, Information Systems and e-Business Management, Vol.10, Issue.4, pp.549-570, 2012.
[41] G.J. Huang, T.C. Huang, J.C. Tseng, “A group-decision approach for evaluating educational web sites”, Computers and Education, Vol.42, Issue.1, pp.65-86, 2004.
[42] C. Ip, R. Law, H.A. Lee, “A review of website evaluation studies in the tourism and hospitality fields from 1996 to 2009”, International Journal of Tourism Research, Vol.13, Issue.3, pp.234-265, 2011.
[43] B. Bakariya, G.S. Thakur, "Effectuation of Web Log Preprocessing and Page Access Frequency using Web Usage Mining", International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.1-5, 2013.
[44] R. Shrivastva, S. Mewad, P. Sharma, “An Approach to Give First Rank for Website and Webpage Through SEO”, International Journal of Computer Sciences and Engineering, Vol. 2, Issue.4, pp.13-17, 2014.
[45] L. Kumar, N. Kumar, “SEO technique for a website and its effectiveness in context of Google search engine”, International Journal of Computer Sciences and Engineering, Vol. 2, Issue.6, pp.113-118, 2014.
[46] B.J. Jansen, A. Spink, “How are we searching the World Wide Web? A comparison of nine search engine transaction logs”, Information processing and management, Vol.42, Issue.1, pp.248-263, 2006.
[47] H. Jati, D.D. Dominic, “Quality evaluation of e-government website using web diagnostic tools: Asian case”, In Information Management and Engineering, 2009. ICIME`09. International Conference on, pp.85-89, IEEE, April 2009.
[48] S. Joo, S. Lin, K. Lu, “A usability evaluation model for academic library websites: efficiency, effectiveness and learnability”, Journal of Library and Information studies, Vol.9, Issue.2, pp.11-26, 2011.
[49] A. Kaur, D. Dani, “The Web Navigability Structure of E-Banking in India”, International Journal of Information Technology and Computer Science (IJITCS), Vol.5, Issue.4, pp.29-37, 2013.
[50] A. Keenan, A. Shiri, “Sociability and social interaction on social networking websites”, Library Review, Vol.58, Issue.6, pp.438-450, 2009.
[51] T. Kincl, P. Štrach, “Measuring website quality: asymmetric effect of user satisfaction”, Behaviour and Information Technology, Vol.31, Issue.7, pp.647-657, 2012.
[52] B. Kitchenham, O.P. Brereton, D. Budgen, “Systematic literature reviews in software engineering–a systematic literature review”, Information and software technology, Vol.51, Issue.1, pp.7-15, 2009.
[53] H. Korda, Z. Itani, “Harnessing social media for health promotion and behavior change”, Health promotion practice, Vol.14, Issue.1, pp.15-23, 2013.
[54] R. Law, S. Qi, D. Buhalis, “Progress in tourism management: A review of website evaluation in tourism research”, Tourism management, Vol.31, Issue.3, pp.297-313, 2010.
[55] S. Lee, R.J. Koubek, “The effects of usability and web design attributes on user preference for e-commerce web sites”, Computers in Industry, Vol.61, Issue.4, pp.329-341, 2010.
[56] K.Y. Lin, H.P. Lu, “Why people use social networking sites: An empirical study integrating network externalities and motivation theory”, Computers in human behavior, Vol.27, Issue.3, pp.1152-1161, 2011.
[57] P. Lorca, J. de Andrés, A.B. Martínez, “Does Web accessibility differ among banks?”, World Wide Web, Vol.19, Issue.3, pp.351, 2016.
[58] J. Lu, Z. Lu, “Development, distribution and evaluation of online tourism services in China”, Electronic Commerce Research, Vol.4, Issue.3, pp.221-239, 2004.
[59] Y. Lu, Z. Deng, B. Wang, “Analysis and evaluation of tourism e-commerce websites in China”, International Journal of Services, Economics and Management, Vol.1, Issue.1, pp.6-23, 2007.
[60] R. Malhotra, A. Sharma, “A neuro-fuzzy classifier for website quality prediction”, In Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, pp.1274-1279, IEEE, August 2013.
[61] E.N. Martins, L.S. Morse, “Evaluation of internet websites about retinopathy of prematurity patient education”, British journal of ophthalmology, Vol.89, Issue.5, pp.565-568, 2005.
[62] M. Matera, M.F. Costabile, F., Garzotto, P. Paolini, “SUE inspection: an effective method for systematic usability evaluation of hypermedia”, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol.32, Issue.1, pp.93-103, 2002.
