Volume-5 , Issue-12 , Dec - 2017 Go Back

In today’s digital world remote senses daily generate large amount of real time data know as Big-Data, wherever insight data encompasses a potential significance if collected and aggregative effectively. There is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a main computational disputes, such as to analyze, aggregate, & store, where data are remotely gathered. Keeping in view the above mentioned factors, there`s a requirement for planning a system design that welcomes each real-time, additionally as offline processing. In this paper we propose efficient and scalable solution to analyze pent bytes of data across an extremely wide increasing wealth of weather variables. In this research we are working on data analysis using Apache Hadoop and Java . Extensive experiments are carried out to find out the best tools among Distributed computing using Pig and Hive Queries. The proposed architecture has the potential of dividing, load balancing, & parallel processing of only utile data. Thus, it results in effectively analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Fixed width clustering algorithm is used to improve the accuracy of results.

Big Data, data analysis decision unit (DADU), data processing unit (DPU), land and sea area, offline, real-time, remote senses, remote sensing Big Data acquisition unit (RSDU)

[1] M. M. U. Rathore, A. Paul, A. Ahmad, B. W. Chen, B. Huang and W. Ji, "Real-Time Big Data Analytical Architecture for Remote Sensing Application," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 10, pp. 4610-4621, Oct. 2015.
[2] H. Herodotou et al., “Starfish: A self-tuning system for Big Data analytics,” in Proc. 5th Int. Conf. Innovative Data Syst. Res. (CIDR), 2011, pp. 261–272.
[3] K. Michael and K. W. Miller, “Big Data: New opportunities and new challenges [guest editors’ introduction],” IEEE Comput., vol. 46, no. 6, pp. 22–24, Jun. 2013.
[4] C. Eaton, D. Deroos, T. Deutsch, G. Lapis, and P. C. Zikopoulos, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. New York, NY, USA: Mc Graw-Hill, 2012.
[5] R. D. Schneider, Hadoop for Dummies Special Edition. Hoboken, NJ, USA: Wiley, 2012.
[6] R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 2nd ed. New York, NY, USA: Academic Press, 1997.
[7] D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ, USA: Wiley, 2003.
[8] C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Norwell, MA, USA: Kluwer, 2003.
[9] J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction. New York, NY, USA: Springer, 2006.
[10] J. Shi, J. Wu, A. Paul, L. Jiao, and M. Gong, “Change detection in synthetic aperture radar image based on fuzzy active contour models and genetic algorithms,” Math. Prob. Eng., vol. 2014, 15 pp., Apr. 2014.
[11] A. Paul, J. Wu, J.-F. Yang, and J. Jeong, “Gradient-based edge detection for motion estimation in H.264/AVC,” IET Image Process., vol. 5, no. 4, pp. 323–327, Jun. 2011.
[12] A. Paul, K. Bharanitharan, and J.-F.Wang, “Region similarity based edge detection for motion estimation in H.264/AVC,” IEICE Electron. Express, vol. 7, no. 2, pp. 47–52, Jan. 2010.
[13] A.-C. Tsai, A. Paul, J.-C. Wang, and J.-F. Wang, “Intensity gradient technique for efficient intra prediction in H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 5, pp. 694–698, May 2008.
[14] A. Labrinidis and H. V. Jagadish, “Challenges and opportunities with Big Data,” in Proc. 38th Int. Conf. Very Large Data Bases Endowment, Istanbul, Turkey, Aug. 27–31, 2012, vol. 5, no. 12, pp. 2032–2033.
[15] [20] P. Chandarana and M. Vijayalakshmi, “Big Data analytics frameworks,” in Proc. Int. Conf. Circuits Syst. Commun. Inf. Technol. Appl. (CSCITA), 2014, pp. 430–434.
[16] Adamu Galadima, D. Sacc, and J. D. Ullman, Big Data: A research agenda," in Proc. Int. Database Eng. Appl. Symp. (IDEAS13), Barcelona, Spain, Oct. 0911, 2013.
[17] C.-I. Chang, “Hyperspectral Imaging: Techniques for Spectral Detection and Classi_cation", Norwell, MA, USA: Kluwer, 2003.

A. Patil, A. Palve, "A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.67-72, 2017.

Now a day the database is increases into hues size of database in multimedia and internet technology, so data science and content Based Image Retrieval (CBIR) system is an important research area since last few years. There are so many models of CBIR have been proposed by various author to retrieve images from huge database. In this work, we present a CBIR system using HU’s seven Invariant moment feature and measures the performance of system in MATLAB. The similarity between query image and database image is measure by Euclidian distance method and the efficiency of system is measure by calculating the precision and recall. All the experimental results are performed on five different standard datasets on 450 images.

