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Over the past decade, ANN is used in many fields including Engineering and Medical electronics. ANN is also been applied to solve many problems of classification and prediction. Depending on the problem space and complexity various approaches were proposed to solve the problems in an efficient way. Multi-layer Feed forward network is one of the network architecture predominantly used to solve classification and prediction problems. The objective of this paper is to study the various methods available in the literature for solving those problems. The study starts with a simple feed forward network for image classification, then continued to investigate the methods to improve the classification accuracy using various wavelets and dimensionality reduction techniques. The various improvements were proposed for Backpropagation algorithm including complex BP were analysed. For performance improvement, methods of evolutionary algorithms and Pruning techniques were studied briefly. Finally the improved RBF network for complex numbers was analysed. This paper gives an overall idea of how the feed forward network was evolved with various approaches for solving classification and prediction problems.

Feed Forward, Classification, Predication, Backpropogration, PSO, RBF, Complex valued ANN.

[1] K. Verma, L. K. Verma and P. Tripathi, “Image Classification using Backpropogation Algorithm”, JOURNAL OF COMPUTER SCIENCE AND SOTWARE APPLICATION, Vol. 1, No.2, 2014
[2] T. Kathirvalavakumar, J. B. Vasanthi, “Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function”, Journal of Intelligent Learning Systems and Applications, 2013
[3] T. Kathirvalavakumar, J. B. Vasanthi, “Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF”, International Journal of Computer Applications, Vol. 74, No.5, 2013
[4] Y. Xie and Y. Zhang, “A Wavelet Network Model for Short-Term Traffic Volume Forecasting”, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 10, No.3, pp.141-150, 2006
[5] A. Sinha, M. Pavithra and B. J. Pandian, “A Wavelet Neural Network based approach in Cancer Diagnosis and Biopsy Classification”, International Journal of Advancements in Research & Technology, Vol. 2, Issue.8, 2013
[6] M. Karthigaiselvi, T. Kathirvalavakumar, “Recognition of Words in Tamil Script Using Neural Network” , Int. Journal of Engineering Research and Application, Vol.7 Issue.3, (Part-6), pp.62-70, 2017
[7] S. K. Roy, C. F. Rodrigues, “Echo Canceller Using Error Back Propogration Algorithm”, International Conference on Soft Computing and Machine Intelligence, 2014
[8] G. A. Ali and A. Tayfour, “Characteristics and Prediction of Traffic Accident Casualties in Sudan Using Statistical Modelling and Artificial Neural Networks”, International Journal of Transportation Science and Technology, Vol.1 No.4.202, pp.305-317, 2012
[9] M. Sornam and P. Thangavel, “An improved three-term optical backpropogation algorithm”, Int. J. Artificial Intelligence and Soft Computing, Vol.2, No.4, pp.321-333, 2011
[10] D. Chhachhiya, A. Sharma and M. Gupta, “Designing optimal architecture of neural network with particle swarm optimization techniques specifically for educational dataset”, 7th International Conference on Cloud Computing, Data Science & Engineering, 2017
[11] T. He, T. Dan, Y. Wei, H. Li, X. Chen, G. Qin, “Particle Swarm Optimization RBF Neural Network Model for Internet Traffic Prediction”, International Conference on Intelligent Transportation, Big Data & Smart City, 2016
[12] X. Liu, X. Jiao, Y. Li and X. Liang, “Improved New Particle Swarm Algorithm Solving Job Shop Scheduling Optimization Problem”, 3rd International Conference on Computer Science and Network Technology, 2013
[13] M. G. Augasta, and T. Kathirvalavakumar, “Pruning algorithms of neural networks – a comparative study”, Cent. Eur. J. Comp Sci. Vol. 3(3), pp.105-115, 2003
[14] M. G. Augasta, and T. Kathirvalavakumar, “A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems”, Springer, 2011
[15] T. Nitta, “A Back-propagation Algorithm for Complex Numbered Neural Networks”, Internal Joint Conference on Neural Networks, 1993
[16] H. E. Michel and A.A.S. Awwal, “Enhanced Artificial Neural Network using Complex Numbers”, IEEE, 1999
[17] A. S. Gangal, P.K. Kalra, and D.S. Chauhan, “Inversion of Complex Valued Neural Networks using Complex Back-propagation Algorithm”, International Journal of Mathematics and Computers in Simulation, Vol.3, Issue.3, 2009
[18] R. Savitha, S. Suresh and N. Sundararajan, “A Fully Complex-valued Radial Basis Function Network and its Learning Algorithm”, International Journal of Neural Systems, Vol.19 No.4, 99, pp.253-267, 2009
[19] R. Savitha, S. Suresh, N. Sundararajan andH. J. Kim, “A fully complex-valued radial basis function classifier for real-valued

M. Sornam, P. Balamurugan, "Evolution of Feed Forward Network for solving Classification and Prediction Problems", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.136-141, 2018.