[63] S. Mavromoustakos, A.S. Andreou, “WAQE: a web application quality evaluation model”, International Journal of web engineering and technology, Vol.3, Issue.1, pp.96-120, 2006.
[64] N.M. Medina, J. Burella, G. Rossi, J. Grigera, E.R. Luna, “An incremental approach for building accessible and usable web applications”, In International Conference on Web Information Systems Engineering, pp.564-577, Springer, Berlin, Heidelberg, December 2010.
[65] L. Mich, “Evaluating website quality by addressing quality gaps: a modular process”, In Software Science, Technology and Engineering (SWSTE), 2014 IEEE International Conference on, pp.42-49, IEEE, June 2014.
[66] L. Mich, M. Franch, G. Cilione, “The 2QCV3Q Quality Model for the Analysiy of Web site Requirements”, J. Web Eng., Vol.2, Issue.1-2, pp.115-127, 2003.
[67] L. Mich, M. Franch, L. Gaio, “Evaluating and designing web site quality”, IEEE MultiMedia, Vol.10, Issue.1, pp.34-43, 2003.
[68] L. Mich, M. Franch, “Un approccio multi-step per la valutazione dell’usabilità del sito web di una destinazione turistica”, Atti di: SIMKTG Il marketing dei talenti: Marketing e Tecnologia, pp.24-25, 2007.
[69] J.M. Moreno, J.M., Del Castillo, C. Porcel, E. Herrera-Viedma, “A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach”, Soft Computing, Vol.14, Issue.8, pp.887-897, 2010.
[70] A.M. Morrison, J.S. Taylor, A. Douglas, “Website evaluation in tourism and hospitality: the art is not yet stated”, Journal of Travel and Tourism Marketing, Vol.17, Issue.2-3, pp.233-251, 2005.
[71] B.L. Neiger, R. Thackeray, S.A. Van Wagenen, C.L. Hanson, J.H. West, M.D. Barnes, M.C. Fagen, “Use of social media in health promotion: purposes, key performance indicators, and evaluation metrics”, Health promotion practice, Vol.13, Issue.2, pp.159-164, 2012.
[72] L. Olsina, G. Rossi, “A quantitative method for quality evaluation of web sites and applications”, IEEE multimedia, Vol.9, Issue.4, pp.20-29, 2002.
[73] L. Olsina, G. Covella, G., Rossi, “Web quality”, Web Engineering, pp.109-142, 2006.
[74] T. Orehovački, A. Granić, D. Kermek, “Evaluating the perceived and estimated quality in use of Web 2.0 applications”, Journal of Systems and Software, Vol.86, Issue.12, pp.3039-3059, 2013.
[75] X. Papadomichelaki, G. Mentzas, “A multiple-item scale for assessing e-government service quality”, In International Conference on Electronic Government, pp.163-175, Springer, Berlin, Heidelberg, August 2009.
[76] A. Parasuraman, V.A. Zeithaml, A. Malhotra, “ES-QUAL: A multiple-item scale for assessing electronic service quality”, Journal of service research, Vol.7, Issue.3, pp.213-233, 2005.
[77] A. Perallos, “Metodología Ágil y Adaptable al Contexto para la Evaluación Integral y Sistemática de la Calidad de Sitios web (Agile and context-adaptable methodology for the integral and systematic evaluation of websites’ quality)”, Ph.D. Diss,.Universidad de Deusto. Facultad de Ingeniería-ESIDE, 2006 http://dialnet.unirioja.es/servlet/tesis?codigo=19163 (2006) Accessed 15 June 2017
[78] F.R. Pérez-López, “An evaluation of the contents and quality of menopause information on the World Wide Web”, Maturitas, Vol.49, Issue.4, pp.276-282, 2004.
[79] V. Petricek, T. Escher, I.J. Cox, H. Margetts, “The web structure of e-government-developing a methodology for quantitative evaluation”, In Proceedings of the 15th international conference on World Wide Web, pp.669-678, ACM, May 2006.
[80] H. Petrie, O. Kheir, “The relationship between accessibility and usability of websites”, In Proceedings of the SIGCHI conference on Human factors in computing systems, pp.397-406, ACM, April 2007.
[81] L. Pranić, D. Garbin Praničević, J. Arnerić, “Hotel website performance: evidence from a transition country”, Tourism and hospitality management, Vol.20, Issue.1, pp.45-60, 2014.
[82] A. Rocha, “Framework for a global quality evaluation of a website”, Online Information Review, Vol.36, Issue.3, pp.374-382, 2012.
[83] A.M. Santos, “Theoretical-Methodological proposal to evaluate the quality of educational websites to support education”, In Proceedings of the 3rd International Conference on Technological Ecosystems for Enhancing Multiculturality, pp.397-401. ACM, October 2015.