Invariant moment, Data science, CBIR, Euclidian distance

[1] R. Gonzales, R. E. Woods, “Digital Image Processing,” 2nd Ed., New Jersey Prentice Hall, 2002.
[2] Gevers and A. Smeulders, “Pictoseek: Combining color and shape invariant features for image retrieval,” IEEE Trans. Image Processing, vol. 9, no. 1, pp.102– 119, Nov. 2000
[3] A. Ouyang, and Y. Tan, “A novel multi-scale spatial-color descriptor for content-based image retrieval,” Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, Mexico, August 2002, vol. 3, pp. 1204-1209.
[4] H. Yu, M. Li, H. Zhang, and J. Feng, “Color texture moments for content-based image retrieval,” Proceedings of the International Conference on Image Processing, Rochester, New York, USA S, ptember 22-25, 2002, vol. 3, pp. 929-932.
[5] T. Gevers, and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,” IEEE Transactions on Multimedia, vol. 5, no. 2, pp. 237-243, Sep. 2003.
[6] H. Guan, and S. Wada, “Flexible color texture retrieval method using multi-resolution mosaic for image classification,” Proceedings of the 6th International Conference on Signal Processing, vol. 1, pp. 612-615, Feb. 2002.
[7] H Moghaddam, T. Khajoie, and A. Rouhi, “A new algorithm for image indexing and retrieval using wavelet correlogram,” Proceedings of the International Conference on Image Processing, vol. 3, pp. 497-500, May 2003.
[8] H.B. Kekre, “Survey of CBIR Techniques and Semantics”, International Journal of Engineering Science and Technology (IJEST), ISSN: 0975-5462 Vol. 3 No. 5, May 2011.
[9] H. Ming-Kuei, "Visual pattern recognition by moment invariants," Information Theory, IRE Transactions, vol. 8, pp. 179-187, 1962.
[10] G. Taubin and D. Cooper, “Recognition and positioning of rigid objects using algebraic moment invariants,” in SPIE Conference on Geometric Methods in Computer Vision, vol. 1570, 1991, pp. 175-186.
[11] Belkasim, S. O., Shridhar, M., & Ahmadi, M. (1991). “Pattern recognition with moment nvariants: a comparative study and new results”. Pattern Recognition, 24, 1117–1138.
[12] Yap, P. T., P. Raveendran, and S. H. Ong. "Chebyshev moments as a new set of moments for image reconstruction." Neural Networks, 2001. Proceedings. IJCNN`01. International Joint Conference on. Vol. 4. IEEE, 2001.
[13] Teh, C-H., and Roland T. Chin. "On image analysis by the methods of moments." IEEE Transactions on pattern analysis and machine intelligence10.4 (1988): 496-513.
[14] Dudani, S. A., Breeding, K. J., & McGhee, R. B. (1977). “Aircraft identification by moment invariants” .IEEE Transactions on Computers, 26, 39–45
[15] El-Khaly, F., & Sid-Ahmed, M. A. (1990). “Machine recognition of optically captured machine printed arabic text” Pattern Recognition, 23, 1207–1214.
[16] Tsirikolias, K., & Mertzios, B. G. (1993). “Statistical pattern recognition using efficient two-dimensional moments with applications to character recognition” Pattern Recognition, 26, 877–882.
[17] Flusser, J., & Suk, T. (1994a). “Affine moment invariants: A new tool for character recognition”. Pattern Recognition Letters, 15, 433–436.
[18] Haralick, K. Shanmugam, and I. Dinstein. “Texture Features for ImageClassification,” IEEE Trans. on Systems, Man and Cybernetics, SMC, vol.3, no 6, pp. 610–621, Nov. 1973.
[19] Kumar, R., Chandra, P., & Hanmandlu, M. (2013, December). Fingerprint matching using rotational invariant image based descriptor and machine learning techniques. In Emerging Trends in Engineering and Technology (ICETET), 2013 6th International Conference on (pp. 13-18). IEEE.
[20] Kumar, Ravinder, Pravin Chandra, and Madasu Hanmandlu. "Rotational invariant fingerprint matching using local directional descriptors." International Journal of Computational Intelligence Studies 3, no. 4 (2014): 292-319.
[21] J. E. Gary, and R. Mehrotra, "Shape similarity-based retrieval in image database systems," Proc. Of SPIE, Image Storage and Retrieval Systems, Vol. 1662, pp. 2-8, 1992.
[22] W. I. Grosky, and R. Mehrotra, "Index based object recognition in pictorial data management," CVGIP, Vol. 52, No. 3, pp. 416-436, 1990.
[23] H. V. Jagadish, "A retrieval technique for similar shapes," Proc. of Int. Conf. on Management of Data, SIGMOID’91, Denver, CO, pp. 208-217, May 1991.
[24] E. M. Arkin, L.P. Chew, D..P. Huttenlocher, K. Kedem, and J.S.B. Mitchell, "An efficientlycomputable metric for comparing polygonal shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 209-226, 1991.
[25] S. Sclaroff, and A. Pentland, "Modal matching for correspondence andrecognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17, No. 6, pp. 545-561, June 995.
[26] K. Arbter, W. E. Snyder, H. Burkhardt, and G. Hirzinger, "Application of affine-invariant Fourier descriptors to recognition of 3D objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 640-647, 1990.
[27] M. K. Hu, J. K. Aggarwal, R. O. Duda, and A. Rosenfeld, "Visual pattern recognition by moment invariants," in Computer Methods in Image Analysis, IEEE computer Society, Los Angeles, CA, 1977
[28] Manjula Gururaj Rao H. and G.S Nagaraja, “Content Based Medical Image Retrieval-A Survey”. International Journal of Computer Sciences and Engineering, 5(11), 157-160. Nov 2016.

Ravinder Kumar, Brajesh Kumar Singh, "Performance evaluation of Invariant moment features on Image retrieval," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.73-78, 2017.

Searching is the most important process in an information retrieval from available large databases. Many times we search for a set of documents which is relevant to the given search document. Text mining helps us to mine the information from a given set of documents and it is most popular technique in Information retrieval. In this research paper we have applied distinct distance measures for retrieval of most similar documents to the queried document from a set of given document. For obtaining optimality for required search, we have gone through pre-processing of documents, creating vector space model and used distance measure techniques. Precision and recall are the basic measures used in evaluating search strategies. We have presented five distance measure technique applied on hundred text documents from standard database 20NewsGroup and calculated Recall and precision rate for text documents retrieval. We have used MatLab 10a as a development tool for our experiment.

Text Mining, Information retrieval, distance measure, recall rate, precession rate, document

[1] L. Kumar, et.al ,”Text Mining: Concept, Process and Applications” JGRCS,Vol-4,No.3, March 2013
[2] K Mugunthadevi, et.al, “Survey on Feature Selection in Document Clustering” IJCSE, Vol-3,3 March 2011,
[3] Sowmya P, et.al , “Survey On Algorithms Used for Text Document Clustering”, IJAEC Special Issue September 2016
[4]A. Sudha Ramkumar et. al, “Text Document Clustering Using Dimension Reduction Technique”, IJAER Vol -11, November 7, 2016,
[5] A. Singh, et.al ,”K-means with Three different Distance Metrics”, IJCA, (0975 – 8887) Volume 67– No.10, April 2013
[6] A.Huang, ,” Similarity Measures for Text Document Clustering”, NZCSRSC 2008, April 2008, Christchurch, New Zealand
[7] S. Goswami et.al, “A Fuzzy Based Approach To Text Mining And Document Clustering”2013,
[8] A Text Book “ Text Mining and Application Programming” Manu Konchady ,Ed. 3 Indian Edition

U.S. Patki, A.B. Kurhe, P.G. Khot, "An Experimental Study of Recall and Precision Rates in Retrieval of Text Documents Using Different Distance Measures," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.79-83, 2017.

Aboodh transform (AT) in combination with the homotopy perturbation method (HPM) is employed to solve the nonlinear differential equation of motion for Euler-Bernoulli beams in a unified way. Aboodh transform based homotopy perturbation method (ATHPM) is found to give analytic solutions with all perturbative corrections to both the displacement and the oscillation frequency in a very simple and straight forward manner. Here, we have also demonstrated the sophistication and simplicity of this technique.