India has a rich intellectual tradition that evolved since last 5000 years. To our luck much knowledge acquired over this period were kept as ancient scriptures. Vedic Mathematics is the name given to the ancient system of Indian Mathematics which was rediscovered from the vedas by Sri Bharathi Krishna Tirthaji. The whole vedic mathematics is based on sixteen sutras providing unique and simple techniques to various mathematical computations. Processing time, power and hardware are the main challenging aspects in any computer algorithms. Hence high speed mathematical tools are of great demand. Ancient Indian scripts are believed to contain many mathematical shortcuts which can be used to fine-tune computer algorithms. Many researchers found the Vedic multipliers, adders and other vedic tools help in saving resources. In this paper we present various applications of vedic mathematics in Digital Signal Processing and also analyzed computational complexity of various algorithms in digital signal processing using vedic mathematics proposed by different researchers.

Vedic Mathematics, Vedic Sutras, Urdhva Tiryakbhyam Sutra, Digital Signal Processing

[1] Jagadguru Swami Sri Bharathi Krishna Tirathji Maharaja, “Vedic Mathematics or Sixteen Simple Mathematical Formulae from Vedas”, Motilal Banarsidas, Varanasi, India, 1965.
[2] M. Pradhan, R. Panda and S.K. Sahu, “Speed Comparison of 16x16 Vedic Multipliers”, International Journal of Computer Applications Vol. 21, Issue 6, pp.16-19, 2011.
[3] P Saha, D Kumar, P. Bhattacharyya, and A Dandapat, “Reciprocal unit based on Vedic mathematics for Signal processing applications”, International Symposium on Electronic System Design, pp.41-45, 2013.
[4] R. Jamgade, S. Ambatkar and S. Kakde. “Design and Implementation of PN Sequence Generator using Vedic Multiplication”, International conference on Advances in Computer Engineering and Applications (ICACEA) IMS Engineering College, Ghaziabad, India, pp .84-87, 2015.
[5] A. Savadi, R.Yanamshetti, S.Biradar, “Design and Implementation of 64 Bit IIR Filters Using Vedic Multipliers”, International conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science 85 (2016) Elsevier, pp.790-797, 2016.
[6] Jinesh S, Ramesh P, J. Thomas, “Implementation of 64 Bit High Speed Multiplier for DSP Application Based on Vedic Mathematics”, TENCON 2015-2015 IEEE Region 10 Conference, 2015.
[7] S.N.Gadakh, A. Khade ,” Design and Optimization of 16 X 16 Bit Multiplier Using Vedic Mathematics”, International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) International Institute of Information Technology (I2 IT), Pune, pp.460-464, 2016.
[8] Anjana S, Pradeep C ,P. Samuel, “Synthesis of High Speed Floating Point Multipliers Based on Vedic Mathematics”, International Conference on Information and Communication Technologies (ICICT 2014), pp.1294-1302, 2014.
[9] M.Shoba, R.Nakkeeram ,”Energy and Area Efficient Hierarchy Multiplier Architecture Based on Vedic Mathematics and GDI logic”, Engineering Science and Technology, an International Journal, Vol 20, Issue.1, pp 321-331, 2017.
[10] L P Thakare , Dr A Y Deshmukh , “Area Efficient Complex Floating Point Multiplier for Reconfigurable FFT/IFFT Processor based on Vedic Algorithm”, 7th International Conference on Communication, Computing and Virtualization 2016, Procedia Computer Science 79 (2016) Elsevier, pp.434-440,2016.
[11] N R Punwantwar, Dr P N Chatur, “Convolution and Deconvolution Using Vedic Mathematics”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol 4,Issue.6, pp.5216-5223, 2015.
[12] Deepthi P, V.S. Chakravarthi,”Design of Novel Vedic Asynchronous Digital Signal Processor Core”, 2014 2nd International Conference on Devices, Circuits and Systems, pp. 1-5, 2014

A. Lisha, T. Monoth, "Analysis of Digital Signal Processing Algorithms Based on Vedic Mathematics", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.142-144, 2018.