[84] K. Schäfer, T.F. Kummer, “Determining the performance of website-based relationship marketing”, Expert Systems with Applications, Vol.40, Issue.18, pp.7571-7578, 2013.
[85] C. Shchiglik, S.J. Barnes, “Evaluating website quality in the airline industry”, Journal of Computer Information Systems, Vol.44, Issue.3, pp.17-25, 2014.
[86] K. Silius, M. Kailanto, A.M. Tervakari, “Evaluating the quality of social media in an educational context”, In Global Engineering Education Conference (EDUCON) 2011 IEEE, pp.505-510, IEEE, April 2011.
[87] A. Sivaji, A. Abdullah, A.G. Downe, “Usability testing methodology: Effectiveness of heuristic evaluation in E-government website development”, In Modelling Symposium (AMS), 2011 Fifth Asia, pp.68-72, IEEE, May 2011.
[88] S. Srivastava, S. Chawla, “Multifaceted classification of websites for goal oriented requirement engineering”, In International Conference on Contemporary Computing, pp.479-485, Springer, Berlin, Heidelberg, August 2010.
[89] A. Stefani, M. Xenos, “E-commerce system quality assessment using a model based on ISO 9126 and Belief Networks”, Software Quality Journal, Vol.16, Issue.1, pp.107-129, 2008.
[90] C.C. Sun, G.T. Lin, “Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites”, Expert Systems with Applications, Vol.36, Issue.9, pp.11764-11771, 2009.
[91] D.D.J. Suwawi, E. Darwiyanto, M. Rochmani, “Evaluation of academic website using ISO/IEC 9126”, In Information and Communication Technology (ICoICT), 2015 3rd International Conference on, pp.222-227, IEEE, May 2015.
[92] M. Tate, J. Evermann, B. Hope, S. Barnes, “Perceived service quality in a university web portal: revising the e-qual instrument”, In System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on, pp.147b-156b, IEEE, January 2007.
[93] M.C.S. Torrente, A.B.M. Prieto, D.A. GutiéRrez, M.E.A. De Sagastegui, “Sirius: A heuristic-based framework for measuring web usability adapted to the type of website”, Journal of Systems and Software, Vol.86, Issue.3, pp.649-663, 2013.
[94] L. Triacca, D. Bolchini, L. Botturi, A. Inversini, “Mile: Systematic usability evaluation for e-Learning web applications”, In Proceedings of EDMEDIA 2004, Lugano, Switzerland, Vol.1, pp 4398-4405, 2004.
[95] L. Vaughan, “New measurements for search engine evaluation proposed and tested”, Information Processing and Management, Vol.40,Issue.4, pp.677-691, 2004.
[96] P. Verdegem, G. Verleye, “User-centered E-Government in practice:: A comprehensive model for measuring user satisfaction”, Government information quarterly, Vol.26, Issue.3, pp 487-497, 2009.
[97] T. Watanabe, “Experimental evaluation of usability and accessibility of heading elements”, Disability and Rehabilitation: Assistive Technology, Vol.4,Issue.4, pp.236-247, 2009.
[98] D. Wenham, P. Zaphiris, “User interface evaluation methods for internet banking web sites: a review, evaluation and case study”, Human-computer interaction, theory and practice, pp.721-725, 2003.
[99] B. Yen, P.J.H. Hu, M. Wang, “Toward an analytical approach for effective Web site design: A framework for modeling, evaluation and enhancement”, Electronic Commerce Research and Applications, Vol.6, Issue.2, pp.159-170, 2007.
[100] B. Yoo, N. Donthu, “Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL)”, Quarterly journal of electronic commerce, Vol.2, Issue.1, pp.31-45, 2001.
[101] C. Zafiropoulos, V. Vrana, “A framework for the evaluation of hotel websites: The case of Greece”, Information Technology and Tourism, Vol.8, Issue.3-1, pp.239-254, 2006.
[102] B. Zhao, Y. Cheng, “Research on B2C e-commerce website service quality evaluation based on analytic hierarchy process”, In Information Science and Technology (ICIST), 4th IEEE International Conference on 2014, pp.364-367, IEEE, Apr 26 2014.
[103] B. Zhao, Y. Zhu, “Formalizing and validating the web quality model for web source quality evaluation”, Expert Systems with Applications, Vol.41, Issue.7, pp.3306-3312, 2014.
[104] Y. Zhu, “Integrating external data from web sources into a data warehouse for OLAP and decision making”, Shaker Verlag, 2004.
[105] M. Zviran, C. Glezer, I. Avni, “User satisfaction from commercial web sites: The effect of design and use”, Information and management, Vol.43, Issue.2, pp.157-178, 2006.

