Volume-6 , Issue-10 , Oct - 2018 Go Back

This is the era of intelligent computing devices. Efforts are going on in all over the world to develop machines and programs which can solve the problems that human beings can solve with ease. One such field is recognition of handwritten characters by computers. In this paper the neural network is first trained using perceptron-learning algorithm. The target pattern is a collection of distinct patterns set for each character. While testing the target pattern is sampled and the distortion in the sampled pattern was compared with the original one. 30% or less of such distortion was considered for the identification of a particular character. The results showed that such methods produce accuracies of at least 90% and more for the hand written upper case English alphabets.

Character recognition, Sampling, Perceptron, Learning Algorithm, Neural Network

[1] L van der Maaten-2009, A New Benchmark Data Set for Handwritten Character Recognition, Available: www.tilburguniversity.nl/faculties/humanities/ticc/.../TR2009002.pdf, (Accessed: 2010, July 19th)
[2] Brown, E.W. (1993), Applying Neural Networks to Character Recognition, Available: http://www.ccs.neu.edu/home/feneric/charrecnn.html (Accessed: 2010, July 19th).
[3] Robinson, G. (1995), The Multiscale Technique, Available: http://www.netlib.org/utk/Isi/pcwLSI/text/node123.html
[4] Handwritten Character Recognition, Available: http://tcts.fpms.ac.be/rdf/hcrinuk.htm
[5] Velappa Ganapathy, and kok Leong Liew, Handwritten Character Recognition Using Multiscale Neural Network Training Technique, World Academy of Science, Engineering and Technology 39 2008
[6] Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, Dipak kumar Basu and Mahantapas Kundu, Combining Multiple Feature Extraction Techniques for Handwritten Devnagri Character Recognition, Available: http://arxiv.org/ftp/arxiv/papers/1005/1005.4032.pdf, (Accessed: 26, July, 2010).
[7] Dayashankar Singh, Sanjay Kr. Singh and Dr. (Mrs.) Maitreyee Dutta, Hand Written Character Recognition Using Twelve Directional Feature Input and Neural Network, Available: http://www.ijcaonline.org/journal/number3/pxc387173.pdf, (Accessed: 26, July, 2010).
[8] Fu Chang, Chin-Chin Lin and Chun-Jen Chen, Applying A Hybrid Method To Handwritten Character Recognition, www.iis.sinica.edu.tw/~fchang/paper/931_chang_F.pdf, (Accessed: 26, July, 2010).

Rakesh Kumar Mandal, "Hand Written English Character Recognition using Pattern Sampling Recognition Technique (PSRT)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.58-61, 2018.

Nowadays, both public and private sector organization have come to be increasingly dependent on Digital Statistics Processing. These Virtual data are going by means of an insecure channel from one location to any other location via networks and anyone can get that critical information without the knowledge of the sender. For protecting those crucial records, Cryptography plays a vital role in community protection. Many Cryptographic algorithms are applied by the Studies Community all around the world. Also they had some limitations and implemented for precise packages, Key size or Block size restricted to some minimum number of bits. This paper provides an implementation of Symmetric and Asymmetric Cryptography techniques and also it elaborates the Hybrid Cryptography techniques by fusing the Cryptographic algorithms. A comparison has been conducted for those Algorithms at different settings such as Sizes of data blocks, Throughput, Encryption Speed and Decryption speed. Finally, the Performance Metrics shows the better performance of the Hybrid Cryptographic Technique.

Cryptography, Encryption, Decryption, AES, RSA

[1]. Kruti H. Patel, Shrikant S.Patel, "Implementing Digital Signature with RSA Encryption Algorithm to Enhance the Data Security of Cloud in Cloud Computing", International Journal for Scientific Research & Development, Vol. 4, Iss. 1, 2016.
[2]. Amit Jain, Avinash Panwar, Divya Bhatnagar, "A Comparative Study of Symmetric Key Encryption Algorithms", International Journal of Advances in Engineering Research, Vol. 8, Iss. 2, 2014.
[3]. Vijay M, Sujatha R, "Intrusion Detection System to Detect Malicious Misbehavior Nodes in Manet", International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, Iss. 1, 2014.
[4]. Vishnu Priya R, Yuvarani G, "Attribute based LZ4 Encryption with Efficient Signature Verifiable Decryption", International Journal of Science Technology and Management", Vol.5, Iss.03, 2016.
[5]. Prerna Mahajan, Abhishek Sachdeva, "A Study of Encryption Algorithms AES, DES and RSA for Security", Global Journal of Computer Science and Technology, Network, Vol. 13, Iss. 15, 2013.
[6]. Ashutosh Bhargave, Niranjan Jadhav, Apurva Joshi, Prachi Oke, Prof. Mr. S. R Lahane, "Digital Ordering System for Restaurant Using Android", International Journal of Scientific and Research Publications, Vol. 3, Iss.4, 2013.
[7]. Arpit Agrawal, gunjan Patankar, "Design of Hybrid Cryptography Algorithm for Secure Communication", International Research Journal of Engineering and Technology, Vol.3, Iss. 1, 2016.
[8]. Ritu Pahal, Vikas Kumar, "Efficient Implementation of AES", International Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, Iss. 7, 2013.
[9]. Pavithra S, Ramadevi E, "Throughput Analysis of Symmetric Algorithms", International Journal of Advanced Networking and Applications", Vol. 4, Iss. 2, 2012.
[10]. Abraham Lemma, Maribel Tolentino, Gebremedhn Mehari, "Performance Analysis on the Implementation of Data Encryption Algorithms used in Network Security", International Journal of Computer and Information Technology, Vol. 4, Iss. 4, 2015.
[11]. S.Rajendirakumar, Dr.A.Marimuthu, “Cryptographic Algorithms used in Cloud Computing – An Analysis and Comparison”, International Journal for Research in Applied Science & Engineering Technology, Vol 6, Iss. 1, 2018.