Rough set theory is a powerful mathematical tool that has been applied widely to extract knowledge from many databases. It deals with inexact and incomplete data. Cluster analysis means finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters that are meaningful, useful, or both. Data clustering techniques are valuable tools for researchers working with large databases of multivariate data. Cluster analysis is used in various applications viz., Pattern Recognition, Data Analysis, Image Processing and so on. This paper analyses Roughness and Maximum Dependency Attribute clustering algorithms that minimizes the need for subjective human intervention and compare the purity analysis between these two methods. Purity analysis percentage is calculated from the result of final clusters. Six datasets are used in this research work for comparing the roughness and maximum dependency attribute algorithm to describe the cluster solution by using the purity analysis (PA).

Rough Clustering, Equivalence Classes, Roughness, Maximum Dependency Attribute, Purity Analysis

[1] Arun K Pujari, “Data Mining Techniques”, Universities Press (India) Private Limited, 2010.
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[3] Chandranath Adak, “Rough Clustering Based Unsupervised Image Change Detection”.
[4] J.Han and M.Kamber, “Datamining: Concepts and Techniques”, Morgan Kaufmann Publishers, 1992.
[5] Jun-Hao Zhang, Ming-Hu Ha, Jing Wu, “Implementation of Rough K-Means Clustering Algorithm in MATLAB”, 9th International Conference on Machine Learning and Cybernetics, pp.2084-2087, July 2010.
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[7] Pawan Lingras, Rui Yan, Chad West, “Comparison of Conventional And Rough K-Means Clustering”, LNAI 2639, Springer – Verlag Berlin Heidelberg,pp.130-137,2003.
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Rough Set Approach for Extracting Decision Rules”, pp.5636-5639, IEEE 2007.

M.Jancy Rani, A. Pethalakshmi, "Comparative Analysis of Roughness with Maximum Dependency Attribute", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.145-149, 2018.

The security is the most important issue in Internet of Things (IoT) nowadays. In this paper we discuss the attacks against Routing Protocol for Low power and Lossy Networks (RPL).The IoT contains the constrained devices which are limited in resources like limited power, memory and processing capabilities. Healthcare, Home appliances, Transport, Social Networking, Defences, Banking are some examples of IoT applications. For this purpose the new protocol is designed called RPL. The RPL is a network layer protocol .The RPL is a leight weight distance vector protocol. In IoT to provide the security and privacy is challenging when the devices are connected to the lossy networks. In this paper the research is focus on the attacks against the RPL.

RPL, Attacks, IoT, Malicious, Security, Nodes

[1] Weekly, Kevin, and Kristofer Pister. "Evaluating sinkhole defense techniques in RPL networks." Network Protocols (ICNP), 20th IEEE International Conference 2012.
[2] Raza, Shahid, Linus Wallgren, and Thiemo Voigt."SVELTE: Real-time intrusion detection in the Internet of Things." Ad hoc networks vol.11 ,Issue.8,pp.2661-2674(2013).
[3] Mohammad Alzubaidi, Mohammed Anbar et al ,”Review on the mechanism of detecting sinkhole attacks on RPL “ International Conference on Information Technology 2017.
[4] Khan, Faraz Idris, et al. "A wormhole attack prevention mechanism for RPL based LLN network." Ubiquitous and Future Networks (ICUFN), 2013 Fifth IEEE International Conference, 2013.
[5] Gurunath chavan, pongle “Real time intrusion and wormhole attack detection in IOT” International journal of computer applications,vol.121,Issue.9,pp.0975-8887,(2015).
[6] David Airehour, Sayan Kumar Ray ”Securing RPL routing protocol from black hole attacks using a Trust-based mechanism” 26th International Telecommunication Networks and Applications Conference (ITNAC) 2016.
[7] ChakShu Goyal ”Detection of clone attack in mobile wireless sensors” International journal of computer application,vol.132,Issue.16,pp.51-55(2015).
[8] Shang Kuan et al,”Sybil attack and their differences in the Internet of Things”IEEE Internet of Things Journal,vol.1,Issue.5,pp.372-383(2014).
[9] Kasinathan,prabaharan “Denial of Service detection in 6LowPAN based internet of things” International conference on IEEE 2013.
[10] Lee Anhtrun et al ” The impact of internal threats towards routing protocol for low power and lossy network performance” IEEE Symposium on IEEE 2013.
[11] Anthea mayzaud et al“A study on RPL DODAG version attacks”International conference on Atonomous Infrastructure, Management and Security, vol.850, pp.92-104(2014).
[12] Anthea, Remi “Detecting version number attacks in RPL based network using a Distributed Monitoring Architecture” 12th International conference on Network and Service Management 2016.
[13] Lee Anhtrun”The impact of rank attack on network topology of routing protocol for low power and lossy networks”, IEEE Sensor Journal,vol.13,Issue.10,pp.3685-3692(2013).
[14] Annas RGHIOUI, Annas KHANNOUS “Denial of service attacks on 6LowPAN RPL networks:Threats and an intrusion detection system proposition.” Journal of Advanced Computer Science and Technologies,vol.3,Issue.2,pp.143-153,(2014).