Aboodh Transform, Homotopy Perturbation Method, Euler-Bernoulli Beam

[1] A. H. Nayfeh, D. T. Mook, “Nonlinear Oscillations”, John Willey and Sons., New York, 1979.
[2] N.N. Bogoliubov, Y.A. Mitropolsky, “Asymptotic Methods in the Theory of Nonlinear Oscillations” Hindustan Publishing Company, Delhi, Chap. I, 1961.
[3] V. P. Agrwal, H. Denman, “Weighted linearization technique for period approximation in large amplitude Nonlinear Oscillations”, J. Sound Vib., Vol.57, pp.463-473, 1985.
[4] S.H. Chen, Y.K. Cheung, S.L. Lau, “On perturbation procedure for limit cycle analysis”, Int. J. Nonlinera Mech., Vol.26, pp.125-133, 1991.
[5] Y.K. Cheung, S.H. Chen, S.L. Lau, “A modified Lindstedt-Pioncare method for certain strong nonlinear oscillations”, Int. J. Non-Linear Mech., Vol.26, pp.367-378, 1991.
[6] G. Adomain, “A review of the decomposition method in applied mathematics”, J. Math. Anal. and Appl., Vol.135, pp.501-544, 1998.
[7] G.L. Lau, “New research direction in singular perturbation theory, artificial parameter approach and inverse-perturbation technique”, In the proceedings of the 1997 National Conference on 7th Modern Mathematics and Mechanics, pp.47-53.
[8] A.F. Nikiforov, V.B. Uvarov, “Special functions of mathematical physics”, Birkhauser, Basel, 1988.
[9] P.K. Bera, T. Sil, “Exact solutions of Feinberg-Horodecki equation for Time dependent anharmonic oscillator”, Pramana-J. Phys., Vol.80, pp.31-39, 2013.
[10] J.H. He, “Homotopy perturbation technique”, Comp. Methods in Appl. Mech. and Engg., Vol.178, pp.257-262, 1999.
[11] J.H. He, “A coupling method of a homotopy technique and a perturbation technique for nonlinear problems”, Int. J. Non-Linear Mech., Vol.3, pp.37-43, 2000.
[12] J. Biazar, M. Eslami, “A new homotopy perturbation method for solving systems of partial differential equations”, Comp. and Math. with Appli., Vol.62, pp.225–234, 2011.
[13] A. Yildirim, “Homotopy perturbation method to obtain exact special solutions with solitary pattern for Boussinesq-like B(m,n) equations with fully nonlinear dispersion”, J. Math. Phys., Vol.50, pp.5–10, 2009.
[14] M. Gover, A.K. Tomer, “Comparison of Optimal Homotopy Asymptotic Method with Homotopy Perturbation Method of Twelfth Order Boundary Value Problems”, International Journal of Computer Sciences and Engineering, Vol.3, pp.2739-2747, 2011.
[15] P.K. Bera, T. Sil, “Homotopy perturbation method in quantum mechanical problems”, Applied Math. and Comp., Vol.219, pp. 3272–3278, 2012.
[16] K.S. Aboodh, “The New integral Transform Aboodh Transform”, Global Journal of Pure and Applied Mathematics, Vol.9, pp.35-43, 2013.
[17] K. Abdelilah, S. Hassan, M. Mohand, M. Abdelrahim, A.S.S. Muneer, “An application of the new integral transform in Cryptography”, Pure and Applied Mathematics Journal, Vol.5, pp.151-154, 2016.
[18] K. Abdelilah, S. Hassan, M. Mohand, M. Abdelrahim, “Aboodh Transform Homotopy Perturbation Method For Solving System of Nonlinear Partial Differential Equations”, Mathematical Theory and Modeling, Vol.6, pp.108-113, 2016.
[19] S.S. Rao, “Vibraton of Continuous Systems”, John Wiley & Sons, Inc., Hoboken, New Jersey, 2007.
[20] S. Bagheri, A. Nikkar, H. Ghaffarzadeh, “Study of nonlinear vibration of Euler-Bernoulli beams by using analytical approximate technique”, Latin American Journal of Solids and Structures,Vol.11, pp.157-168, 2014.
[21] I. Parkar, M. Bayat, “An analytical study of Nonlinear Vibrations of Bucked Euler-Bernoulli beams”, 2012.
[22] H. Yaghoobi, M. Torabi, “An analytical approach to large amplitude vibration and post-bucking functionally graded beams rest on nonlinear elastic foundation”, Journal of theoretical and applied mechanics, Vol.51, pp.39-52, 2013.
[23] M. Hamid, S. Hasan, A. Reza, J. Zare, “The effect of quintic nonlinearity on the investigation of transversely vibrating Buckled Euler-Bernoulli beams”, Journal of theoretical and applied mechanics, Vol.51, pp.959-968, 2013.

P.K. Bera, S.K. Das, P. Bera, "A Study of Nonlinear Vibration of Euler-Bernoulli Beams Using Coupling Between The Aboodh Transform And The Homotopy Perturbation Method," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.84-93, 2017.

With the advancement in the communication technology, the probability of external attacks through networks is increasing day by day. Therefore, Intrusion Detection System (IDS) had became very important and an emerging area of research which, attempts to identify and notify the activities of users as normal (or) anomaly. IDS are a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. That is why the development of effective and robust Intrusion detection system is necessary. This paper presents a state of the art of intrusion detection system (IDS) classification techniques using various machine learning algorithms. Experiments have been conducted to evaluate the performance of various well known machine learning algorithms on NSL-KDD data set.

Intrusion Detection System, Attacks, KDD data set, False Acceptance Rate , Detection Rate, Neural Networks