S. Rajendira Kumar, A. Marimuthu, "IMPLEMENTING HYBRID CRYPTOGRAPHY ALGORITHM TO ENHANCE THE SECURITY IN CLOUD COMPUTING," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.62-68, 2018.

Today, almost everyone is part of a socialized media, whether to express opinions on any of the products of others, business organizations, industry, educational institutions, etc., so that these views or views fluctuate they are analyzed and compared with the dictionary With the help of classifying in order to better understand whether the person who is commenting on is conducive to a positive side or a negative side and may not even support either party (neutral). Basically, the sentiment analysis is where the subjective information is extracted from the original data. The popularity of Internet users and the rapid development of emerging technologies are in parallel; active use of online commentary sites, social networks and personal blog to express their views. Through natural language processing and machine learning, with other methods for working with a lot of text along the tools, you can begin to extract the sentiments of social networks. In this article, we have discussed some of the sentiments of extraction techniques; some have taken to respond to these challenges, our approach to analyze the sentiments of social networking methods.

Behavioural analysis, Clustering, Sentiment Analysis, Social media, Tweet

[1] Sunny Kumar and Paramjeet Singh, “Sentimental Analysis of Social Media Using R Language and Hadoop: Rhadoop”, 5thInternational Conference on Reliability, Infocom Technologies and Optimization (ICRITO), 2016.
[2] Gayathiri.R and Arunkumar.A, “Opinion Mining On Traffic Dataset Using Rule Based Approach”, IJCSMC, Vol. 5, Issue. 3, March 2016, pg.512 – 516.
[3] Tian-Shyug Lee and Ben-Chang Shia, “Social Media Sentimental Analysis in Exhibition’s Visitor Engagement Prediction”, American Journal of Industrial and Business Management, 2016, 6, 392-400.
[4] AmolPatwardhan by “Edge Based Grid Super-Imposition for Crowd Emotion Recognition”, Computer Vision and Pattern Recognition, 2016.
[5] Anne Veenendaal, Eddie Jones, Zhao Gang, Elliot Daly, SumaliniVartak, Rahul Patwardhan by “Fight and Aggression Recognition using Depth and Motion Data”, 2016.
[6] Mustofa Kamal, Ali RidhoBarakbah, NurRosyidMubtadai by “Temporal Sentiment Analysis for Opinion Mining of ASEAN Free Trade Area on Social Media”, Knowledge Creation and Intelligent Computing (KCIC), 2016.
[7] Shaohua Wan and J.K. Aggarwal, “Spontaneous facial expression recognition: A robust metric earning approach”, Computer Vision Research Center, The University of Texas at Austin, Austin, TX 78712-1084.
[8] J.F. Cohn and K.L.Schmidt,” The Timing of Facial Motion In Posed And Spontaneous Smiles”, international journal of wavelets, multiresolution and information processing, 2, 1-12.
[9] Dileep M R and AjitDanti, ‘Two Level Decision for Human age prediction using Neural Network”, International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 5 Issue ICICC (May 2015).
[10] Rishi Gupta and Dr. Ajay Khunteta, “SVM Age Classify based on the facial images”, International Journal of Computing, Communications and Networking, volume 1, No. 2, September- October-2012.
[11] HlaingHtakeKhaung Tin, “Perceived Gender Classification from Face Images”, I.J. Modern Education and Computer Science,2012, 1, 12-18.
[12] M. Kirby and L. Sirovich, “Application of the Karhunen-Lokve Procedure for the Characterization of Human Faces”, IEEE Transactions On Pattern Analysis And Machine Intelligence. VOL. 12, NO. I, JANUARY 1990.
[13] Damian Borth, RongrongJi, Tao Chen, Thomas Breuel, and Shih-Fu Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proceedings of the 21st ACM International Conference on Multimedia. ACM, 2013.
[14] Tao Chen, Felix X. Yu, Jiawei Chen, Yin Cui, Yan-Ying Chen, and Shih -Fu Chang. Object-based visual sentiment concept analysis and application. In Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 2014.
[15] YannLeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989.
[16] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE CVPR, 2014.
[17] D. Joshi, R. Datta, E. Fedorovskaya, Q.-T. Luong, J.Z. Wang, J. Li. and J. Luo, "Aesthetics and emotions in images," Signal Processing Magazine, IEEE, vol. 28, no. 5, 2011, pp. 94-115.
[18] S. Siersdarfer. E. Minack, F. Deng, and J. Hare. "Analyzing and predicting sentiment of images on the social web," in Proceedings of the 18th ACM international conference on Multimedia, 2010, pp. 715-718.
[19] D. Borth. R. Ji, T. Chen, T. Breue!, and S.-F. Chang, "Large-scale visual sentiment ontology and detectars using adjective noun pairs," in Proceedings of the 21st ACM international conference on Multimedia, 2013, pp. 223-232.
[20] L. P. Marency, R. Mihalcea, and P. Doshi, "Towards multimodal sentiment analysis: Harvesting opinions from the web," in Proceedings ofthe 13th international conference on multimodal interfaces, 2011, pp. 169-176.