S. Kaur, S.K. Gupta, "A Systematic Review of Realistic Methods and Approaches for Evaluation of Website," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.39-52, 2018.

In the world of cryptography there are a lot of techniques and their simultaneous operations, which are used for making our data transmission better, secure and fast. Today to get more and more data transmission capabilities, people tend to compromise security of their data due to non availability of better cryptography techniques to suit different needs of their data transmission. Keeping this requirement of enhanced security in mind, some new techniques are making their way in cryptography, which are reliable, fast and give better data security for transmission of different kind of data (i.e. Text, Images, Videos etc.). In this paper, authors are proposing a cryptography method for enhanced encryption and decryption with help of LFSR (linear feedback shift register), which can reliably give much desired security with more speed. In this paper the method is used only for text, it could be further modified for 2D as well as 3D images.

LFSR (Linear Feedback Shift Register); Encryption; Decryption; Cipher text; Block cipher

[1]. A. Joseph Amalraj, Dr.J. John Raybin Jose, “A Survey Paper of Cryptography Techniques” Issue.8, August 2016.
[2]. Rohit S and Vinay G “A Novel to Stage Binary image Security system Using (2, 2) Visual Cryptography Scheme”, ISSN: 2250-3005.
[3]. Prof. Mukund R.Joshi , Renuka Avinash Karkade, “Network Security With Cryptography” , Issue.1 , January 2015
[4]. A. Balasubramani, Ch.D.V Subba Rao, “Image Security Implementing Steganography and Cryptographic Methods”, International Journal of Computer Science and Engineering, Volume.6, Issue.1, January 2018.
[5]. Jawahar Thakur, Nagesh Kumar, “Des, Aes And Blowfish: Symmetric Key Cryptography Algorithms Simulation Based Performance Analysis” International Journal Of Emerging Technology And Advanced Engineering, Volume 1, Issue 1, November 2011.
[6]. Sonia Rani, Harpreet Kaur, “Technical Review on Symmetric and Asymmetric Cryptography Algorithms.” Vol.8, May 2017.
[7]. Mini Malhotra, Aman Singh, “Study of various Cryptographic Algorithms”, Vol.1, Issue 3, November 2013.
[8]. Wang, Suli; Liu, Ganlai, “File encryption and decryption system based on RSA algorithm”, Computational and Information Sciences (ICCIS), pp. 797 – 800, 2011.
[9]. Suresh, V., and C. Saraswathy. "Separable reversible data hiding using Rc4 algorithm", 2013 International Conference on Pattern Recognition Informatics and Mobile Engineering, 2013.
[10]. R. L. Rivest, A. Shamir, and L. Adleman “A Method for Obtaining Digital Signatures and Public- Key Cryptosystems.” Communications of the ACM, vol. 26, no. 1, pp. 96–99, 1983
[11]. Faiqa Maqsood, Muhammad Ahmed, Muhammad Mumtaz, Munam Ali. "Cryptography: A Comparative Analysis for Modern Techniques", International Journal of Advanced Computer Science and Applications, 2017
[12]. M. E. Student, “Algorithms for Secure Cloud,” vol. 3, no. 6, pp. 1–9, 2014
[13]. P. Nalwaya, V. P. Saxena, and P. Nalwaya, “A cryptographic approach based on integrating running key in feedback mode of elgamal system,” Proc. - 2014 6th Int. Conf. Comput. Intell. Commun. Networks, CICN 2014, pp. 719–724, 2014.
[14]. X. Li, X. Shen, and H. Chen, “ElGamal digital signature algorithm of adding a random number,” J. Networks, vol. 6, no. 5, pp. 774–782, 2011
[15]. M. S. Anoop, “Elliptic Curve Cryptography,” Infosecwriters, pp. 1–11, 2015
[16]. Alok Kumar Kasgar, Mukesh Kumar Dhariwal,”A Review Paper Of Message Digest 5(MD5)”, Volume 1, Issue 4, December 2013
[17]. Chaitya B. Shah, Drashti R. Panchal, “Secured Hash Algorithm-1: Review Paper”, Volume 2, Issue X, October 2014.
[18]. Tang Songsheng, Ma Xianzhen,”Research of typical block cipher algorithm”, Computer, Mechatronics, Control and Electronic Engineering (CMCE), pp. 319 – 321, 2010.
[19]. Wenxue Tan ; Wang Xiping ; Jinju Xi ; Meisen Pan , “A mechanism of quantitating the security strength of RSA key”, Electronic Commerce and Security (ISECS), pp. 357 – 361, 2010.
[20]. Aarti Devi, Ankush Sharma, Anamika Rangra, “Performance Analysis Of Symmetric Key Algorithms: Des, Aes And Blowfish For Image Encryption And Decryption” International Journal Of Engineering And Computer Science Volume 4 Issue 6 June 2015
[21]. G. Ramesh1 Dr. R. Umarani, “Performance Analysis of Most Common Symmetrical Encryption Algorithms” International Journal of Power Control Signal and Computation (IJPCSC) Vol 3. No 1. Jan-Mar 2012.
[22]. Jitendra Singh Laser, Viny Jain, “A Comparative Survey Of Various Cryptographic Techniques” International Research Journal Of Engineering And Technology (IRJET), Volume: 03 Issue: 03 | Mar-2016
[23]. Md. Alam Hossain, Md. Biddut Hossain, Md. Shafin Uddin, Shariar Md. Imtiaz, “Performance Analysis Of Different Cryptography Algorithms”, International Journal Of Advanced Research In Computer Science And Software Engineering Volume 6, Issue 3, March 2016.
[24]. Dr. D. Vimal Kumar, Mrs. J. Divya Jose, “Over View of Cryptographic Algorithms for Information Security” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 5, May 2016.
[25]. Siddesh Gaonkar, “Design Of 8bit, 16bit and 32bit LFSR For PN Sequence Generation Using VHDL”, Issue 31(September, 2015)
[26]. Sumitra, “Comparative Analysis of AES and DES Security Algorithms”, Volume 3, Issue 1, January 2013.

