Volume-7 , Issue-5 , May - 2019 Go Back

In the recent years, the use of cloud computing was found in broader perspectives. So it is the security of these extensive systems where fire and theft of personal information was at risk. In spite of one unit use, many primary intruding tools are in the cloud, but they are effective in the last resort. For Avoiding or quicker than intensive conditions, clouds should focus solely on known threats, but to avoid repetition. This paper involves a relationship of mutual understanding the detection of violence in the high infrastructure. This method is used to enhance the effectiveness of revealing potentials and enhancement in the ability to provide carrier. In all this paper we want us to be part of the discovery technique, the behavioral block, and natural analysis and prevention. In practice of implementing this mechanism, we understand that the worst of the malware software is compared to an antivirus engine and eight hundred and eighty-eight eyes around the globe.

Cloud computing, threats, antivirus, security, deployability, resilience, malware

[1]. Bo Li, Eul Gyu ”signature policy options for antivirus" Physics and Computer Engineering computer programs, Hanian University, Seoul, Korea. © IEEE 2011
[2]. С. Subashini, V. Kavitha SL "security issues in the condition delivery model of the cloud computing." Direct Science, Network Magazine and Computer Programs, pp.
[3]. Scott Treadwell, Mian Chou "Mechanism for Determining the Last Challenge", American Bank, 1201 Main St, Dallas, TX 75202, © IEEE 2009.
[4]. 4. Lajos Nagy, funded were Ford, and William Allen, N-version program for the detection of daytime failures, the World Conference on Cyber Securit

Ashok Koujalagi, D.K Sreekantha, "Real time pernicious Detection in Cloud computing Using Service-oriented Architecture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1038-1043, 2019.

In recent years, as our social media has become more and more prevalent. The news websites and blogs have become into the limelight, there are number of web pages and social media that have come into the state that claim to report on upcoming events, but whose reliability has been brought up into question. Now the debate over such websites and news agencies has become so prevalent that the issue of ‘fake news’ is itself an vital part of the news world. What establishes `fake news,` in any case, has just turned out to be less clear as the topic has turned out to be increasingly normal, with standard news sources. Nowadays` fake news is making various issues from mocking articles to a created news and plan government publicity in certain outlets. fake news and absence of trust in the media are developing issues with immense consequences in our general public. It is needed to look into how the techniques in the fields of computer science using machine learning, natural language processing helps us to detect fake news. Fake news is now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. In this research a comprehensive way of detecting fake news using machine learning model has been presented that is trained by two different data which is based on US election fake news and recent Indian political fake news respectively.

Machine Learning, Fake News, Data Cleaning, Classification Model, Text processing, Natural Language Toolkit

[1] E. Alpaydin, "Introduction to machine learning", MIT press, 2009.
[2] Kotsiantis,B.Sotiris, I. Zaharakis,P.Pintelas, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explorations Newslette, vol. 19, no. 1, pp. 22-36, 2017.
[3] Conroy,J. Niall,L. Victoria L. Rubin,Y. Chen, “Automatic deception detection: Methods for finding fake news,” In the Proceedings of the 2015 Association for Information Science and Technology, vol. 52, no. 1, pp. 1--4, 2015.
[4] S. B. I. Z. a. P. P. Kotsiantis, “Supervised machine learning: A review of classification techniques,” Emerging artificial intelligence applications in computer engineering , vol. 160, pp. 3-24, 2007.
[5] M. a. J. P. Stevanovic, “An efficient flow-based botnet detection using supervised machine learning,” In the Proceedings of the 2014 International Conference on Computing, Networking and Communications, pp. 797-801, 2014.
[6] S. Koley, E. Koley,S. Ghosh,S. G. Shukla, “Detection and Classification of Open Conductor Faults in Six-Phase Transmission Line Using Wavelet Transform and Naive Bayes Classifier,” International Conference on Computational Intelligence and Computing Research, pp. 1-6, 2017.
[7] W. Y. Wang, “liar, liar pants on fire": A new benchmark dataset for fake news detection,” arXiv preprint , vol. arXiv:1705.00648, pp. 12-13, 2017.
[8] Ruchansky, Natali, S.Seo,Y. Liu, “Csi: A hybrid deep model for fake news detection” In the Proceedings of the 2017 Information and Knowledge Management. ACM, 2017.
[9] M. M. V. Y. a. A. Granik, “Determining Fake Statements Made by Public Figures by Means of Artificial Intelligence” International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 424-427, 2018.
[10] A. P. Flach, “The geometry of ROC space: understanding machine learning metrics through ROC isometrics” In the Proceedings of the 2003 20th International Conference on Machine Learning (ICML-03), pp. 194-201, 2003.
[11] H. a. A. G. Bhavsar, “A comparative study of training algorithms for supervised machine learning,” nternational Journal of Soft Computing and Engineering (IJSCE), vol. 2.4, pp. 2231-2307, 2012.