S. Yuvarani, "A Survey: Attacks against RPL network in IoT", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.150-153, 2018.

Bioinformatics is an interdisciplinary field that uses the information technology algorithms for biological data analysis. Many tools and techniques have been investigated by the researchers for biological data interpretation, analysis and prediction. In accordance with the latest statistics, biological sequence analysis is one of the emerging areas in the field of Bioinformatics. In this paper, a survey on computational algorithms of Bioinformatics has been made. We analyzed the contributions made by the computer researchers for biological sequence analysis and survey is presented on various categories such as biological sequencing, alignment, compression and encoding, feature extraction, clustering and classification. The objective of the paper is to provide a deep understanding and knowledge regarding the existing computer algorithms used for biological data analysis and to identify the research areas for computer researchers in the field of bioinformatics.

Bioinformatics, Biological Sequences, DNA, RNA, Protein

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[5] Cao, Minh Duc, Trevor I. Dix, Lloyd Allison, and Chris Mears. "A simple statistical algorithm for biological sequence compression." In Data Compression Conference, 2007. DCC`07, pp. 43-52. IEEE, 2007.
[6] Chen, Lei, Shiyong Lu, and Jeffrey Ram. "Compressed pattern matching in DNA sequences." In Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE, pp. 62-68. IEEE, 2004.
[7] Chen, Xin, Sam Kwong, and Ming Li. "A compression algorithm for DNA sequences." IEEE Engineering in Medicine and biology Magazine 20.4 (2001): 61-66.
[8] Chen, Yang, and Jinglu Hu. "Accurate reconstruction for DNA sequencing by hybridization based on a constructive heuristic." IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 8.4 (2011): 1134-1140.
[9] Choi, Jeong-Hyeon, Hwan-Gue Cho, and Sun Kim. "GAME: a simple and efficient whole genome alignment method using maximal exact match filtering." Computational Biology and Chemistry 29, no. 3 (2005): 244-253.
[10] Choi, Kwangmin, Youngik Yang, and Sun Kim. "CLASSEQ: Classification of Sequences via Comparative Analysis of Multiple Genomes." Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on. IEEE, 2007.
[11] Fritz, Markus Hsi-Yang, Rasko Leinonen, Guy Cochrane, and Ewan Birney. "Efficient storage of high throughput DNA sequencing data using reference-based compression." Genome research 21, no. 5 (2011): 734-740.
[12] Giancarlo, Raffaele, Davide Scaturro, and Filippo Utro. "Textual data compression in computational biology: a synopsis." Bioinformatics 25.13 (2009): 1575-1586.
[13] Grumbach, Stéphane, and FarizaTahi. "Compression of DNA sequences." In Data Compression Conference, 1993. DCC`93., pp. 340-350. IEEE, 1993.
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[15] Hach, Faraz, Ibrahim Numanagić, Can Alkan, and S. CenkSahinalp. "SCALCE: boosting sequence compression algorithms using locally consistent encoding." Bioinformatics28, no. 23 (2012): 3051-3057.
[16] Heather, James M., and Benjamin Chain. "The sequence of sequencers: the history of sequencing DNA." Genomics 107.1 (2016): 1-8.
[17] Hira, Zena M., and Duncan F. Gillies. "A review of feature selection and feature extraction methods applied on microarray data." Advances in bioinformatics 2015 (2015).