R. Adaikkalam, A. Shaik Abdul Khadir, "Behavioural Analysis of Tweets using HFIDC Algorithm in Social Media," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.69-73, 2018.

Machine learning is to learn machine on the basis of various training and testing data and determines the results in every condition without explicit programmed. One of the techniques of machine learning is Decision Tree. Different fields used Decision Tree algorithms and used it in their respective application. These algorithms can be used as to find data in replacement statistical procedures, to extract text, medical certified fields and also in search engines. Different Decision tree algorithms have been built according to their accuracy and cost of effectiveness. To use the best algorithm in every situations of decision making is very important for us to know. This paper includes three different algorithms of Decision Tree which are ID3, C4.5 and CART.

Machine Learning, Decision Tree (DT), WEKA tool

[1]. Sorower MS. A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis. 2010 Dec;18.
[2]. Utku A, Hacer (Uke) Karacan, Yildiz O, Akcayol MA. Implementation of a New Recommendation System Based on Decision Tree Using Implicit Relevance Feedback. JSW. 2015 Dec 1;10(12):1367-74.
[3]. Gershman A, Meisels A, Lüke KH, Rokach L, Schclar A, Sturm A. A Decision Tree Based Recommender System. InIICS 2010 Jun 3 (pp. 170-179).
[4]. Jadhav SD, Channe HP. Efficient recommendation system using decision tree classifier and collaborative filtering. Int. Res. J. Eng. Technol. 2016;3:2113-8.
[5]. Beel J, Langer S, Genzmehr M, Nürnberger A. Introducing Docear`s research paper recommender system. InProceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries 2013 Jul 22 (pp. 459-460). ACM.
[6]. Zhang X, Jiang S. A Splitting Criteria Based on Similarity in Decision Tree Learning. JSW. 2012 Aug;7(8):1775-82.
[7]. Bhargava N, Sharma G, Bhargava R, Mathuria M. Decision tree analysis on j48 algorithm for data mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Jun;3(6).
[8]. Anyanwu MN, Shiva SG. Comparative analysis of serial decision tree classification algorithms. International Journal of Computer Science and Security. 2009 Jun;3(3):230-40.
[9]. Freund Y, Mason L. The alternating decision tree learning algorithm. Inicml 1999 Jun 27 (Vol. 99, pp. 124-133).
[10]. Pandey M, Sharma VK. A decision tree algorithm pertaining to the student performance analysis and prediction. International Journal of Computer Applications. 2013 Jan 1;61(13).
[11]. Priyama A, Abhijeeta RG, Ratheeb A, Srivastavab S. Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology. 2013 Jun;3(2):334-7.
[12]. Anyanwu MN, Shiva SG. Comparative analysis of serial decision tree classification algorithms. International Journal of Computer Science and Security. 2009 Jun;3(3):230-40.
[13]. Quinlan JR. Induction of decision trees. Machine learning. 1986 Mar 1;1(1):81-106.
[14]. Drazin S, Montag M. Decision tree analysis using weka. Machine Learning-Project II, University of Miami. 2012:1-3.
[15]. Banu GR. A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease. International Journal of Computer Sciences and Engineering. 2016;4(11):111-5.
[16]. Jayakameswaraiah M, Ramakrishna S. Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System. International Journal of Computer Science and EngineeringVolume-2, Issue-3 E-ISSN. 2014.