Anant Kumar, Satwinder Singh, Gurpreet Kaur, "Analyzing And Detecting The Fake News Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1044-1050, 2019.

Due to Access of Internet and social media now a days, everyone, irrespective of age and area of concern is able to express his opinions regarding any entity, which can be a product, an article, a blog or just a simple tweet. These reviews thus plays a major role for marketers, customers or product analysts in creating opinions regarding a particular entity. This has led to creation of 2.5 quintillion bytes of data every day. Sentiment analysis or opinion mining is a branch of data mining which deals with the study of opinions and sentiments of the peoples which are expressed over internet. This paper presents a detailed study of approaches made so far for opinion mining, the comparison of data mining techniques and algorithm and their accuracy on various data sets. The paper also include various challenges that may be faced during the analysis of opinionated data.

Data mining, Knowledge discovery, Opinion Mining, Polarity check, Sentiment analysis

[1]Neha Raghuvanshi, J.M. Patil , “A Brief Review on Sentiment Analysis”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), India,2016,IEEE
[2] Jawad khan, Byeong soo jeong, “Summarizing customer review based on product feature and opinion” Proceedings of the 2016 International Conference on Machine Learning and Cybernetics, Jeju, South Korea, 10-13 July, 2016, IEEE
[3] Premnarayan Arya, Dr.Amit Bhagat, “Deep Survey on Sentiment Analysis and Opinion Mining on Social Networking Sites and E-Commerce Website” , International Journal of Engineering Science and Computing, March 2017 Volume 7 Issue No.3
[4] Harpreet Kaur, Veenu Mangat, “A Survey of Sentiment Analysis techniques” International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2017)
[5] Ms.K.Mouthami ,Ms.K.Nirmala Devi, Dr.V.Murali Bhaskaran, “Sentiment Analysis and Classification Based On Textual Reviews” International Conference on Information Communication and Embedded Systems (ICICES) ,India, Pages 1-622, 2013
[6] Jawad khan, byeong soo jeong “Summarizing customer review based on product feature and opinion” Proceedings of the 2016 International Conference on Machine Learning and Cybernetics, Jeju, South Korea, 10-13 July, 2016
[7] Ms.A.M.Abirami , Ms.V.Gayathri “A survey on sentiment analysis methods and approach” 2016 IEEE Eighth International Conference on Advanced Computing (ICoAC)
[9] Gautami Tripathi, Naganna.S, “Feature Selection and Classification approach for Sentiment Analysis”, An International Journal, vol.2, pp.1-16, (2015).
[10] Rui Xia, Feng Xu, Chengqing Zong, Qianmu Li, Yong Qi, and Tao Li, “Dual Sentiment Analysis: Considering Two Sides of One Review”, IEEE Transactions on Knowledge and Data Engineering, MANUSCRIPT ID, 1041-4347 (c) 2015 IEEE.
[11] Onam Bharti,z Monika Malhotra ,”Sentiment analysis on twitter data”, IJCSMC, Vol. 5, Issue. 6, June 2016, pg.601 – 609
[12] Akshay Amolik, Niketan Jivane, Mahavir Bhandari, Dr.M.Venkatesan,” Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques.”, International Journal of Engineering and Technology (IJET) Vol 7 No 6 Dec 2015-Jan 2016. Pg 2038-2044
[13] Jaspreet Singh , Gurvinder Singh and Rajinder Singh,” Optimization of sentiment analysis using machine learning classifiers” Human-centric Computing and Information Sciences 2017, Springer. https://doi.org/10.1186/s13673-017-0116-3
[14] Raheesa Safrin, K.R.Sharmila, T.S.Shri Subangi, E.A.Vimal ,” SENTIMENT ANALYSIS ON ONLINE PRODUCT REVIEW” Volume: 04 Issue: 04 | Apr -2017 IRJET Pg 2381-2388
[15] Naw Naw (2018); Twitter Sentiment Analysis Using Support Vector Machine and K-NN Classifiers; International Journal of Scientific and Research Publications (IJSRP) 8(10) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.8.10.2018.p8252
[16] H. M. Keerthi Kumar, B. S. Harish, H. K. Darshan. Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method, International Journal of Interactive Multimedia and Artificial Intelligence, (2018), http://dx.doi.org/10.9781/ijimai.2018.12.005
[17] Bharat Gaind, Varun Syal, Sneha Padgalwar “Emotion Detection and Analysis on Social Media”,proceedings of International Conference on Recent Trends In Computational Engineering and Technologies (ICTRCET’18), May 17-18, 2018, Bengaluru, India.
[18] Ali Hasan , Sana Moin , Ahmad Karim and Shahaboddin Shamshirband,” Machine Learning-Based Sentiment Analysis for Twitter Accounts “Mathematical Computer Application. 2018, 23, 11; doi:10.3390/mca23010011
[19] Siddu P. Algur, Jyoti G. Biradar, “Opinion Mining and Review Spam Detection: Issues and Challenges” IJARCSSE Volume 7, Issue 1, January 2017 DOI:10.23956/ijarcsse/V7I1/0170
[20] Ketan Sarvakar, Urvashi K Kuchara, "Sentiment Analysis of movie reviews: A new feature-based sentiment classification", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.8-12, 2018
[21] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017