Sakshi Dubey, Darpan Anand, Jayash Sharma, "LFSR Based Block Cipher Technique for Text," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.53-60, 2018.

Health Information Systems (HIS) are gaining wide popularity in the recent days demanding digitization for easy storage of medical data in the secure environment that protects the patient privacy. As the medical data has been transferred on unsecure network authentication and integrity of medical data is a prime concern to attract the researchers. These objectives are gained by watermarking. Medical Image Watermarking (MIW) has recently evolved to solve the issues like storage, security and privacy related with HIS. This paper proposes an effective approach based on Contourlet Transform (CNT) for data hiding of medical records Electronic Patient Record (EPR), biographic signal like EEG or ECG and hospital logo. The contourlet transform is exploited in this research for its multiscale properties and predictive coding like ADM is proposed for compression of ECG signal. The medical text data is further encrypted using RSA public key algorithm. The efficacy of the developed approach is examined using various performance parameters ( Peak Signal to Noise Ratio, PSNR and Structural Similarity Measure , SSIM).The Quality of extracted logo and recovered ECG is evaluated using Normalized Correlation Coefficient (NC) .The similarity of original and extracted EPR examined using Bit Error Rate (BER).Imperceptibility of algorithm is preserved as PSNR values are above 45 DB And MSSIM is greater than 0.98.The comparative analysis with other frequency domain techniques is presented which claims and confirms the superiority of proposed contourlet approach for efficient multiple watermarking in HIS.

Contourlet Transform; Watermarking ; Health Information system

[1] Lei, Baiying, et al. "Reversible watermarking scheme for medical image based on differential evolution." Expert Systems with Applications 41.7 (2014): 3178-3188.
[2] Petitcolas, Fabien AP, Ross J. Anderson, and Markus G. Kuhn. "Information hiding-a survey." Proceedings of the IEEE 87.7 (1999): 1062-1078.
[3] Giakoumaki, Aggeliki, Sotiris Pavlopoulos, and Dimitris Koutsouris. "Multiple image watermarking applied to health information management." IEEE Transactions on Information Technology in Biomedicine 10.4 (2006): 722-732.
[4] Nergui, Myagmarbayar, et al. "Reliable and robust transmission and storage techniques for medical images with patient information." Journal of Medical systems 34.6 (2010): 1129-1139.
[5] Do, Minh N., and Martin Vetterli. "Contourlets: a directional multiresolution image representation." Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 1. IEEE, 2002.
[6] Xiao, Shangqin, et al. "Adaptive image watermarking algorithm in contourlet domain." 2007 Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST 2007). IEEE, 2007
[7] Zain, Jasni M., and Abdul RM Fauzi. "Medical image watermarking with tamper detection and recovery." Engineering in Medicine and Biology Society, 2006. EMBS`06. 28th Annual International Conference of the IEEE. IEEE, 2006.
[8] U. Rajendra Acharya, D. Anand, P. Subbanna Bhat, U.C. Niranjan, “Compact storage of medical image with patient information”, IEEE Transaction on Information Technology in Biomedicine, vol. 5, No. 4, pp. 320-323, Dec. 2001.
[9] Deepthi Anand, U. C. Niranjan, “Watermarking medical images with patient information”, Proceeding of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, No. 2, pp. 703-706, 1998.
[10] Manasrah, Tuqa, and Ali Al-Haj. "Management of medical images using wavelets-based multi-watermarking algorithm." Innovations in Information Technology, 2008. IIT 2008. International Conference on. IEEE, 2008.
[11] Das, Sudeb, and Malay Kumar Kundu. "Hybrid contourlet-DCT based robust image watermarking technique applied to medical data management." International Conference on Pattern Recognition and Machine Intelligence. Springer Berlin Heidelberg, 2011.
[12] Do, Minh N., and Martin Vetterli. "Contourlets: a directional multiresolution image representation." Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 1. IEEE, 2002.
[13] Xiao, Shangqin, et al. "Adaptive image watermarking algorithm in contourlet domain." 2007 Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST 2007). IEEE, 2007.
[14] Po, DD-Y., and Minh N. Do. "Directional multiscale modeling of images using the contourlet transform." Statistical Signal Processing, 2003 IEEE Workshop on. IEEE, 2003.
[15] Turuk, Mousami, and Ashwin Dhande. "Interleaving Scheme for Medical Image Authentication." Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2016. 669-681.
[16] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on, 13(4), 600-612.
[17] Das, Sudeb, and Malay Kumar Kundu. "Effective management of medical information through a novel blind watermarking technique." Journal of medical systems 36.5 (2012): 3339-3351.
[18] Rahimi, Farhad, and Hossein Rabani. "A visually imperceptible and robust image watermarking scheme in contourlet domain." IEEE 10th International Conference On Signal Processing Proceedings. IEEE, 2010.
[19] Prachi Joshi, Mousami Munot, Parag Kulkarni, Madhuri Joshi, "Efficient Karyotyping of Metaphase Chromosomes Using Incremental Learning", The IET Journal of Science Measurement and Technology, Vol.7, Issue 5, September 2013, pp. 287-295
[20] Mousami Munot, Prachi Joshi, Madhuri Joshi, Parag Kulkarni, “An Incremental Approach for Efficient Karyotyping Systems”, Journal of Medical Imaging and Health Informatics, Med. Imaging Health, Inf. 6, February 2016, pp. 221-225
[21] Mousami Munot, Jayanta Mukhopadhyay, Madhuri Joshi, "A Novel Approach for Efficient Extrication of Overlapping Chromosomes in Automated Karyotyping", Springer Journal of Medical and Biological Engineering and Computing, August 2013, pp. 1-14.