Books
[17]. Larose D.T. (2005), Discovering Knowledge in Data: An Introduction to Data Mining, Wiley.
[18]. DATA MINING WITH DECISION TREES: Theory and Applications (2nd Edition) by Lior Rokach and Oded Maimon.
[19]. Lior R. Data mining with decision trees: theory and applications. World Scientific; 2014 Sep 3.

Harsh H. Patel, Purvi Prajapati, "Study and Analysis of Decision Tree Based Classification Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.74-78, 2018.

In large organizations same data is stored on the different places by different users. This will occupy the storage space. In the duplicate removal process one can remove the file duplicate with the original file and make space empty for the further storage. It works by eliminating redundant data and ensuring that only one unique instance of the data is actually retained on storage. The data deduplication technique works by tracking each data file and eliminate each file that it found more than one copy of it in the storage. There are many techniques for deduplication. Our proposed algorithm depends on reducing the data before it`s stored in the storage or backup. Basically the procedure is the system analyses the data before storing it by one of mechanism for checking like hash value. If the system found the same data is stored already, ignore the data or document else store the data and save its analysis for future processing. There are many advantages by using this technique. No need for extra storage space.

data deuplication, classification, storage, hashing

[1] Jyoti Malhotra , Jagdish Bakal “A Survey and Comparative Study of Data Deduplication Techniques” International Conference on Pervasive Computing (ICPC)
[2] Junbeom Hur, Dongyoung Koo, Youngjoo Shin,And Kyungtae Kang “Secure Data Deduplication With Dynamic Ownership Management in Cloud Storage” IEEE Transactions on Knowledge and Data Engineering
[3] Jin Li, Xiaofeng Chen, Xinyi Huang, Shaohua Tang and Yang Xiang Senior Member, IEEE And Mohammad Mehedi Hassan Member,IEEE and Abdulhameed Alelaiwi Member,IEEE “Secure Distributed Deduplication Systems with Improved Reliability” IEEE Transactions on Computers
[4] Ms. Priyanka S. Savaji and Dr. K. H. Walse, “Survey on Data Deduplication system,” In Proceedings International Conference- EECCMC 2018, pp. 1-7, 2018.
[5] Shai Halevi, Danny Harnik, Benny Pinkas, Alexandra Shulman-Peleg “Proofs of Ownership in Remote Storage Systems” October 17–21, 2011, Chicago, Illinois, USA2011ACM.
[6] Roberto Di Pietro , Alessandro Sorniotti “Proof of ownership for deduplication systems: A secure, scalable, And efficient solution” 2016 Elsevier
[7] Pawar P.R, Aarti Waghmare “Data Deduplication in Cloud Storage”, National Conference on Advances in Computing 2015, pp 1-5, 2015.
[8] Nagapramod Mandagere, Pin Zhou, Mark A Smith, Sandeep Uttamchandani, “Demystifying Data Deduplication”, 2008 ACM, pp 12-17, 2008.
[9] Yujuan Tan, Hong Jiang, Dan Feng, Lei Tian, Zhichao Yan, Guohui Zhou “SAM: A Semantic-AwareMulti-Tiered Source De-duplication Framework for Cloud Backup” 2010 39th International Conference on Parallel Processing.
[10] K. H. Walse , Dr. R. V. Dharaskar, Manisha V. Kharat ,” Survey on Soft Computing Approaches for Human Activity Recognition”, International Journal of Science and Research (IJSR), Vol. 6, 2017, pp 1328-1334, 2017.
[11] Shai Halevi, Danny Harnik, Benny Pinkas, Alexandra Shulman-Peleg,” Proofs of Ownership in Remote Storage Systems”, 2011 ACM, pp 491-500, 2011.
[12] Yinjin F, Hong Jian, Nong Xiao, Lei Tian, Fang Liu “AA-Dedupe: An Application-Aware Source Deduplication Approach for Cloud Backup Services in the Personal Computing Environment” In IEEE International Conference on Cluster Computing 2011.pp 112-120, 2011.
[13] Iuon-Chang Lin and Po-Ching Chien,” Data Deduplication Scheme for Cloud Storage”, International Journal of Computer, Consumer and Control (IJ3C), Vol. 1, 2012, pp 26-31, 2012.
[14] R. V. Dharaskar, Ph.D, Pravin R Futane, V. M. Thakare, Ph.D,” Summarization of Own Contributory Efforts in the Field of Indian Sign Language Recognition System”, Proceedings published by International Journal of Computer Applications NCIPET-2013, pp 26-30, 2013.
[15] Amrita Upadhyay, Pratibha R Balihalli, Shashibhushan Ivaturi and Shrisha Rao “Deduplication and Compression Techniques in Cloud Design” 2012 IEEE,pp 1-6, 2012.