Jasneet Kaur , "A Brief Study on Sentiment Analysis & Opinion Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1051-1056, 2019.

Nowadays, the huge set of spatio-temporal data (STD)are increasingly collected and utilized in different domains that include social sciences, epidemiology, mobile health, climate science, neuroscience, transportation and Earth sciences. Compared to relational data, the STD is different for that the researcher developed computational techniques in the data mining community. The process of extracting implicit knowledge and unknown information, structures in spatio-temporal (ST)dataset , patterns that are not explicitly stored and spatio-temporal relationships is called Spatio-temporal data mining (STDM). As one of information mining procedures, data prediction approach is widely utilized toward forecast the unknown future on the basis of hidden patterns in the past and current data. To obtain ST forecasting, few of them developed analysis tools like spatial information are prolonged to temporal dimension (TD)as well as the time series extended to the spatial dimension (SD) or joined linearly as a ST combination. But, such kind of linear combination of TD and SD is a generalization of difficult ST relations which is present in complex geographical occurrences. The present study, reviews the traditional STDM approach, tools, pattern mining approach and data analysis. On the basis of data mining issues, this literature has been classified into four main categories: trajectory mining approach, clustering, pattern mining, predictive learning and location prediction. We discourse the different forms of STDM issues in each of these groups.

Spatio-Temporal Data, Trajectory Clustering and Mining Approach, Location Prediction, Trajectory Pattern Analysis