Mousami V. Munot, Mousami P. Turuk, "An Efficient Contourlet Based Multiple Watermarking Scheme For Health Information System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.61-65, 2018.

Reverse biorthogonal wavelets are highly regular wavelets with compact support and symmetric filters and they have explicit construction. This paper explores the performance of the reverse biorthogonal spline wavelets in denoising images differentiated by the detail-contents in the images. The transform used in the study is the Undecimated Wavelet Transform which is a translation-invariant transform. The selected images are corrupted by adding white Gaussian noise to produce noisy test images. The study shows that the denoising effect depends on the amount of details in the image. It is also seen that reverse biorthogonal spline wavelets are highly effective in denoising dense-detail images like fingerprints. These wavelets also give good denoising for low-detail images like human face. The best wavelet in the family for each of these purposes has been sorted out. Rbio 3.1 is found to be an odd member of the family. These wavelets are found to give poor results in denoising medium-detail images. The study finds application in Forensic science and in restoration of facial images and when the images encountered in such applications contain several types of noise distributions simultaneously.

Reverse biorthogonal, Spline, Undecimated Transform, Image detail

[1] I. Daubechies,“Orthonormal Bases of Ccompactly Supported Wavelets”, Communications on Pure and Applied Mathematics. XLI, pp. 909-996, 1988
[2] G. Strang, T. Nguyen, “Wavelets and filter banks”, Wellesley-Cambridge Press, Wellesley, pp.xi-71,1996.
[3] Y.S. Bahendwar, G.R. Sinha, “Mathematical Methods and Systems in Science and Engineering”, WSEAS Press, Spain, pp.158 – 162, 2015.
[4] J. Kaur, R. Kaur, “Biomedical Images Denoising Using Symlet Wavelet with Wiener Filter”. International Journal of Engineering Research and Applications (IJERA), Vol. 3, No. 3, pp. 548-550, 2013
[5] L. Renjini, R.L. Jyothi, “Wavelet Based Image Analysis: a Comprehensive Survey”, International Journal of Computer Trends and Technology (IJCTT), Vol. 21, No.3, pp.134-140, 2015
[6] M. Unser, “Ten Good Reasons for Using Spline Wavelets.” Proceedings of SPIE, Wavelets Applications in Signal and Image Processing V, 3169, pp. 422-431, 1997.
[7] P.P. Vaidyanathan, I. Djokovic, “An Introduction to Wavelet Transforms”, California Institute of Technology, Pasadena, pp. 1-116, 1994.
[8] A. Cohen, I. Daubechies, J.C. Feauveau, “Biorthogonal Bases of Compactly Supported Wavelets”, Communications on Puree and Applied Mathematics, XLV, pp. 485-560, 1992
[9] M. Vetterli, C. Herley, “Wavelets and Filter Banks: Ttheory and Design”, IEEE Transactions on Signal Processing, Vol. 40, No. 9, pp. 2207-2232, 1992.
[10] L. Passrija, A.S. Virk, M. Kaur, “Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains”, International Journal of Computers & Technology, Vol.3, No.1, pp. 162-166, 2012.
[11] P. Rakheja, R. Vig, “Image Denoising Using Combination of Median Filtering and Wavelet Transform”, International Journal of Computer Applications, Vol. 141, No.9, pp. 31-35, 2016
[12] T.N. Tilak, S. Krishnakumar, “Effectiveness of Symlets in De-noising Fingerprint Images”, International Journal of Computer Science and Engineering, Vol. 3, No.12, pp. 29-34, 2015.
[13] R.R. Coifman, D.L. Donoho, “Translation-Invariant Denoising”, Stanford University, California, pp. 1-26, 1995.
[14] J.L. Starck, J. Fadili, F. Murtagh, “The Undecimated Wavelet Decomposition and Its Reconstruction”, IEEE Transactions on Image Processing, Vol.16, No.2, pp. 297-309, 2007.
[15] I.W. Selesnick, “The Double-Density Dual-Tree DWT”, IEEE Transactions on Signal Processing, Vol. 52, No.5, pp. 1304-1314, 2004.
[16] G.P. Nason, B.W. Silverman, “Wavelets and statistics”, Springer-Verlag, New York, pp. 281–299, 1995.
[17] S.G. Chang, B. Yu, M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Transactions on Image Processing, Vol.9, No.9, pp. 1532-1546, 2000.
[18] W. Qian, “Research on Image Denoising With an Improved Wavelet Threshold Algorithm”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.8, No.9, pp. 257-266, 2015.
[19] L. Gabralla, H. Mahersia, M. Zaroug, “Denoising CT Images Using Wavelet Transform”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol.6, No.5, pp.125-129, 2015.
[20] L. Yuan, J. Wu, S. Li, “Improved Wavelet Threshold for Gray Scale Image Denoising”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7, No.3, pp. 45-52, 2014.
[21] Anutam, Rajini, “Comparative Analysis of Filters and Wavelet Based Thresholding Methods for Image Denoising”, Computer Science & Information Technology (CS & IT). pp. 137–148, 2014.
[22] K.V. Thakur, P.G. Ambhore, A.M. Sapkal, “Medical image denoising using rotated wavelet filter and bilateral filter”, IJAIS Proceedings of 7th International Conference on Communication Computing and Virtualization ICCCV, India, pp. 14-18, 2016.
[23] H. Jagadish, J. Prakash, “A New Approach for Denoising Remotely Sensed Images Using DWT Based Homomorphic Filtering Techniques”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol.3, No.3, pp. 90-96, 2014.
[24] S. Bhatnagar, R.C. Jain, “Application of Discrete Multi-Wavelet Transform in Denoising of Mammographic Images”, Indian Journal of Science and Technology, Vol.9, No.48, pp. 1-9, 2016.