[1] Garcia-Teodoro, Pedro, et al, "Anomaly-based network intrusion detection: Techniques, systems and challenges”, Computers & Security, Vol. 28, No. 1, pp 18-28. (2009):
[2] D. E. Denning. “An intrusion detection model.” IEEE Transactions on Software Engineering, Special issue on computer security and privacy, Vol. 13, No. 2, pp 222–232, 1987.
[3] H. Debar, M. Dacier, and A. Wespi. “Towards a taxonomy of intrusion-detection systems.” Networks, Vol. 31, No. 8, pp 805–822, 1999.
[4] S. Chebrolu, A. Abraham, and J. P. Thomas. Feature deduction and ensemble design of intrusion detection systems. Computers & Security, Vol. 24, No. 4, pp 295–307, 2005.
[5] D. Poole, A. Mackworth, R. Goebel. “Computational Intelligence - A Logical Approach”, Oxford University Press, Oxford, UK, 1998. ISBN-10: 195102703.
[6] J. C. Bezdek, “What is computational intelligence? Computational Intelligence Imitating Life”, pp 1–12, 1994. IEEE Press, New York
[7] W. Duch, “What is computational intelligence and where is it going”, In W. Duch and J. Mańdziuk, editors, Challenges for Computational Intelligence, volume 63 of Studies in Computational Intelligence, pp 1–13. Springer Berlin / Heidelberg, 2007.
[8] B. Craenen, A. Eiben, “Computational intelligence. Encyclopedia of Life Support Sciences”, EOLSS; EOLSS Co. Ltd., 2002.
[9] The KDD99 Dataset. Retrieved January 26, 2008, from http://kdd. ics.uci.edu/databases/kddcup99/task.html.
[10] J. McHugh, Testing intrusion detection systems a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Transactions on Information and System Security, Vol. 3, No. 4, pp 262–294, 2000.
[11] M. V. Mahoney, P. K. Chan, “An analysis of the 1999 DARPA/Lincoln laboratory evaluation data for network anomaly detection”. Technical Report TR CS-2003-02, Computer Science Department, Florida Institute of Technology, 2003.
[12] M. Sabhnani and G. Serpen. “Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set. Intelligent Data Analysis”, Vol. 8, No. 4, pp 403–415, 2004.
[13] J. Balthrop, S. Forrest, M. R. Glickman, “Revisiting LISYS: Parameters and normal behavior”, In D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow, and M. Shackleton, editors, Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’02), volume 2, pp 1045–1050, Honolulu, HI, USA, 12-17 May 2002. IEEE Press.
[14] L. Mé. GASSATA, “a genetic algorithm as an alternative tool for security audit trails analysis”. In Proceedings of the 1st International Workshop on the Recent Advances in Intrusion Detection (RAID 98), Louvain-la-Neuve, Belgium, pp 14-16 September 1998.
[15] K. Tan, “The application of neural networks to unix computer security”, In Proceedings of IEEE International Conference on Neural Networks, volume 1, pp 476–481, Perth, WA, Australia, Nov/Dec 1995. IEEE Press.
[16] J. Ryan, M. J. Lin, R. Miikkulainen, “Intrusion detection with neural networks”, Advances in Neural Information Processing Systems, Vol. 10, pp 943–949, 1998.
[17] A. P. F. Chan, W. W. Y. Ng, D. S. Yeung, and E. C. C. Tsang. “Comparison of different fusion approaches for network intrusion detection using ensemble of RBFNN.” In Proceedings of 2005 International Conference on Machine Learning and Cybernetics, volume 6, pp. 3846–3851. IEEE Press, 18-21 Aug. 2005.
[18] A. Hofmann, C. Schmitz, and B. Sick. “Rule extraction from neural networks for intrusion detection in computer networks”, In IEEE International Conference on Systems, Man and Cybernetics, volume 2, pp 1259–1265. IEEE Press, 5-8 Oct. 2003.
[19] Z. Liu, G. Florez, and S. M. Bridges. “A comparison of input representations in neural networks: A case study in intrusion detection”, In Proceedings of the International Joint Conference on Neural Networks (IJCNN ’02), volume 2, pages 1708–1713, Honolulu, HI, USA, 12-17 May 2002. IEEE Press.
[20] A. Rapaka, A. Novokhodko, and D. Wunsch. Intrusion detection using radial basis function network on sequence of system calls. In Proceedings of the International Joint Conference on Neural Networks (IJCNN ’03), volume 3, pages 1820–1825, Portland, OR, USA, 20-24 July 2003. IEEE Press.
[21] C. Zhang, J. Jiang, and M. Kamel. Comparison of BPL and RBF network in intrusion detection system. In G. Wang, Q. Liu, Y. Yao, and A. Skowron, editors, Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC ’03), 26-29 May, Chongqing, China, volume 2639 of Lecture Notes in Computer Science, chapter Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC ’03), pages 466– 470. Springer Berlin / Heidelberg, 2003.
[22] A. K. Ghosh and A. Schwartzbard. A study in using neural networks for anomaly and misuse detection. In Proceedings of the 8th USENIX Security Symposium, volume 8, pages 141–152, Washington, D.C., USA, 23-36 August 1999.
[23] Z. Zhang, J. Li, C. Manikopoulos, J. Jorgenson, J. Ucles, “HIDE: a hierarchical network intrusion detection system using statistical preprocessing and neural network classification”, In Proceedings of the 2001 IEEE Workshop Information Assurance and Security, pp 85–90, West Point, NY, USA, 2001. IEEE Press.
[24] Y. Yu, F. Gao, Y. Ge, “Hybrid BP/CNN neural network for intrusion detection” In Proceedings of the 3rd international conference on Information security, volume 85 of ACM International Conference Proceeding Series, pp 226–228, 2004.
[25] K. Fox, R. Henning, J. Reed, “A neural network approach toward intrusion detection” In Proceedings of the 13th National Computer Security Conference, Vol. 1, pp 124–134, Washington, D.C., USA, 1-4 Oct. 1990.
[26] A. J. Hoglund, K. Hatonen, A. S. Sorvari, “A computer host-based user anomaly detction system using the self-organizing map”, In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN ’00), Vol. 5, pp 411–416, Como, Italy, 24-27 July 2000. IEEE Press.
[27] W. Wang, X. Guan, X. Zhang, L. Yang, “Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data”, Computers & Security, Vol. 25, No. 7, pp. 539–550, 2006.
[28] A. Bivens, C. Palagiri, R. Smith, B. Szymanski, M. Embrechts, “Network based intrusion detection using neural networks”, Intelligent Engineering Systems through Artificial Neural Networks, Vol. 12, No. 1, pp 579–584, 2002.
[29] J. Cannad, J. Mahaffey, The application of artificial neural networks to misuse detection: Initial results”, In Proceedings of the 1st International Workshop on Recent Advances in Intrusion Detection (RAID 98), Louvain-la-Neuve, Belgium, ppn14-16 September 1998.
[30] C. Jirapummin, N. Wattanapongsakorn, P. Kanthamanon, “Hybrid neural networks for intrusion detection system”, In The 2002 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC ’02), Vol 7, pp 928–931, Phuket, Thailand, 2002.
[31] M. Amini, R. Jalili, H. R. Shahriari, “RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks”, Computers & Security, Vol. 25, No. 6, pp 459–468, 2006.
[32] Y. Liao, V. R. Vemuri, and A. Pasos. “Adaptive anomaly detection with evolving connectionist systems”, Journal of Network and Computer Applications, Vol. 30, No. 1, pp 60–80, 2007. Special Issue on Network and Information Security: A Computational Intelligence Approach.
[33] N. A. Durgin, P. Zhang, “Profile-based adaptive anomaly detection for network security”, Technical report, Sandia National Laboratories, 2005.

Ravinder Kumar, "A Review of Network Intrusion Detection System using machine learning algorithms," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.94-100, 2017.

Data mining plays an effective role in the field of computer science to analysis the data objects. The data mining process is used to mine the knowledge from huge database. Then, the extracted information is modified into an understandable data structure for the future analysis. The data structure in a computer is an essential approach to categorize and manage the data which is utilized for efficient usage. The data stream is referred as a structured sequence of instances; the data stream mining discovers the knowledge structures from continuous and fast data records. The clustering is the process of creating the group by collecting the data of similar patterns and also describes the meaningful structure of data. The additional process of traditional clustering termed as Subspace Clustering which is utilized for detecting the clusters in various subspaces within dataset. Then, the subspace clustering algorithms are introduced to discover the cluster in multiple overlapping subspaces by searching the relevant dimensions. Many research works are developed for managing the high dimensional data with the objective of providing better improvement on minimizing the performance of dimensionality and enhancing the clustering accuracy. However, the existing works failed to reduce the space complexity. Therefore, the research work focuses on reducing the dimensionality with improved clustering accuracy by executing the clustering and subspace clustering for data stream with data structure techniques.