[1] De Almeida, V.T. & Güting, R.H. (2005). Indexing the Trajectories of Moving Objects in Networks*. Geoinformatica. [Online]. 9 (1). Pp. 33–60. Available from: http://link.springer.com/10.1007/s10707-004-5621-7.
[2] Altche, F. & de La Fortelle, A. (2017). An LSTM network for highway trajectory prediction. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). [Online]. October 2017, IEEE, pp. 353–359. Available from: http://ieeexplore.ieee.org/document/8317913/.
[3] Aryal, A.M. & Sujing Wang (2017). Discovery of patterns in spatio-temporal data using clustering techniques. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC). [Online]. June 2017, IEEE, pp. 990–995. Available from: http://ieeexplore.ieee.org/document/7984703/.
[4] Bahadori, M.T., Yu, Q. (Rose) & Liu, Y. (2014). Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. Nips. [Online]. Pp. 1–9. Available from: https://pdfs.semanticscholar.org/82e5/c9a1ccb59b7ca6952d1f2741881c3a15ab57.pdf.
[5] Bao, J., Zheng, Y. & Mokbel, M.F. (2013). Location-based and preference-aware recommendation using sparse geo-social networking data. [Online]. Pp. 199. Available from: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/locationrecommendation.pdf.
[6] Bengio, Y. (2010). Neural net language models. Scholarpedia.
[7] Bock, J., Beemelmanns, T., Klösges, M. & Kotte, J. (2017). Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections. [Online]. (Vehits). Pp. 346–351. Available from: https://www.scitepress.org/papers/2017/63740/63740.pdf.
[8] Bogorny, V., Kuijpers, B. & Alvares, L.O. (2009). ST‐DMQL: A Semantic Trajectory Data Mining Query Language. International Journal of Geographical Information Science. [Online]. 23 (10). Pp. 1245–1276. Available from: http://www.tandfonline.com/doi/abs/10.1080/13658810802231449.
[9] Bolstad, P. (2005). Gis Fundamentals: A First Text on Geographic Information Systems. [Online]. Eider Press. Available from: https://books.google.co.in/books?Id=cfheaaaayaaj.
[10] Cao, H., Mamoulis, N. & Cheung, D.W. (2007). Discovery of Periodic Patterns in Spatiotemporal Sequences. IEEE Transactions on Knowledge and Data Engineering. 19 (4). Pp. 453–467.
[11] Catlett, C., Cesario, E., Talia, D. & Vinci, A. (2018). A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities. In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP). [Online]. June 2018, IEEE, pp. 17–24. Available from: https://ieeexplore.ieee.org/document/8421327/.
[12] Chen, L., Lv, M., Ye, Q., Chen, G. & Woodward, J. (2011). A personal route prediction system based on trajectory data mining. Information Sciences. [Online]. 181 (7). Pp. 1264–1284. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0020025510005888.
[13] Cheng, T. & Adepeju, M. (2014). Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection. Plos ONE.
[14] Elhorst, J.P. (2003). Specification and estimation of spatial panel data models. International Regional Science Review.
[15] Ester, M., Kriegel, H.-P., Jorg, S. & Xu, X. (1996). A Density-Based Clustering Algorithms for Discovering Clusters. Kdd. [Online]. 96 (34). Pp. 226–231. Available from: https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf.
[16] Ganguly, A.R. & Steinhaeuser, K. (2008). Data Mining for Climate Change and Impacts. In: 2008 IEEE International Conference on Data Mining Workshops. [Online]. December 2008, IEEE, pp. 385–394. Available from: http://ieeexplore.ieee.org/document/4733959/.
[17] Giannotti, F., Nanni, M. & Pedreschi, D. (2006). Efficient Mining of Temporally Annotated Sequences. In: Proceedings of the 2006 SIAM International Conference on Data Mining. [Online]. 20 April 2006, Philadelphia, PA: Society for Industrial and Applied Mathematics, pp. 348–359. Available from: https://epubs.siam.org/doi/10.1137/1.9781611972764.31.
[18] Ignaccolo, R., Mateu, J. & Giraldo, R. (2014). Kriging with external drift for functional data for air quality monitoring. Stochastic Environmental Research and Risk Assessment.
[19] Janaki, V.R.; P.V.K.; V. (2016). Mining of Outlier Temporal Patterns. In: 2016 International Conference on Engineering & MIS (ICEMIS). 2016, Agadir, Morocco: IEEE Comput. Soc. Press.
[20] Jeung, H., Liu, Q., Shen, H.T. & Zhou, X. (2008). A Hybrid Prediction Model for Moving Objects. In: 2008 IEEE 24th International Conference on Data Engineering. [Online]. April 2008, IEEE, pp. 70–79. Available from: http://ieeexplore.ieee.org/document/4497415/.
[21] Jeung, H., Shen, H.T. & Zhou, X. (2007). Mining Trajectory Patterns Using Hidden Markov Models. In: Data Warehousing and Knowledge Discovery. [Online]. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 470–480. Available from: http://link.springer.com/10.1007/978-3-540-74553-2_44.
[22] Kurashima, T., Iwata, T., Irie, G. & Fujimura, K. (2010). Travel route recommendation using geotags in photo sharing sites. Pp. 579.
[23] Lathia, N., Hailes, S. & Capra, L. (2009). Temporal collaborative filtering with adaptive neighbourhoods. [Online]. University College London. Available from: http://www0.cs.ucl.ac.uk/staff/n.lathia/papers/lathia_sigir09.pdf.
[24] Lei, P.-R., Li, S.-C. & Peng, W.-C. (2012). QS-STT: quadsection clustering and spatial-temporal trajectory model for location prediction. [Online]. Available from: https://www.researchgate.net/publication/232703396_QS-STT_quadsection_clustering_and_spatial-temporal_trajectory_model_for_location_prediction.
[25] Lei, P.R., Shen, T.J., Peng, W.C. & Su, I.J. (2011). Exploring spatial-temporal trajectory model for location prediction. In: Proceedings - IEEE International Conference on Mobile Data Management. 2011.
[26] Li, J., Qin, Q., Han, J., Tang, L.-A. & Lei, K.H. (2015). Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference. Transactions in GIS. [Online]. 19 (1). Pp. 1–18. Available from: http://doi.wiley.com/10.1111/tgis.12072.
[27] Li, X., Yu, C., Ju, L., Qin, J., Zhang, Y., Dou, L. & Yuqing, S. (2018). Position prediction system based on spatio-temporal regularity of object mobility. Information Systems. [Online]. 75. Pp. 43–55. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0306437917304726.
[28] Li, Z., Chen, J. & Baltsavias, E. (2008). Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book. ISPRS Book Series. [Online]. CRC Press. Available from:https://books.google.co.in/books?Id=-fqm3buoejcc.
[29] Li, Z., Ding, B., Han, J., Kays, R. & Nye, P. (2010).Mining periodic behaviors for moving objects. [Online]. University of Illinois at Urbana-Champaign. Available https://faculty.ist.psu.edu/jessieli/Publications/KDD10-zli-periodmovobj.pdf.
[30] Li, Z., Han, J., Ding, B. & Kays, R. (2012). Mining periodic behaviors of object movements for animal and biological sustainability studies. Data Mining and Knowledge Discovery. [Online]. 24 (2). Pp. 355–386. Available from: http://link.springer.com/10.1007/s10618-011-0227-9.
[31] Liu, Q., Wu, S., Wang, L. & Tan, T. (2017). Predicting the Next Location : A Recurrent Model with Spatial and Temporal Contexts. Aaai. [Online]. Pp. 194–200. Available from: https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11900/11583