K. H. Walse, "A Novel Method to Improve Data Deduplication System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.79-84, 2018.

Clustering pertains to the task of finding out groups of objects such that the objects of one group are dissimilar from other groups and is similar within the same group. This work uses feature selection technique like the Document frequency Feature selection (DFFS) and feature extraction techniques like Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) were it constructs a small set of features from the original features. The newly constructed features run the K-Means algorithm without any loss of information. On several runs evaluate the accuracy for the clustering algorithms and record the results. For the obtained results, determine the cluster validation. Internal validation measures are employed to evaluate for cluster validation, based on these measures the relative validation measure is employed to determine the best clustering algorithm. Experiments are conducted for various benchmark datasets comprising of unlabelled documents and the final results prove to show that DFFS, KPCA followed by K-Means algorithm gives the best clustering results of accuracy.

Clustering,RelativeValidityMeasures,PCA,KPCA

[1] K.P. Agrawal, S.Garg, P. Patel, "Performance Measures for Densed and Arbitrary Shaped Cluster", International Journal of Computer Science & Communication, vol 6, no.2, pp.338-350, 2015.
[2] Y. Liu, Z. Li, H. Xiong, X. Gao, J. Wu,"Understanding of Internal Clustering Validation Measure", 2010 IEEE InternationalConference on Data Mining Australia, pp.911-916, 2010.
[3] S. Saitta, B. Raphael, I.F.C. Smith, "A Bounded Index for Cluster Validity", Machine Learning and Data Mining in Pattern Recognition, Springer, Heidelberg, LNAI.4571, no.1, pp.174-187, 2007
[4] Mustakim, "Centroid K-Means clustering Optimization using Eigen vector principal component analysis", Journal of Theoretical and Applied InformationTechnology , vol.95, no.15, pp.3534-3542, 2017
[5] C. Legany, S. Juhasz, A. Babos, "Cluster Validity Measurement Techniques", Proceedings of the 5th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Spain, pp.388-393, 2006.
[6] T. Karkkainen, S.Jauhiainen, "A Simple Cluster Validation Index with Maximal Coverage", ESANN 2017 proceedingsEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , i6doc.com publ, Belgium, pp.293-298, 2017.
[7] L.J.Deborah, R.Baskaran, A.Kannan, "A Survey on Internal Validity Measure for ClusterValidation", International Journal of Computer Science & Engineering Survey (IJCSES), vol.1, no.2, pp.85-102, 2010
[8] S.Jauhiainen,J.Hamalainen, T.Karkkainen, "Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering Algorithms", ArticleAlgorithms , vol.10, no.105, pp.1-14, 2017.
[9] M. Charrad,Y. Lechevallier, M.B. Ahmed, G. Saporta, ”On the Number of Clusters in Block Clustering Algorithms", Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010), pp.392-397, Florida
[10]J.Baarsch, M. EmreCelebi, "Investigation of Internal Validity Measures for K-Means Clustering", Proceedings of the Intn. Multiconference of Engineers and computer scientist, Hongkong, vol 1, 2012.
[11]A.Thalamuthu, I.Mukhopadhyay, X. Zheng, G.C. Tseng, "Evaluation and comparison of gene clustering method in microarray analysis", Bioinformatics, vol.22, no.19, pp.2405-2412, 2006.
[12]J.Schultz, L.Hubert, "Quadratic assignment as a general data analysis strategy", British Journal of Mathematical and Statistical Psychology, vol.29, no.2, pp.190-241,1976.
[13] D.W.Bouldin, D. L. Davies, "A cluster separation measure", IEEE Transaction on Pattern Analysis and Machine Intelligence PAMI-1,vol.3, no.2, pp.224-227, 1979.
[14]M. Halkidi, Y.Batistakis, M.Vazirgiannis, “Quality Scheme Assessment in the Clustering Process”, Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp.265-276, 2000
[15]M.Vazirgiannis, M.Halkidi, "Clustering validity assessment:Finding the optimal partitoning of a data set", Proceedings IEEE International Conference on data Mining, USA, pp.187-194, 2001.
[16] T. Harabasz, J. Calinski , "A dendrite method for cluster analysis", Communications in Statistics, vol.3, no.1, pp.1-27, 1974
[17] J.Dunn, "Well separated clusters and optimal fuzzy partitions", Journal of Cybernetics, vol.4, no.1, pp.95-104, 1974
[18] F. B Baker, L. J.Hubert, "Measuring the power of hierarchical cluster analysis", Journal of the American Statistical Association, vol.70, no.349, pp.31-38, 1975
[19] P.J.Rousseeuw, "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis", Journal of Computaional and Applied mathematics, vol.20, pp.53-65, 1987
[20] T.SenthilSelvi, R.Parimala, "Improving Clustering Accuracy using Feature Extraction Method", International Journal of Scientific Research in Computer Science and Engineering (isroset) ,vol.6, no. 2, pp.15-19, 2018.
[21]R Core Team, “R: A language and environment for statistical computing”, R Foundation for Statistical Computing, Vienna, Austria, pp.1-2673, 2018, https://www.R-project.org/.
[22] B. Desgraupes, ” clusterCrit: Clustering Indices”, R package, pp.1-34, 2018, https://CRAN.Rproject.org/package=clusterCrit