Data stream, Multidimensional data, Data mining, Data structure, Subspace clustering

[1] Salman Ahmed Shaikh and Hiroyuki Kitagawa, “Top-k Outlier Detection from Uncertain Data”, International Journal of Automation and Computing, Volume 11, Issue 2, Pages 128-142, April 2014.
[2] Juan Zamora, Marcelo Mendoza and Hector Allende, “Hashing-based clustering in high dimensional data”, Expert Systems with Applications, Elsevier, Volume 62, Pages 202-211, November 2016,
[3] Weiling Cai, “A dimension reduction algorithm preserving both global and local clustering structure”, Knowledge-Based Systems, Volume 118, Pages 191–203, February 2017
[4] Ewa Nowakowska, Jacek Koronacki and Stan Lipovetsky, “Dimensionality reduction for data of unknown cluster structure”, Information Sciences, Elsevier, Volume 330, Pages 74-87, February 2016,
[5] Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, and Chuangyin Dang, “Space Structure and Clustering of Categorical Data”, IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 10, Pages 2047-2059, October 2016
[6] Can Wang, Xiangjun Dong, Fei Zhou, Longbing Cao and Chi-Hung Chi, “Coupled Attribute Similarity Learning on Categorical Data”, IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 4, Pages 781-797, April 2015
[7] Yifan Fu, Junbin Gao, David Tien, Zhouchen Lin, and Xia Hong, “Tensor LRR and Sparse Coding-Based Subspace Clustering”, IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 10, Pages 2120 – 2133, October 2016
[8] Xi Peng, Zhiding Yu, Zhang Yi, and Huajin Tang, “Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering”, IEEE Transactions on Cybernetics Volume 47, Issue 4, Pages 1053 – 1066, April 2017
[9] Madjid Khalilian, Norwati Mustapha and Nasir Sulaiman, “Data stream clustering by divide and conquer approach based on vector model”, Journal of Big Data, Springer, Volume 3, Issue 1, Pages 1-21, 2016
[10] Niwan Wattanakitrungroj, Saranya Maneeroj and Chidchanok Lursinsap “Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away Learning”, Journal of Classification, Springer, Volume 34, Issue 1, Pages 108–147, April 2017
[11] Dominik Olszewski, “Asymmetric k-Means Clustering of the Asymmetric Self-Organizing Map”, Neural Processing Letters, Springer, Volume 43, Pages 231–253, 2016
[12] Ghatage Trupti B, Patil Deepali E, Takmare Sachin B and Patil Sushama A “Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review”, International Journal of Computer Sciences and Engineering, Volume 4, Issue 3, Pages 54 – 58, 2016

K. Chitra, D. Maheswari, "A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.101-108, 2017.

To improve classification performance and prediction time of (IDS) system when a few number of large hyperboxes are formed in the network. We have proposed FMM NN and PCA features extraction algorithms. The process of a system consisting of the pre-processing dataset based on interquartile ranges filter and features extraction. Followed by the Fuzzy min-max neural network (FMM NN), which define as supervised learning classifier, that utilizes a fuzzy set hyberbox as pattern classes for learning and classification. Each hyberbox consists of min and mix points of opposite corners of hyberbox with corresponding to membership function. Two real-time and faster streaming datasets (KDD99 and NSL-KDD) are used to empirically evaluate the effectiveness of the proposed FMM NN system. The results are analyzed and compared with others existing systems and published results.

Intrusion Detection, Fuzzy min-max neural network, Principal Component Analysis (PCA), Machine Learning

[1] Ahmed Fuad Mohammed, Vikas T. Humbe, Santosh S. Chowhan, "Analytical Study of Intruder Detection System in Big Data Environment " Soft Computing: Theories and Applications SoCTA 2016, Volume 2, 2016.
[2] Khattab M. Ali Alheeti, Anna Gruebler, Klaus D. McDonald-Maier, "An Intrusion Detection System against Black Hole Attacks on the Communication Network of Self-Driving Cars", Emerging Security Technologies (EST), 2015 Sixth International Conference on, Publisher: IEEE, 10 March 2016.
[3] JABEZ Ja , Dr.B.MUTHUKUMAR, "Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection Approach ", International Conference on Intelligent Computing, Communication & Convergence (ICCC-2014), Procedia Computer Science 338 – 346 Publisher: Science Direct, 2015.
[4] Modi, Chirag N., Dhiren R. Patel, Avi Patel, and Rajarajan Muttukrishnan. "Bayesian Classifier and Snort based network intrusion detection system in cloud computing", 2012 Third International Conference on Computing Communication and Networking Technologies (ICCCNT 12), 2012.
[5] Ahmed Fuad Mohammed, Vikas T. Humbe, Santosh S. Chowhan, "Data Mining Based Traffic Classification Using Low-Level Features" International Journal of Computer Applications (0975 – 8887), 2017.
[6] Azad C, Jha VK , "Data mining based hybrid intrusion detection system", Indian J Sci Technol 7(6):781–789, 2014.
[7] Chen T, Zhang X, Jin S, Kim O , "Efficient classification using parallel and scalable compressed model and its application on intrusion detection", Expert Syst Appl 41(13):5972–5983, 2014.
[8] Gu B, Guo H, "The intrusion detection system based on a novel association rule", In: International conference on information science, electronics and electrical engineering (ISEEE), vol. 2, pp 1313–1316, 2014.
[9] Anas Quteishat, Chee Peng Lim. " A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification", Elsevier, Applied Soft Computing 8 (2008) 985–995.
[10] Liao S-H, Chu P-H, Hsiao P-Y, "Data mining techniques and applications–a decade review from 2000 to 2011", Expert Syst Appl 39(12):11303–11311, 2012.
[11] Julisch K, "Data mining for intrusion detection. Applications of data mining in computer security". Springer, US, Fuzzy Min-Max Neural Network-Based Intrusion Detection System pp 33–62, 2002.
[12] Wang G, Hao J, Ma J, Huang L "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering.Expert Syst" Appl 37(9):6225–6232.
[13] Chandrashekhar Azad, Vijay Kumar Jha, "Fuzzy min–max neural network and particle swarm optimization based intrusion detection system", Springer-Verlag Berlin Heidelberg 2016.
[14] . Lei JZ, Ghorbani AA , "Improved competitive learning neural networks for network intrusion and fraud detection", Neurocomputing 75(1)135–145.
[15] Basant Subba , Santosh Biswas, Sushanta Karmakar, "A Neural Network Based System for Intrusion Detection and Attack Classification", 978-1-5090-2361-5/16/$31.00 c 2016 IEEE, 08 September 2016.
[16] Azad Chandrashekhar, "Fuzzy Min-Max Neural Network-Based Intrusion Detection System", © Springer Nature Singapore Pte Ltd. PP 191-201, 2017.
[17] L. A. Zadeh, "Fuzzy Sets", Department of Electrical Engineering and Electronics Research Laboratory, University of California, Berkeley, California.
[18] Simpson PK, "Fuzzy min-max neural networks. I. classification", IEEE Trans Neural Networks 3(5), 776–786, 1992.
[19] Anjay Krishnankutty Alonso, eMath teacher for MAMBANI`S FUZZY INFERENCE MMETHOD, Retrievedfrom,http://www.dma.fi.upm.es/recursos/aplicaciones/logica_borrosa/web/fuzzy_inferencia/funpert_en.htm, (2017, Oct, 19)
[20] Pfahringer B Winning, " the KDD99 classification cup: bagged boosting", ACM SIGKDD Explor Newsl 1(2):65–66, 2000.
[21] Levin I, KDD-99, "classifier learning contest: LLSoft’s results overview", SIGKDD Explor 1(2):67–75, 2000.

A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai, "Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.109-117, 2017.