[18] Kawaji, Hideya, Yosuke Yamaguchi, Hideo Matsuda, and Akihiro Hashimoto. "A graph-based clustering method for a large set of sequences using a graph partitioning algorithm." Genome Informatics 12 (2001): 93-102.
[19] Kchouk, Mehdi, and Faouzi Mhamdi. "New online hierarchical feature extraction algorithm for classification of protein." Database and Expert Systems Applications (DEXA), 2014 25th International Workshop on. IEEE, 2014.
[20] Kelil, Abdellali, Shengrui Wang, Ryszard Brzezinski, and Alain Fleury. "CLUSS: clustering of protein sequences based on a new similarity measure." BMC bioinformatics 8, no. 1 (2007): 286.
[21] Kingsford, Carl, and Rob Patro. "Reference-based compression of short-read sequences using path encoding." Bioinformatics 31, no. 12 (2015): 1920-1928.
[22] Korodi, Gergely, and IoanTabus. "An efficient normalized maximum likelihood algorithm for DNA sequence compression." ACM Transactions on Information Systems (TOIS) 23.1 (2005): 3-34.
[23] Li, Weizhong, and Adam Godzik. "Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences." Bioinformatics 22.13 (2006): 1658-1659.
[24] Liu, Libin, Yee-kin Ho, and Stephen Yau. "Clustering DNA sequences by feature vectors." Molecular phylogenetics and evolution 41.1 (2006): 64-69.
[25] Mayilvaganan, M., and R. Rajamani. "Analysis of nucleotide sequence with normal and affected cancer liver cells using Hidden Markov model." Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on. IEEE, 2014.
[26] Montanari, Piero, Ilaria Bartolini, Paolo Ciaccia, Marco Patella, Stefano Ceri, and Marco Masseroli. "Pattern similarity search in genomic sequences." IEEE Transactions on Knowledge and Data Engineering 28, no. 11 (2016): 3053-3067.
[27] Nicolae, Marius, Sudipta Pathak, and Sanguthevar Rajasekaran. "LFQC: a lossless compression algorithm for FASTQ files." Bioinformatics 31.20 (2015): 3276-3281.
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[29] Pettersson, Erik, Joakim Lundeberg, and Afshin Ahmadian. "Generations of sequencing technologies." Genomics 93.2 (2009): 105-111.
[30] Pinho, Armando J., Diogo Pratas, and Paulo JSG Ferreira. "Bacteria DNA sequence compression using a mixture of finite-context models." Statistical Signal Processing Workshop (SSP), 2011 IEEE. IEEE, 2011.
[31] Ramanujam, E., and S. Padmavathi. "Constraint frequent motif detection in sequence datasets." Advanced Computing (ICoAC), 2012 Fourth International Conference on. IEEE, 2012.
[32] Ren, Xianwen, et al. "iPcc: a novel feature extraction method for accurate disease class discovery and prediction." Nucleic acids research 41.14 (2013): e143-e143.
[33] Saha, Subrata, and SanguthevarRajasekaran. "Efficient algorithms for the compression of FASTQ files." In Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on, pp. 82-85. IEEE, 2014.
[34] Saidi, Rabie, Mondher Maddouri, and Engelbert Mephu Nguifo. "Protein sequences classification by means of feature extraction with substitution matrices." BMC bioinformatics 11.1 (2010): 175.
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[37] Tembe, Waibhav, James Lowey, and Edward Suh. "G-SQZ: compact encoding of genomic sequence and quality data." Bioinformatics 26.17 (2010): 2192-2194.
[38] Wandelt, Sebastian, and Ulf Leser. "FRESCO: Referential compression of highly similar sequences." IEEE/ACM Transactions on Computational Biology and Bioinformatics10.5 (2013): 1275-1288.
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M.Muthu Lakshmi, G.Murugeswari, "A Survey on Computational Algorithms For Biological Data Analysis", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.154-161, 2018.