N. Venkata Subba Reddy, D. S. R. Murthy, "Spatio-Temporal Neural Network Approach for Location Prediction: State-of-the-Art, Challenges and Future Directions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1057-1067, 2019.

Domain name is meant to locate the web address on internet but in today’s era domain name has gone very far from the purpose of domain name. Now generally domain names are used to identify the goods and services which a particular manufacture, company and service provider is offering be it offline or online. As per prevalent practices registration of domain name being not as stringent as that of trade marks lead to the practice of cybersquatting. In plethora of judgments Courts have responded positively to have resolved the disputes applying the principles of trade mark and passing off laws. It automatically speaks that domain name and trade mark are different concepts but on the ground of unavailability of the law in pertain to domain name cyber squatters may not be allow to play with the laws. The paper covers lack of laws in India, history of cyber squatters, cyber squatters position in India and working of ICANN and ACPA.

Trademarks, Domain name, Cyber squatters, GTLD, ICANN and ACPA

[1]. J. P. Mishra “INTELLECTUAL PROPERTY RIGHTS”, Central Law Publications, India, pp. 265-274, 2012.
[2]. Steven Wright, “Cybersquatting at the Intersection of Internet Domain names and Trade mark Law”, IEEE Communications Surveys & Tutorials, Vol.14, No.1, First Quarter 2012.

Mausam Thaker, A.K. Tripathi, Ravi Sheth, "Domain name vis-à-vis Trademark in reference to Cyber Squatters," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1068-1075, 2019.