The Cloud computing come into points of interest as a brand new emerging technology which provides personalized QoS guaranteed and trustworthy computing environment for end-customers. In cloud environment the Jobs are planned and accomplish in the constraints. Means of constraints right here is to apply QoS that customers want and balancing among those QoS and impartiality some of the Jobs. Many scheduling algorithms are proposed and executed to fulfill the requirement of cloud computing. Some of the task scheduling algorithms focused on the priority to each task depending on its attributes and then schedule tasks while considering the high priority. Others Jobs scheduling algorithm based upon QoS resourcefully utilized the resources idle time from monitoring the task schedule and timing information. But most of the current scheduling algorithms have not focus on the QoS parameter based upon task computation & communication time. They only examine the inactive time of resources continuously & renew the minimum completion time (MCT) of resources in matrix; but not executed the jobs based upon their job type. The task which demanded high computation resources should executed on high speed CPU and task required communication based resource should be executed on resources with high speed bandwidth. This paper proposed an enhanced QoS based algorithm which also taken into account the task monitoring and job type parameters.

Cloud Computing, Job Type, Job Priority, QoS, Task Scheduling

[1]. Ali, H.G.E.D. Hassan, I.A. Saroit and A.M. Kotb, "Grouped tasks scheduling algorithm based on QoS in cloud computing network." Egyptian Informatics Journal, 1(18), pp.11-19, 2017.
[2]. Zhao, Laiping, Y. Ren, and K. Sakurai. "Reliable workflow scheduling with less resource redundancy." Parallel Computing 39.10 (2013): 567-585.
[3]. Awad, A. I., N. A. El-Hefnawy, and H. M. Abdel_Kader. "Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments." Procedia Computer Science 65 (2015): 920-929.
[4]. S. Singh and I. Chana. "Q-aware: Quality of service based cloud resource provisioning." Computers & Electrical Engineering 47 (2015): 138-160.
[5]. W. Xiaonian, M. Deng, R. Zhang, B. Zeng, and S. Zhou. "A task scheduling algorithm based on QoS-driven in cloud computing." Procedia Computer Science 17 (2013): 1162-1169.
[6]. S. Singh and I. Chana. "A survey on resource scheduling in cloud computing: Issues and challenges." Journal of Grid Computing 14.2 (2016): 217-264.
[7]. Wang, Wei-Jen, Y. Chang, W. Lo and Y.K. Lee. "Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments." The Journal of Supercomputing 66, no. 2 (2013): 783-811.
[8]. Lakra, A. Vikas and D. K. Yadav. "Multi-objective tasks scheduling algorithm for cloud computing throughput optimization." Procedia Computer Science 48 (2015): 107-113.
[9]. Zhang, Rui, K. Wu, M. Li, and J. Wang. "Online resource scheduling under concave pricing for cloud computing." IEEE Transactions on Parallel and Distributed Systems 27, no. 4 (2016): 1131-1145.
[10]. He, Hua, G. Xu, S. Pang, and Z. Zhao. "AMTS: Adaptive multi-objective task scheduling strategy in cloud computing." China Communications 13, no. 4 (2016): 162-171.
[11]. Alkhashai, H. M., and F.A. Omara, "An Enhanced Task Scheduling Algorithm on Cloud Computing Environment." International Journal of Grid and Distributed Computing 9.7 (2016): 91-100.
[12]. Xue, Shengjun, M. Li, X. Xu, J. Chen, and S. Xue. "An ACO-LB algorithm for task scheduling in the cloud environment." Journal of Software 9, no. 2 (2014): 466-473.
[13]. N. Bansal, A. Maurya, T. Kumar, M. Singh, and S. Bansal. "Cost performance of QoS Driven task scheduling in cloud computing." Procedia Computer Science 57 (2015): 126-130.
[14]. T. Antony, G. Krishnalal, and V.P.J. Raj. "Credit based scheduling algorithm in cloud computing environment." Procedia Computer Science 46 (2015): 913-920.
[15]. Rodriguez, M. Alejandra, and R. Buyya, "Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds." IEEE Transactions on Cloud Computing 2, no. 2 (2014): 222-235.
[16]. Kumar, R. Sathish, and S. Gunasekaran, "Improving Task Scheduling in Large Scale Cloud Computing Environment using Artificial Bee Colony Algorithm." International Journal of Computer Applications 103, No. 5 (2014).
[17]. L. Wang, G. Laszewski, M. Kunze, J. Tao, “Cloud Computing: A Perspective Study”, New Generation Computing- Advances of Distributed Information Processing, pp. 137-146, vol. 28, no. 2, 2008. DOI: 10.1007/s00354-008-0081-5.
[18]. R. K. Bawa and G. Sharma, “Modified Min –Min Heuristic for Job Scheduling based on QoS in Grid Environment”. IEEE Conference on International Management in the Knowledge Economy, 19-20 December 2013.
[19]. C.Jasmin Selvi, G.Sathish Kumar, “An Adaptive Double-Quality-Guaranteed (DQG) Scheme based Quality of Service (QOS) in Heterogeneous Cloud Environment”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.258-265, 2016.
[20]. S. Arnold,”Cloud computing and the issue of privacy”, KM World, pp14-22. Available: www.kmworld.com, Aug. 19, 2009.
[21]. Anbazhagi, L. Tamilselvan and Shakkeera, "QoS based dynamic task scheduling in IaaS cloud," International Conference on Recent Trends in Information Technology, Chennai, 2014, pp. 1-8. doi: 10.1109/ICRTIT.2014.6996176.
[22]. R.K. Bawa and G. Sharma, "Reliable Resource Selection in Grid Environment”. International Journal of Grid Computing & Applications, March 2012, Volume 3, Number 1, pp. 1-10.
[23]. R.L. Grossman, “The Case for Cloud Computing”, IT Professional, vol. 11(2), pp. 23-27, 2009, ISSN: 1520-9202.

S. Khurana, R. K. Singh, "Job Categorization based Task Scheduling using QoS in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.118-122, 2017.

Working Memory (WM) is the mental thinking space in which learners manipulate or act on aspects of their knowledge in mathematical learning situations. It is the activity that allows learners to create links between the data collected during such a situation, and the knowledge gained from long-term memory, to synthesize new knowledge in mathematics, and when they direct their learning and thinking activity to calculate or solve mathematical problems. This work investigates the impact of WM on the academic achievement of mathematics knowledge using electronic learning resources. It also attempts to determine the process of WM to foster and improve the cognitive structures of the learner to facilitate the transformation of information and to succeed in a learning mathematics session. The results obtained lead to a good correlation between these two variables. The digital resources used promote the expected results, and facilitate the collection and processing of the data needed for such research. This makes it possible to better understand the functioning of WM during a learning situation in mathematics, and thus to be able to lead to solutions when learning problems appear.