T. Senthil Selvi, R. Parimala, "Clustering Algorithms Validated Using Relative Index Validation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.85-95, 2018.

Self-organizing maps (SOM) are different from other artificial neural networks in the sense that they use a neighbourhood function to preserve the topological properties of the input space. It converts complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. In this works, classifying the virus type using the SOM Toolbox. Self-organizing feature maps (SOFM) learn to classify input vectors according to how they are grouped in the input space. They differ from competitive layers in that neighboring neurons in the self-organizing map learn to recognize neighbouring sections of the input space.

SOFM, ANN, Neurons, Cluster, Classification, Quantization, Visualization

[1] T. Kohonen, “The self-organizing map”, In the Proceedings of the IEEE, 78, pp. 1464–1480, 1990.
[2] A. Ultsch, “Self-Organizing Neural Networks for Visualization and Classification”, In: O. Opitzet al. (Eds). Information and Classification, Springer Berlin, pp. 301–306, 1993.
[3] J. Vesanto, “Using SOM in Data Mining”, In Licentiate`s Paper, Helsinki University of Technology, Espoo, Finland, 2000.
[4] J. A. F. Costa and M. L. de Andrade Netto, “Clustering of complex shaped data sets via Kohonen maps and mathematical morphology”, In: B. Dasarathy (Ed.), Proceedings of the SPIE, Data Mining and Knowledge Discovery, 4384, pp. 16-27, 2001.
[5] F. L. Gorgônio and J. A. F. Costa, “Parallel Self-Organizing Maps with Applications in Clustering Distributed Data”, In the International Joint Conference on Neural Networks (IJCNN’2008), 2008.
[6] S. Haykin, “Neural networks: A comprehensive foundation”, 2nd ed., Macmillan College Publishing Company, New York, 1999.
[7] T. Kohonen, “Emergence of invariant-feature de-tectors in the adaptive-subspace self-organizing map”, Biological Cybernetics, Vol.75, pp.281-291, 1996.
[8] T. Kohonen, “Self-Organizing Map”, Springer-Verlag, 1995.
[9] R. Xu and D. Wunsch II, “Survey of Clustering Algorithms”, IEEE Trans. on Neural Networks, Vol.16, Issue. 3, pp. 645-678, 2005.
[10] F. L. Gorgônio and J. A. F. Costa, “Parallel Self-Organizing Maps with Applications in Clustering Distributed Data”, In International Joint Conference on Neural Networks (IJCNN’2008), 2008.
[11] P. Berkhin, “A Survey of Clustering Data Mining Techniques”, In: J. Kogan et al., Grouping Multi-dimensional Data: Recent Advances in Clustering. Springer-Verlag, New York, 2006.
[12] T. Francis Thamburaj, “Analysis of Genome Signature Strength of SARS Coronavirus som neural network”, IEEE, 2010.

R. Vijayalakshmi, S. Isabella, "An Approach to Find the Serotypes of Rotavirus Using Self-Organizing Feature Map," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.96-100, 2018.

The research deals with prediction of Diabetes Mellitus in Pregnant and Non pregnant obese adult ladies using the data mining classification algorithms such as J48, Naïve Bayes, and SVM. Comparative study of classification algorithm is done by using Naïve Bayes, J48 and SVM. The results obtained by J48 and Naïve Bayes were found to be more satisfactory when compared to SVM classification. Naïve Bayes and J48 can be used as a predictor classifier for doing the diabetes mellitus prediction.