Clustering is one of the main diagnostic method in data mining, widely used in cluster analysis having higher efficiency and scalability when dealing with large data sets. So far, numerous useful clustering algorithms have been developed for large databases, such as Connectivity based clustering [1], Centroid based clustering [2], Distribution based clustering[3] and Density based clustering[4]. K-means clustering algorithm was proposed by MacQueen [5] which is a Centroid based cluster analysis method. However there are some limitations of standard K-means algorithm: initialization of cluster centers, how K-means clustering algorithm calculates the distance between each data objects and cluster centers in each iteration. This paper proposes an improved K-means algorithm which first preprocesses the data and then arranges the dataset in a sequential order thus reducing the number of iterations and complexity. In preprocessing, the noisy data is removed and the resultant data undergoes the improved process of sorting and clustering which controls the computing of distance with each data object to the cluster centers iteratively, saving the execution time. Experimental results show that the improved method can effectively advance the speed of clustering and accuracy, reducing the computational complexity of the K-means.

Data mining, Clustering, K-means, improved K-means

[1] Jianhua Li and Laleh Behjat, “A Connectivity Based Clustering Algorithm With Application to VLSI Circuit Partitioning”, IEEE Transactons On Circuis and Systems-II: Express Briefs, Vol.53, No. 5, May 2006.
[2] Lifei Chen , Shengrui Wang, Xuanhui Yan, “Centroid-based clustering for graph datasets.”, 21st International Conference on Pattern Recognition , November 11-15, 2012.
[3] Xiaowei Xu, Martin Ester, Hans-Peter Kriegel, Jörg Sander , “A distribution-based clustering algorithm for mining in large spatial databases”, Proceedings 14th International Conference on Data Engineering, 06 August 2002.
[4] Ester M., Kriegel H., Sander J., Xiaowei Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, KDD’96, Portland, OR, pp.226-231, 1996.
[5] J.MacQueen, “Some methods for classification and analysis of multivariate observations.”, In Proc. 5th Berkeley Symp. Math. Stat. Prob., 1:281–297, Berkeley, CA, 1967.
[6] Kaufman L. and Rousseeuw P. J., “Finding Groups in Data: An Introduction to Cluster Analysis”, John Wiley & Sons, 1990.
[7] Raymond T. Ng and Jiawei Han, “CLARANS: A Method for Clustering Objects for Spatial Data Mining”, IEEE Transactions On Knowledge and Data Engineering, Vol. 14, No. 5, SEPTEMBER 2002.
[8] Zhang T, Ramakrishnan R., Livny M., “ BIRCH: An efficient data clustering method for very large databases”, In: SIGMOD Conference, pp.103~114, 1996.
[9] Guha S, Rastogi R, Shim K., “ CURE: An efficient clustering algorithm for large databases”, In: SIGMOD Conference, pp.73~84, 1998.
[10] Ankerst M., Markus M. B., Kriegel H., Sander J., “OPTICS: Ordering Points To Identify the Clustering Structure”, Proc.ACM SIGMOD’99 Int. Conf. On Management of Data, Philadelphia, PA, pp.49-60, 1999.
[11] Fahim A M,Salem A M,Torkey F A, “An efficient enhanced K-means clustering algorithm” Journal of Zhejiang University Science A, Vol.10, pp:1626-1633,July 2006.
[12] Sun Jigui, Liu Jie, Zhao Lianyu, “Clustering algorithms Research”,Journal of Software ,Vol 19,No 1, pp.48-61,January 2008.
[13] Zhe Zang, Junxi Zhang, Huifeng Xue, “Improved K-means clustering algorithm”, Congress on Image and Signal Processing, IEEE DOI 10.1109/CISP.2008.
[14] Malay K. Pakhira, “A modified K-means algorithm to avoid empty clusters” International Journal of Recent Trends in Engineering, Vol 1, No. 1, May 2009 .
[15] Shi Na, Liu Xumin, Guang Yong, “Research on K-means clustering algorithm: An improved K-means clustering algorithm ” Third International Symposium on Intelligent Information Technology and Security Informatics, IEEE, DOI 10.1109/IITSI.2010.
[16] Mumtaz, Dr. K. Duraiswamy, “A novel density based improved K-means clustering algorithm- Dbkmeans” International Journal on Computer Science and Engineering ISSN : 0975-3397 213 Vol. 02, No. 02, 2010, 213-218.
[17] Juntao Wang, Xiaolong Su, “An improved K-means clustering algorithm” IEEE, 3rd ICCSN International Conference on Communication Software and Networks, 2011.
[18] Navjot Kaur, Jaspreet Kaur Sahiwal, Navneet Kaur, “Efficient K-means clustering algorithm using ranking method in datamining” ISSN: 2278 – 1323, International Journal of Advanced Research in Computer Engineering & Technology ,Volume 1, Issue 3, May 2012.
[19] Shyr-Shen Yu , Shao-Wei Chu , Chuin-Mu Wang , Yung-Kuan Chan , Ting-Cheng Chang, “Two Improved K-means Algorithms”, Applied Soft Computing Journal ,2017.