In this paper, a new method for simultaneous separation of features in color images using Orthogonal Polynomials is proposed. The low-level features,edge and texture present in the color image under analysis are extracted simultaneously in frequency domain usingOrthogonal Polynomials Transformation. The transformed coefficientsobtained from Orthogonal Polynomials Transformation are categorized into color coefficients, texture coefficients and edge coefficients based on the linear contrast due to Orthogonal Polynomials Transformation in different coordinate axes. A Simplified Gradient Measure approach (SGM approach) is used to extract the edge and texture part of the color image from the categorized coefficients simultaneously after careful examination and representation of color textures. The proposed method is tested with various standard color texture images. The results obtained using this proposed feature separation method is encouraging.

Edge extraction, Feature extraction, Orthogonal Polynomials Transformation, Textureextraction

[1] Chen, LP, Liu, YG, Huang, ZX & Shi, YT, “An improved SOM algorithm and its application to color feature extraction”, Neutral Computing and Applications, Vol. 24, No. 7, pp. 1759-1770,2014.
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[7] Sanjay Kumar &Ankur Chauhan, “Feature Extraction Techniques based on Color images”, Cloud computing and Big Data, Vol-94, pp.208-214.
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R. Krishnamoorthy, S. Thennavan, R.G. Harini and K.K. Manisha Narayani, "Simultaneous Separation of Low Level Features in Color Images using Orthogonal Polynomials", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.162-166, 2018.

The main objective of this proposed effort is to advance the system that empowers recognition of Tamil characters from palm leaf and inscription through captured images and stock them for forthcoming use. Some training mechanism has done with several methodologies, but distinguishing Tamil characters stances challengeable mission. Tamil language is considered too complex compared to any other language because of the presences of curved, slope, twist, pits and it will vary writing style of individual to individual. More research needs adapting ancient Tamil characters to modern Tamil characters to extend the aim of creating computerized system for providing improved understanding of human knowledge. This proposed work is applicable for segmenting Tamil characters and store it in an organized system folder for further processing of the image. Gray-Level Co-occurrence Matrix (GLCM) feature extraction is used to quantify the statistical features of segmented characters. At this juncture segmented Tamil Characters are compared with Palm leaf manuscript, Stone Inscription, Handwritten characters and document characters using GLCM feature and the results are promising.

Gaussian, Bilateral, GLCM, PSNR, SSIM, MSE, Homogeneity, Angular Second Moment (ASM).

[1] Dr. Antony P J and Savitha C K, “A framework for recognition of handwritten south Dravidian Tulu script”, Conference on Advances in Signal Processing(CASP), IEEE, pp. 7 - 12, 2011.
[2] Mrs. G. Bhuvaneswari and Dr. V. Subbiah Bharathi, “An Efficient Positional algorithm for recognition of Ancient Stone Inscription Characters”, International Conference on Advanced Computing(ICoAC), IEEE, pp. 1 - 5, 2015.
[3] G. Janani, V. Vishalini and Dr. P. Mohan Kumar, “Recognition and Analysis of Tamil Inscriptions and Mapping using Image Processing Techniques”, International Conference on Science Technology Engineering and Management(ICONSTEM), IEEE, pp. 181 - 184, 2016.
[4] N Jayanthi and S Indu, “Inscription Image Retrieval using Bag of Visual words”, ICMAEM, pp. 1 - 7, 2017.
[5] Er. Kanchan Sharma, Er. Priyanka, Er. Aditi Kalsh and Er. Kulbeer Saini, “GLCM and its feature”, International Jorunal of Advanced Research in Electronics and Communication Engineering(IJARECE), Vol. 4, Issue. 8, pp. 2180-2182, 2015.
[6] Karthigaiselvi. M and T. Kathirvalavakumar, “Recognition of words in Tamil script using Neural Network”,International Journal of Computer Research and Apllication, Vol.7, Issue.3, pp. 62-70, 2017.
[7] A.S. Kavitha, P. Shivakumara, G.H. Kumar and Tong Lu, “Text Segmentation in degraded historical document images”, Egyptian Informatics Journal, Vol.17, Issue.2, pp. 189 - 197, 2016.
[8] Kavitha Subramani and Dr. S. Murugavalli, “A Novel Binarization method for degraded Tamil palm Leaf image”, International Conference on Advanced Computing(ICoAC), IEEE, pp. 176 - 181, 2016.
[9] P. Rajan and S. Sridhar, “Identification of Ancient Tamil Letters and Its characters: Automatic date fixation based on Contour-Let technique”, Association for Computing Machinery (ACM), pp. 40 – 43, 2017.
[10] Saikat Roy, Nibaran Das, Mahantapas Kundu and Mita Nasipuri, “Handwritten Isolated Bangla compound character recognition: A new benchmark using a novel deep learning approach”, Pattern Recognition Letters 90, Vol. 90, pp. 15 - 21, 2017.
[11] Shijin Kumar. P.S and Dharun V.S, “ Extraction of Texture features using GLCM and shape Features using connected regions”, International Journal of Engineering and Technology(IJET), Vol. 8, No. 6, pp. 2926-2930, 2017.
[12] M. Sornam and C. Vishnu Priya, “ Deep Convolutional Neural Network for Handwritten Tamil Character Recognition using Principal Component Analysis”, Intl. Conf. on Next Generation Computing Technologies (NGCT 17), University of Petroleum and Energy Studies, Dehradun,India, pp. 102-112, 2017.