Medical data mining, Support Vector Machine, J48, Naïve Bayes, Diabetes mellitus

[1] D. A Kumar and R. Govindasamy,” Performance and Evaluation of Classification Data Mining Techniques in Diabetes”, International Journal of Computer Science and Information Technologies, Vol. 6 (2), 2015.
[2] Chandra. J, Nachamai.M, and A.S Pillai, “Predicting Cervical Carcinoma Stages Identification Using SVM Classifier”, International Journal of Computer Trends and Technology, Volume (22) Number( 3), 2015.
[3] C.Shah, A.G. Jivani,” Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction”, IEEE – 31661
[4] D.Tomar and S.Agarwal ,“A survey on Data Mining approaches for Healthcare”, International Journal of Bio-Science and Bio-Technology,Vol(5), No(5), pp. 241-266,2013.
[5] G.Kaur and A.Chhabra,”Improves J48 classification algorithm for prediction of diabetes”, International journal of computer Applications,Volume(98) ,No(22), July 2014.
[6] G.K.M.Nookala, B.K.Pottumuthu, N.Orsu, and S.B. Mudunuri,” Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification”, International Journal of Advanced Research in Artificial Intelligence,Vol(2), No(5), 2013.
[7] M.A. Khaleel, S. K. Pradham, G.N. Dash,” A Survey of Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume(3), Issue(8), August 2013.
[8] M.kumari, R.Vohra ,Anshularora,” Prediction of Diabetes Using Bayesian Network”, International Journal of Computer Science and Information Technologies, Volume(5), Number(4) , pp(5174-5178), 2014.
[9] N.Nandkumarsakhare, s.joshy, “Classification of Criminal Data Using J48 Decision”, International journal forum of researchers students and Academician,2012
[10]V.Karthikeyan, I.ParvinBegum, K.Tajudin, I.S.Begam, ”comparative of data mining classification algorithm(CDMCA)in diabetes disease prediction”,International journal of computer applications(0975-8887) Volume(60),No.(12),December 2012.

Manumol Thomas, Chandra J, "Diabetes Mellitus Prediction on Obese Adult Ladies Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.101-107, 2018.

The silicates of calcium are known for their thermal stability, high temperature strength, low thermal expansion, cheep residence and chemical inertness. Silicate phosphors are used for a fluorescent, a cathode-ray tube, a luminous body, a vacuum ultraviolet excitation light emitting element etc. Calcium Silicate acquires a higher luminous efficiency when it is doped with rare earth activated ions. In present work the silicate is prepared by combustion method at initiating temperature of 700-8000 C, using urea as a fuel and activated by Er3+ and Tb3+. The prepared CaSiO3:Er3+and CaSiO3:Tb3+ phosphor was characterized by X-ray diffract meter (XRD), scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), photoluminescence (PL) and thermo luminescence (TL). The chemical composition of the sintered phosphor was confirmed by EDX spectra, The PL spectra indicate that both phosphor exhibit bright green emission and with excellent colour stability. The PL broadness were typically observed in the range of 650-680 nm. The detail analysis of result it is observed the both compositions are promising green emitting phosphor for white light emitting diode (LED) application.

X-ray diffract meter (XRD), scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), photoluminescence (PL) and thermo luminescence (TL)

[1] K. S. Hong, R. S. Meltzer, B. Bihari, D. K. Williams, and B. M.Tissue, J. Lumin. 234, 76 (1998).
[2] R. N. Bhargara, D. Gallaghar, X. Hong, and A. Nurmikko, Phys. Rev. Lett. 72, 416 (1994).
[3] Haiyan Jiao and Yuhua Wang, “Ca2Al2SiO7: Ce3+, Tb3+ A White–Light Phosphor Suitable for White–Light-Emitting Diodes” Journal of the Electrochemical Society, 156 (5) J117-J120 (2009).
[4] CAI Jinjun, PAN Huanhuan and WANG Yi, “Luminescence properties of red-emitting Ca2Al2SiO7:Eu3+ nanoparticles pre- pared by sol-gel method” RARE METALS Vol. 30, No. 4, p. 374, 2011.
[5] G.J. Talwar, C.P. Joshi, S.V. Moharil Ã, S.M. Dhopte, “Combustion synthesis of Sr3MgSi2O8:Eu2+and Sr2MgSi2O7:Eu2+phosphors” Journal of Luminescence 129 1239–1241, (2009).
[6] Haoyi Wu, Yihua Hu, Guifang Ju, Li Chen, Xiaojuan Wang, Zhongfu Yang “Photoluminescence and thermo-luminescence of Ce3+ and Eu2+ in Ca2Al2SiO7 matrix” Journal of Luminescence 131, 2441–2445 (2011).
[7] E.C.S.H.D.E. Harrison, N.T. McLamed, A new family of self-activated phosphors, J. Electrochem. Soc. 110 (1963) 23.
[8] J.E.P.-H. Ping Li, I. Wei Chen, X-ray-absorption studies of zirconia polymorphs. III. Static distortion and thermal distortion, Physic Rev. B 48 (1993) 10082.
[9] F.F. Lange, Transformation toughening, J. Mater. Sci. 17 (1982) 255e263.
[10] C. Ming, F. Song, F. Wang, S. An, Y. Cai, X. Ren, et al., Luminescent characters ofEr 3 þ/Yb 3 þ co-doped ZrO 2 e Al 2 O 3 phosphors, J. Lumin 168 (2015) 59e61.
[11] R.I. Merino, J.A. Pardo, J.I. Pe~na, V.M. Orera, Microstructure-size dependence of the 1.520 mm Er[sup 3þ] luminescence lifetime in Al[sub 2]O[sub 3]eZrO[sub 2] eutectic melt growth composites, Appl. Phys. Lett. 80 (2002) 589.
[12] T. Rivera, R. Sosa, J. Azorín, J. Zarate, A. Ceja, Synthesis and luminescent characterization of sol-gel derived zirconia-alumina, Radiat. Meas. 45 (2010) 465e467.
[13] Haiyan Jiao and Yuhua Wang, “Ca2Al2SiO7: Ce3+, Tb3+ A White–Light Phosphor Suitable for White–Light-Emitting Diodes” Journal of the Electrochemical Society, 156 (5) J117-J120 (2009).
[14] R. Chen, Y. Krish., Analysis of thermally stimulated process, Pergamon, New York (1981).