T.N. Tilak, S. Krishnakumar, "Reverse Biorthogonal Spline Wavelets in Undecimated Transform for Image Denoising," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.66-72, 2018.

Uncovering causal interactions in data is a most important objective of data analytics. Causal relationships are usually exposed with intended research, e.g. randomised controlled examinations, which however are costly or insufficient to be performed in several cases. In this research paper aims to present a new Casual Decision tree structure of first-order logical casual decision tree called FOL-CDT structure. The proposed method follows a well-recognized pruning approach in causal deduction framework and makes use of a standard arithmetical test to create the causal relationship connecting a analyst variable and the result variable. At the similar instance, by taking the advantages of standard decision trees, a FOL-CDT presents a compact graphical illustration of the causal relationships with pruning method, and building of a FOL-CDT is quick as a effect of the divide and conquer strategy in use, making FOL-CDTs realistic for representing and resulting causal signals in large data sets.

Data Mining, First order Logical, Decision Tree, Pruning, Classification

[1] E. A. Stuart, “Matching methods for causal inference: A review and a look forward,” Statistical Sci., Vol. 25, No. 1, pp. 1–21, 2010
[2] N. Cartwright, “What are randomised controlled trials good for?” Philosophical Studies, vol. 147, no. 1, pp. 59–70, 2009.
[3] P. R. Rosenbaum, Design of Observational Studies. Berlin, Germany: Springer, 2010.
[4] R. P. Rosenbaum and B. D. Rubin, “Reducing bias in observational studies using subclassification on the propensity score,” J. Amer. Statistical Assoc., Vol. 79, No. 387, pp. 516–524, 1984.
[5] N. Mantel and W. Haenszel, “Statistical aspects of the analysis of data from retrospective studies of disease,” J. Nat. Cancer Inst., Vol. 22, No. 4, pp. 719–748, 1959.
[6] C. F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, “Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation,” J. Mach. Learn. Res., Vol. 11, pp. 171–234, 2010.
[7] J. Foster, J. Taylor, and S. Ruberg, “Subgroup identification from randomized clinical trial data,” Statistics Med., Vol. 30, No. 24, pp. 2867–2880, 2011
[8] R. P. Rosenbaum and B. D. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika, Vol. 70, No. 1, pp. 41–55, 1983.
[9] D. B. Rubin, “Causal inference using potential outcomes: Design, modeling, decision,” J. Amer. Statistical Assoc., Vol. 100, No. 469, pp. 322–331, 2005.
[10] S. Greenland and B. Brumback, “An overview of relations among causal modelling methods,” Int. J. Epidemiology, Vol. 31, pp. 1030–1037, 2002
[11] N. Mantel and W. Haenszel, “Statistical aspects of the analysis of data from retrospective studies of disease,” J. Nat. Cancer Inst., Vol. 22, No. 4, pp. 719–748, 1959.
[12] B. K. Lee, J. Lessler, and E. A. Stuart, “Improving propensity score weighting using machine learning,” Statistics Med., Vol. 29, No. 3, pp. 337–46, 2010.
[13] D. Chickering, D. Heckerman, and C. Meek, “Large-sample learning of Bayesian networks is NP-hard,” J. Mach. Learn. Res., Vol. 5, pp. 1287–1330, 2004.
[14] M. K. P. B€uehlmann and M. Maathuis, “Variable selection for highdimensional linear models: Partially faithful distributions and the PC-simple algorithm,” Biometrika, Vol. 97, pp. 261–278, 2010.
[15] Z. Jin, J. Li, L. Liu, T. D. Le, B. Sun, and R. Wang, “Discovery of causal rules using partial association,” in Proc. IEEE 12th Int. Conf. Data Mining, Dec. 2012, pp. 309–318.
[16] S. Athey and G. Imbens, “Recursive partitioning for heterogeneous causal effects,” in Proc.Natl Academy Sci., Vol.113, No. 27, pp. 7353–7360, 2016.
[17] P.T.Kavitha, Dr.T.Sasipraba , Knowledge Driven HealthCare Decision Support System using Distributed Data Mining, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 3 No. 3 Jun-Jul 2012.
[18] K. Bache and M. Lichman, “ Evolutionary Learning of concepts” , Journal of Computers and Communications,Vol.2 No. 8, June 27, 2014.