M. Sornam, Poornima Devi. M, "Tamil Palm Leaf Manuscript Character Segmentation using GLCM feature extraction", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.167-173, 2018.

Maintaining privacy and security of any data, specifically for images that are transmitted through network is a major issue in today’s world. In this paper, a new three level Chaotic Shuffling Hill Pad encryption (CSHP) scheme is proposed. In first level encryption, the input image is subdivided and shuffled using chaotic algorithm. In second level, the shuffled blocks are encrypted using subset Hill algorithm with different product keys. Third level encryption is performed with the basis of Onetime pad encryption algorithm, in order to tighten the security of the image. Finally, the encrypted image is converted to text file, which is the intended cipher format. Product keys are chosen in such a way that they are invertible. The encrypted text file transmitted through insecure channel can be decrypted at the receiver’s end. The proposed method is tested with various standard images and the results are encouraging. The proposed method finds its application in Defence field.

Chaotic, Cryptosystem, Encryption, Decryption

[1] Bibhudendra Acharya, Saroj Kumar Panigrahy, Sarat Kumar Patra, and Ganapati Panda, “Image Encryption Using Advanced Hill Cipher Algorithm”, ACEEE International Journal on Signal and Image Processing Vol .1, No. 1, pp.37-41, 2010.
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[3] K. Mani, M. Viswambari “Generation of Key Matrix for Hill Cipher using Magic Rectangle”, Advances in Computational Sciences and Technology ISSN 0973-6107 Vol. 10, No. 5 pp.1081-1090, 2017.
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[6] Yaqeen S. Mezaal, Dalal A. Hammood, Mohammed H. Ali, “OTP Encryption Enhancement Based on Logical Operations”, IEEE ISBN: 978-1-4673-7504-7, pp.109-112, 2016.
[7] Prerna, Urooj, Meenakumari, Jitendra Nathshrivastava, “Image Encryption and Decryption using Modified Hill Cipher Technique”, International Journal of Information & Computation Technology. ISSN 0974-2239 Vol. 4, No. 17 pp. 1895-1901, 2014.
[8] Zhenjun Tang &Xianquan Zhang & Weiwei Lan, “Efficient image encryption with block shuffling and chaotic map” Multimed Tools Appl, New York, 2014.
[9] Zhang G, Liu Q, “A novel image encryption method based on total shuffling scheme” Optics Communication, pp. 2775–2780, 2011.
[10] Zhang Yong, “A Chaotic System Based Image Encryption Scheme with Identical Encryption and Decryption Algorithm” Chinese Journal of Electronics, Vol.26, No.5, Sept. 2017.

T. Jaison Vimalraj, P. Mithun, K. Mathiyarasu, "Cryptosystem using Chaotic Shuffling Hill pad Encryption Scheme for Secure Data Transmission", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.174-178, 2018.