Shailendra Verma, Anup Mishra, Manmeet Bhuie, Nirbhay K Singh, "Comparative analysis of luminescence Property of Tb3+and Er3+Activated Calcium Silicate Phosphor," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.108-114, 2018.

Fire detection is a technique through which fire or flame can be detected that alarm in crucial situation. Fire should be detected at real time and required action supposed to be taken immediately. Fire can be detected either by physical sensors or image processing. Some, remote area like forest requires real time detection but physical sensor cannot be placed at well that image processing is more powerful in such areas. Most of the image based recognition technique is processed through flame color detection. Flame color possesses yellow, red and orange that belongs to RGB and CMY color models. Here the proposed system focuses on flame luminance enhancement that increases the color intensity of flame through which fire can be detected with high level of accuracy. Proposed system uses HSL and CMY color models along with chromaticity pigmentation technique that allows to increase particular color intensity for higher true acceptance rate that reduces true rejection rate.

Fire Detection, Flame Luminance, Chromaticity Pigmentation, HSL, RGB and CMY color models

[1] firesafetynation.com, “Fire Detection and Alarm System”, http://firesafetynation.com/fire-detection-alarm-system/, 2018.
[2] Chaiwut Maneechot, “Fire Detection C++”, https://www.youtube.com/watch?v=bbOCYUbN2UQ, 2018.
[3] U.S. Forest Service, “Contrasting Effects of Invasive Insects and Fire on Forest Carbon Dynamics”, https://www.fs.fed.us/research/highlights/highlights_display.php?in_high_id=647, 2018.
[4] Nurul Shakira Bakri, Ramli Adnan, Abd Manan Samad, Fazlina Ahmat Ruslan, “A Methodology for Fire Detection Using Colour Pixel Classification”, In proceeding to the 2018 IEEE International Colloquium on Signal Processing & its Applications, Batu Feringghi, Malaysia, 2018.
[5] Angelo Gonzalez, Marcos D. Zuniga, Christopher Nikuli, “Accurate Fire Detection through Fully Convolutional Network”, IET Digital Library, 2017.
[6] Shruti Gupta, Lekha Doshi, “An Acknowledgement based System for Forest Fire Detection via Leach Algorithm”, In proceeding to the 2017 3rd International Conference on Computational Intelligence and Networks (CINE), Odisha, India, 2017.
[7] Kuang-Pen Chou, Mukesh Prasad, Deepak Gupta, “Block-based Feature Extraction Model for Early Fire Detection”, In proceeding to the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017.
[8] Rubayat Ahmed Khan, Jia Uddin, Sonia Corraya, “Real-Time Fire Detection Using Enhanced Color Segmentation and Novel Foreground Extraction”, In proceeding to the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 2017.
[9] Teng Wang, Lei Shi, Peng Yuan, Leping Bu, Xinguo Hou, “A New Fire Detection Method Based on Flame Color Dispersion and Similarity in Consecutive Frames”, In proceeding to the 2017 Chinese Automation Congress (CAC), Jinan, China, 2017.
[10] Oxsy Giandi and Riyanarto Sarno, “Prototype of Fire Symptom Detection System”, In proceeding to the 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2018.
[11] Khan Muhammad1, Irfan Mehmood, “Convolutional Neural Networks based Fire Detection in Surveillance Videos”, IEEE Access ( Volume: 6 ), 10.1109/ACCESS.2018.2812835, 2018.

Gokul Choudhary, Pankaj Pandey, "Flame Luminance Enhancement using Chromaticity Pigmentation for Real Time Fire Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.115-120, 2018.