Working memory capacity, Digital resources, Learning, Academic achievement Mathematics

[1] G. Brousseau, “Théories des situations didactiques". La pensée Sauvage, Grenoble, 1998.
[2] A.D. Baddeley, G.J Hitch, “Working memory”. In G.H. Bower (Ed), The psychology of learning and motivation: advances in research and theory, Vol. 8, pp. 47-89, 1974.
[3] A. D. Baddeley, “Working memory”, Oxford: Oxford University Press, 1986.
[4] A. T. Packiam, “How Does Working Memory Work in The Classroom”? Educational Research and Reviews Vol. 1, Issue 4, pp. 134-139, 2006.
[5] T. Alloway, R. Alloway, “The working memory advantage”. New York : Simon & Schuster, 2013.
[6] P. Peng, J. Namkung, M. Barnes, and C. Sun, “A Meta-Analysis of Mathematics and Working Memory: Moderating Effects of Working Memory Domain, Type of Mathematics Skill, and Sample Characteristics”, Journal of Educational Psychology, Vol. 108, Issue 4, pp.455-473, 2015
[7] G. Bussy, "La memoire de travail a l’ecole. pour comprendre et accompagner au quotidien" Editions Remédiacog. ISBN : 978-2-9543342-4-0, 2014.
[8] P. Barouillet and V. Camos, « Le développement de la mémoire de travail ». In J. Lautrey (Ed.), Psychologie du développement et de l’éducation. Paris : PUF, pp. 51-86, 2007.
[9] A. George, “Miller The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information”, Psychological Review, Vol. 63, pp.81–97, 1956.
[10] S. E. Gathercole, S. J. Pickering, B. Ambridge, and H. Wearing, “The structure of Working Memory From 4 to 15 years of age ” , Developmental Psychology, Vol. 40, No. 2, pp. 177-190, 2004.
[11] A. Baddeley, “The fractionation of working memory”,Proc. Natl. Acad. Sci. USA, Vol. 93, pp.13468-13472, 1996.
[12] G. RepovS, A. Baddeley, “The multi-component model of working memory: Explorations in experimental cognitive psychology”,Neuroscience, Vol. 139, Issue 1, pp. 5-21, 2006
[13] A. D. Baddeley, “Working Memory”, Philosophical Transactions of the royal Society of London”.Series B, Biological Sciences, Functional Aspectsof Human Memory, Vol. 302, No. 1110, pp. 311-324, 1983.
[14] A.Baddeley, “Working memory; looking back and looking forward”,Nature Reviews Neuroscience, Vol. 4, pp. 829-839, 2003.
[15] A. Baddeley, “The episodic buffer: a new component of working memory?”,Trends in Cognitive Sciences, Vol. 4, Issue 11, pp. 417-423, 2000.
[16] T.P. Alloway, S. Gathercole, S. I. Pickering and D. Child, “Verbal and visuospatial short-term and working memory in children: are they separable?”, Vol. 77, Issue 6, pp. 1698-1716, 2006
[17] J. Holmes, “Working memory and learning difficulties”, Dyslexia Review Summer, 2012.
[18] C. Giraudeau and G. Chasseigne,”Psychologie, éducation et vie scolaire”, Paris: EPU, 2014.
[19] L. S. Pagani, C. Fitzpatrick, L. Belleau and M. Janosz, “Predicting Academic Achievement in Fourth Grade from Kindergarten Cognitive, Behavioural and Motor Skillsl”, Québec Longitudinal Study of
Child Development (QLSCD 1998-2010) – From Birth to 10 Years, Québec, Institut de la statistique du Québec, Vol. 6, 2011.
[20] P. Brrouillet, V. Camos, S. Morlaix and B. Suchaut, “Academic progress, task memory and social background. Their connectionc in elementary school”, Revue francaise de pédagogie, Vol. 162, 2008.
[21] S Frenkel, H. Deforge, "Métacognition et réussite scolaire: Perspectives théoriques”, Psychologie, Education et Vie Scolaire, 2014.
[22] J. Tardif, "Pour un enseignement stratégique: l’apport de la psychologie cognitive", Montréal :Editions Logiques, 1992.
[23] C. Cote, “Etude des pratiques sur l’adaptation de l’enseignement des Mathématiques en contexte de collaboration et de coenseignement”, Doctoral Thesis, Université du Québec à Montréal, 2015.
[24] T.P. Alloway, “How does working memory work in the classroom?”, Educational Research and Reviews, Vol. 1, Issue 4, pp. 134-139, 2006.
[25] B. Dominique. “Le manuel scolaire”. Paris : Documentation française, p. 36, 1998.
[26] S. Bhattacharya, S. Chowdhury and S. Roy, “An Online Recommender Agent for Boosting the Confidence of an e-learner”, International Journal of Computer Sciences and Engineering, Vol. 2, Issue 12, pp. 17-20, 2014.
[27] L. Moran and B. Myringer, "Higher Education through open
and distance learning, " in Flexible learning and university
change, (Ed. K. Harry), London, 1999.
[28] J. Heutte, "Influence de l’habituation à l’usage de l’outil informatique sur l’apprentissage et les résultats scolaires d’élèves du cycles 3 de l’école primaire", -e - Revue de Recherches en Éducation – 2008.
[29] J. M. Fourgous, "Rapport sur la modernisation de l’école par le numérique, Réussir l’école numérique". La Documentation Française, pp. 105-115, 2010.

B. El Mamoun, M. Erradi, A. El Mhouti, "Using Digital Resources in the Measure of the Impact of Working Memory on Students` Acquisition of Mathematical Knowledge," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.123-129, 2017.

DNA sequences are the store house of all the biological information. DNA sequencing focuses on determining the exact order of the nucleotides in a DNA sequence. Many research efforts have been put to the development of cheaper and increasingly higher-throughput sequencing techniques. This lead to development of a massively parallel and efficient method called as Next Generation Sequencing (NGS). The massively parallel NGS technologies have a high throughput with reduced cost. This paper gives a brief working principle of sequencer such as Roche 454 (GS FLX Titanium/GS Junior), Illumina (Genome Analyzer/HiSeq 2000/MiSeq) and Life Technologies (SOLiD/Ion Torrent PGM) along with a their comparison.

Next generation sequencing, Roche, Illumina, SOLiD, Sanger

[1] A. Jiwan and S. Singh, “A review on RNA pseudoknot structure prediction techniques,” in 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET). IEEE, 2012, pp. 975–978.

[2] R. Garc´ıa, “Prediction of RNA pseudoknotted secondary structure using Stochastic Context Free Grammars (SCFG),” CLEI Electronic Journal, vol. 9, no. 2, 2006.
[3] Y. Tateno, T. Imanishi, S. Miyazaki, K. Fukami-Kobayashi, N. Saitou,

H. Sugawara, and T. Gojobori, “DNA Data Bank of Japan (DDBJ) for genome scale research in life science,” Nucleic Acids Res, vol. 30, no. 1, pp. 27–30, 2002.

[4] G. H. Hamm and G. N. Cameron, “The EMBL data library,” Nucleic Acids Res, vol. 14, no. 1, pp. 5–9, 1986.
[5] C. Kanz, P. Aldebert, N. Althorpe, W. Baker, A. Baldwin, K. Bates,

P. Browne, A. van den Broek, M. Castro, G. Cochrane et al., “The EMBL nucleotide sequence database,” Nucleic Acids Res, vol. 33, no. suppl 1, pp. D29–D33, 2005.