S.Preethi and C.Rathika, "An Efficient First Order Logical Casual Decision Tree in High Dimensional Dataset," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.73-78, 2018.

the wireless sensor network is a largely growing research field in the recent world. This network has a vast area of implementation now and is gradually increasing day by day. The main use of the wireless sensor network technology is in the environment system, the object tracking system, sensing data from the location where human can’t reach etc. A sensor network is a combination of low-cost sensor devices with a limited range of data transmission and battery power. A sensor node is responsible to collect sensed data and send those data to the base station and the base station processes those data. Normally a sensor network requires a fixed amount of energy to collect a bit of data. The battery use of the sensor nodes depends on the data collected and transmitted to the base station and also the data transmission range. So, it is very difficult for a sensor network to send data directly to the base station as some sensor nodes may be placed at a long distance from the base station. Then to send data to the base station will finish all its power and the node will die soon. This is the reason the sensor nodes use the clustering technique where the nodes send data to its cluster head and the cluster head forwards data as a tree structure to the base station. This assures a better lifetime of the sensor devices. Some common well known lifetime optimization algorithms are- LEACH, LEACH-C, PEGASIS, GROUP, Ant Colony etc [1]. In this paper, we have proposed an Updated Particle Gaggle Optimization based Routing protocol (UPGOR) where energy efficiency of the sensor nodes is the major focus for the routing protocol and finding an optimized path for data forwarding to the base station and data processing through the base station. The UPGOR algorithm takes the energy as the fitness and finds an optimized path among several available paths to route data. At the end of this paper, we compared our simulated experimental results with other relevant algorithms which show a better result obtained by the proposed UPGOR algorithm. The simulation is done in the NS2 simulation in Ubuntu environment and the simulated data then placed to generate the tables and charts.

Particle Gaggle Optimization, Routing, Lifetime, Wireless Sensor Network, Energy Efficiency

[1] N. Jamal, “Routing techniques in wireless sensor
networks a survey,” IEEE Wireless communication, Vol
11(6), 2012
[2] S. K. Singh , M. P. Singh , and D. K. Singh, “Energy Efficient Homogenous Clustering Algorithm for Wireless Sensor Networks”, International Journal of Wireless & Mobile Networks ( IJWMN ), Vol.2, No.3, August 2010.
[3]S. Okdem and D. Karaboga, Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router
Chip In Sensors”, 2009.
[4] Z. Luo, L. Lu, J. Xie, J. He, “An ant colony
optimization-based trustful routing algorithm for wireless sensor networks,” in 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), pp. 1128–1131, IEEE, dec 2015
[5] R. Arya, S.C. Sharma, “Analysis and
optimization of energy of sensor node using ACO in
wireless sensor network,” Elsevier B.V, Vol. 45, pp. 681-
686, 2015
[6] W. B. Heinzelman, A. P. Chandrakasan, H. Balakrishnan,“An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Trans. Wirel. Commun. 2002, 1, 660–670.
[7] S. Lindsey, C. S. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems”, In Proceedings of the Aerospace Conference, Big Sky, MT, March, 2002; pp. 1125–1130.
[8] Y. Zhang, L. D. Kuhn, and M. P. J. Fromherz, "Improvements on Ant Routing for Sensor Networks", M. Dorigo et al. (Eds.): ANTS 2004, Springer-Verlag Berlin Heidelberg 2004, vol. LNCS 3172, pp. 154-165, 2004
[9] A. Chakraborthy, S. K. Mitra, M. K. Niskar, “A Genetic Algorithm Inspired routing Protocol for Wireless
sensor Network”, in International Journal of Computational Intelligence Theory and practice, Vol 6 No.1 June 2011
[10] S. Mccanne, S. Floyd, and K. Fall, "Network Simulator 2 (NS-2) version 2.35," in http://www-nrg.ee.lbl.gov/ns/,
http://www.isi.edu/nsnam/ns (1998)
[11] P. Rani, A. Gupta, Y. Singh, “Multiple Event Detection In Wireless Sensor Networks Using Compressed Sensing: Health Monitoring Perspective ”, in International Journal of Computer Science and Engineering, vol.6 , Issue.1 , pp.36-41, Jan-2018

M.A. Mukib, L. B. Mahabub, M. A. Rahman, "An Updated Particle Gaggle Based Optimization Routing Algorithm for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.79-83, 2018.