To improve the data processing of the unstructured data generated, a NoSQL framework can be used to achieve better distribution of storage and analysis work of the collected big data. NoSQL is particularly helpful when a venture needs to get to and investigate huge measure of unstructured information or information that is put away on numerous virtual servers. MongoDB uses an extensive variety of methods to solve the huge information execution issues that ordinary databases were not intended to solve. Relational databases like MySQL are storing data in structured format in tables as rows and columns. This paper concentrates on the advantages of NoSQL databases over relational databases in the analysis of the big data. It mainly uses MongoDB which is one of the boosting technology of NoSQL databases and makes a performance comparison of a particular query in MySQL and MongoDB and justifies why MongoDB is preferred over MySQL.

Unstructured Data, NoSQL Databases, MongoDB, Relational Databases, MySQL

[1] Yesha Mehta, and Sanjay Buch, “Big Data Mining and Semantic Technologies: Challenges and Opportunities”, Int. J. on Recent and Innovation Trends in Computing and Communications(ISSN 2321-8169) ,Vol.3, Issue.7,pp.4907-4913, 2015.
[2] Lidong Wang ,and Cheryl Ann Alexander, ”Big Data Driven Supply Chain Management and Business Administration”, American Journal of Economics and Business Administration,Vol.7, Issue.2, pp.60-67, 2015.
[3] Dipina Damodaran B , Shirin Salim, Surekha Marium Vargese ,”Performance Evaluation of MySQL and MongoDB databases”, Int.J of Cybernetics and Informatics,Vol.5, Issue.2,pp.387-394, 2016.
[4] Sanobar Khan, Vanita Mane, “SQL support over MongoDB using metadata”. International Journal of Scientific and Research publications, Vol.3,Issue.10,pp.1-5,2013.
[5] Seyyed Hamid Aboutorabi, Mehdi Rezapour, Milad Moradi, Nasser Ghadiri, “Performance evaluation of SQL and MongoDB databases for big e-commerce data”, International Symposium on Computer Science and Software Engineering (CSSE),Tabriz, Iran,pp.72-78, 2015.
[6] Zhu Wei-ping, Li Ming-xin, Chen Huan,” Using MongoDB to implement textbook management system instead of MySQL”, IEEE 3rd International Conference on Communication Software and Networks, Xi’an, China, pp.828-830, 2011.
[7] Enqing Tang, Yushun Fan, “Performance Comparison between Five NoSQL Databases”, 7th International Conference on Cloud Computing and Big Data (CCBD), Macau, China, pp.105-109, 2016.
[8] Azhi Faraj,Bilal Rashid,Twana Shareef, ”Comparative Study of Relational and Non Relations Database Performance using ORACLE and MongoDB Systems”, International Journal of Computer Engineering & Technology,Vol.5,Issue.11,pp.11-22, 2014.
[9] Nancy Bhardwah, Gurvinder Kaur, “Greedy based privacy preserving in Data Mining using NoSQL”, International Journal of Engineering Development and Research, Vol.5, Issue.1, pp.9-17, 2017

Benymol Jose, Sajimon Abraham, Praveen Kumar V S , "Query Performance Analysis in NoSQL and Relational Dtabases: MongoDB Vs MySQL", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.179-182, 2018.

This paper aims to review the purpose of the Big Data characteristics, to identify Big Data solutions in different perspective. In 2001, the first three ‘V’ (Volume, Velocity, and Variety) dimensions of Big Data are addressed. Later, V’s like Variability, Veracity, Virality, Visualization and Value were compiled from several sources including IBM, Data Science Central, National Institute of Standards and Technology (NIST) etc.,. Recently, characteristics of Big Data increased to understand and analyze the big data efficiently and effectively. The big data and big data policy can be better revealed by adding more V’s. Addition of more V’s was providential, in the sense that big data first act in response were meet these additional challenges with this massive data. The new V’s are added to the list will provide valuable and most excellent observation over the data. Therefore, this study tries to summarize the available characteristics in the literature to get the better picture about Big Data further. From this, it has been observed that there are more than 54 V’s dimensions (characteristics) like Venue, Vocabulary, Vendible, Validity, Volatility, Verbosity, Vagueness, Vanity, Voracity and so on. These characteristics were emerged to suit different applications and domains. This review results in finding the impacts of V’s on Big data analytics.

Big Data, Analytics, V’s, Volume, Variety, Velocity, Business analytics

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S. Dhamodharavadhani, R. Gowri, R. Rathipriya, "Unlock Different V`s of Big Data for Analytics", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.183-190, 2018.