[6] D. A. Benson, M. Cavanaugh, K. Clark, I. Karsch-Mizrachi, D. J. Lipman, J. Ostell, and E. W. Sayers, “Genbank,” Nucleic Acids Res, vol. 41, no. D1, pp. D36–D42, 2013.
[7] P. D. Stenson, E. V. Ball, M. Mort, A. D. Phillips, J. A. Shiel, N. S. Thomas, S. Abeysinghe, M. Krawczak, and D. N. Cooper, “Human gene mutation database (HGMD R ): 2003 update,” Hum Mutat, vol. 21, no. 6, pp. 577–581, 2003.

[8] M. Cariaso and G. Lennon, “SNPedia: a wiki supporting personal genome annotation, interpretation and analysis,” Nucleic Acids Res, vol. 40, no. D1, pp. D1308–D1312, 2012.
[9] U. Consortium et al., “Reorganizing the protein space at the Universal Protein Resource (UniProt),” Nucleic Acids Res, p. gkr981, 2011.

[10] C. H. Wu, L.-S. L. Yeh, H. Huang, L. Arminski, J. Castro-Alvear,
Y. Chen, Z. Hu, P. Kourtesis, R. S. Ledley, B. E. Suzek et al., “The protein information resource,” Nucleic Acids Res, vol. 31, no. 1, pp. 345–347, 2003.

[11] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat,
H. Weissig, I. N. Shindyalov, and P. E. Bourne, “The protein data bank,” Nucleic Acids Res, vol. 28, no. 1, pp. 235–242, 2000.

[12] A. Andreeva, D. Howorth, S. E. Brenner, T. J. Hubbard, C. Chothia, and

A. G. Murzin, “SCOP database in 2004: refinements integrate structure and sequence family data,” Nucleic Acids Res, vol. 32, no. suppl 1, pp. D226–D229, 2004.

[13] S. Griffiths-Jones, A. Bateman, M. Marshall, A. Khanna, and S. R. Eddy, “Rfam: an RNA family database,” Nucleic Acids Res, vol. 31, no. 1, pp. 439–441, 2003.
[14] T. Weirick, D. John, S. Dimmeler, and S. Uchida, “C-It-Loci: a knowl-edge database for tissue-enriched loci,” Bioinformatics, p. btv410, 2015.
[14] G. D. Bader, D. Betel, and C. W. Hogue, “BIND: the biomolecular interaction network database,” Nucleic Acids Res, vol. 31, no. 1, pp. 248–250, 2003.

[15] C. Stark, B.-J. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and

M. Tyers, “BioGRID: a general repository for interaction datasets,” Nucleic Acids Res, vol. 34, no. suppl 1, pp. D535–D539, 2006.
[16] H. Parkinson, M. Kapushesky, M. Shojatalab, N. Abeygunawardena,

R. Coulson, A. Farne, E. Holloway, N. Kolesnykov, P. Lilja, M. Lukk et al., “ArrayExpressa public database of microarray experiments and gene expression profiles,” Nucleic Acids Res, vol. 35, no. suppl 1, pp. D747–D750, 2007.

[18] T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev, C. Evan-gelista, I. F. Kim, A. Soboleva, M. Tomashevsky, and R. Edgar, “NCBI GEO: mining tens of millions of expression profilesdatabase and tools update,” Nucleic Acids Res, vol. 35, no. suppl 1, pp. D760–D765, 2007.
[19] R. Backofen and D. Gilbert, “Bioinformatics and constraints,” CSTR, vol. 6, no. 2-3, pp. 141–156, 2001.

[20] N. M. Luscombe, D. Greenbaum, and M. Gerstein, “What is bioinfor-matics? an introduction and overview,” Yearb Med Inform, vol. 1, pp. 83–99, 2001.

[21] M. V. Schneider, J. Watson, T. Attwood, K. Rother, A. Budd, J. Mc-Dowall, A. Via, P. Fernandes, T. Nyronen, T. Blicher et al., “Bioin-formatics training: a review of challenges, actions and support require-ments,” Briefings in bioinformatics, p. bbq021, 2010.

[22] M. J. Munoz and D. L. Riddle, “Positive selection of caenorhabditis el-egans mutants with increased stress resistance and longevity,” Genetics, vol. 163, no. 1, pp. 171–180, 2003.

[23] M. P. Sawicki, G. Samara, M. Hurwitz, and E. Passaro, “Human genome project,” Am J Surg, vol. 165, no. 2, pp. 258–264, 1993.

[24] M. Fedurco, A. Romieu, S. Williams, I. Lawrence, and G. Turcatti, “BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies,” Nucleic Acids Res, vol. 34, no. 3, p. e22, 2006.

[25] KA Wetterstrand, “DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP)” National Human Genome Research Institute the cost of sequencing a human genome, 2016.

[26] M. Ronaghi, S. Karamohamed, B. Pettersson, M. Uhlen,´ and P. Nyren,´ “Real-time DNA sequencing using detection of pyrophosphate release,” Anal. Biochem., vol. 242, no. 1, pp. 84–89, 1996.

[27] J. M. Heather, and B. Chain, “The sequence of sequencers: the history of sequencing DNA,” Genomics, vol. 107.1, pp. 1-8, 2016.
[28] G. Turcatti, A. Romieu, M. Fedurco, and A.-P. Tairi, “A new class of cleavable fluorescent nucleotides: synthesis and optimization as reversible terminators for DNA sequencing by synthesis,” Nucleic Acids Res, vol. 36, no. 4, p. e25, 2008.

[29] C. Adessi, G. Matton, G. Ayala, G. Turcatti, J.-J. Mermod, P. Mayer, and E. Kawashima, “Solid phase DNA amplification: characterisation of primer attachment and amplification mechanisms,” Nucleic Acids Res, vol. 28, no. 20, p. e87, 2000.

[30] J. Shendure, G. J. Porreca, N. B. Reppas, X. Lin, J. P. McCutcheon,

A. M. Rosenbaum, M. D. Wang, K. Zhang, R. D. Mitra, and G. M. Church, “Accurate multiplex polony sequencing of an evolved bacterial genome,” Science, vol. 309, no. 5741, pp. 1728–1732, 2005.

[31] K. McKernan, A. Blanchard, L. Kotler, and G. Costa, “Reagents, methods, and libraries for bead-based sequencing,” Feb. 1 2006.

[32] S. Myllykangas , J. Buenrostro , and H. P. Ji “Bioinformatics for High Throughput Sequencing”, Springer, pp 255, 2012 .
[33] S. Pabinger, A. Dander, M. Fischer, R. Snajder, M. Sperk, M. Efremova,

B. Krabichler, M. R. Speicher, J. Zschocke, and Z. Trajanoski, “A survey of tools for variant analysis of next-generation genome sequencing data,” Briefings Bioinf., vol. 15, no. 2, pp. 256–278, 2014.

[34] R. Bao, L. Huang, J. Andrade, W. Tan, W. A. Kibbe, H. Jiang, and
G. Feng, “Review of current methods, applications, and data man-agement for the bioinformatics analysis of whole exome sequencing,” Cancer Inform, pp. 67–83, 2014.

A. Jiwan, S. Singh, "Next Generation Sequencing: an Emerging Bioinformatics Field," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.130-134, 2017.