B.S. Rawat, K. Kumar, R.K. Mishra, S.S Bedi, "Rapid Clustering Algorithm for Optimizing Cognate Data of Online Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1076-1082, 2019.

The expansion in mixed media administrations has put vitality saving money on the highest point of current requests for cell phones. Shockingly, batteries` lifetime has not been as reached out as it would be alluring. Hence, lessening vitality utilization in each undertaking perform by this gadgets essential. In this work, a novel artful steering convention, called JOKER has been presented. The opinion present in both the applicant choice and coordination stages, has increased the performance in network supporting sight and sound traffic In the proposed system client has to send the request the videos to file server, and file server takes responsibility for distributing the frame to the neighbors. In this system we are considering the two neighbors, neighbour1 and neighbor2 and both neighbors are receiving the frames from file server alternatively, finally client receiving the videos from the neighbors. By using this proposed system we can reduce the time consuming between sender and user

Opportunistic routing, QoE, SOAR, ad-hoc networks, JOKER

[1] Hussain. Ramon sanchez-iborra and mariadolorescano. JOKER: A Novel Opprtunistic ROUTING Protocol. IEEE journal on selected areas in commun., VOL. 34, NO. 5, MAY 2016
[2] E. Rozner,SOAR: Simple Opportunistic Adaptive Routing Protocol for Wireless Mesh Networks, WIMESH06.
[3] Zhu, Hua, and Kejie Lu. "Resilient opportunistic forwarding: Issues and challenges." Military Communications Conference, 2007. MILCOM 2007. IEEE. IEEE, 2007.
[4] Yang, Shengbo,. "Position based opportunistic routing for robust data delivery in MANETs." Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE. IEEE, 2009
[5] S. Chachulski, M. Jennings, S. Katti, and D. Katabi, “Trading Structure for Randomness in Wireless Opportunistic Routing,” Proc. ACM SIGCOMM, Aug. 2007
[6] W.L. Rodolfo, A. Boukerche, F.M. Luiz, and A.F.L. Antonio “Transmission power control-based opportunistic routing for wireless sensor networks,” in Proceedings of the 17th ACM international conference on Modeling, Analysis and simulation of wireless Montreal, Canada, September. 2014, pp: 219-226.
[7] H. Liu, B. Zhang, H.T. Mouftah, X. Shen, J. Ma, “Opportunistic routing for wireless ad-hoc and sensor networks: Present and future directions,” IEEE Communication
[8] M. Zhao, A. Kumar, P.H.J. Chong, R. Lu, “A Reliable and Energy-Efficient Opportunistic Routing Protocol for Dense Lossy Networks,” IEEE Wireless Communications Letters, vol. 6, no. 1, 2017, pp: 26-29.[
9] R. Sanchez-Iborra, M.D. Cano, “JOKER: A Novel Opportunistic Routing Protocol,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, 2016, pp: 1690-1703.
[10] S, Wang, A, Basalamah, S.M. Ki Correlation-Aware Opportunistic Routing in Wireless Networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 1, 2015, pp: 47-56.
[11] S. Joseph , N. Srikanth , J. E. N. Abhilash, “A Novel Approach of Modified Run Length Encoding Scheme for High Speed Data Communication Application”, International Journal of Science and Research (IJSR), Vol.2, No.12, 2013.

Soundarya K Sajjan, Deepak G, "Efficient Video Streaming Using JOKER an Opportunistic Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1083-1087, 2019.

Security is the main aspect of any communications among untrusted networks in the current world. Thanks to many researchers for their enormous contributions on effective security algorithms despite different attacks breaching the weakness on the computer systems. As always, execution of these algorithms used to take time because of their dependency on mathematical analysis. The more the complexity of mathematical model, the more is the robustness of the algorithm though. Good number of them proved to be strong and are used in many popular applications over internet systems for providing security. There were a few of some methods which used very less of the mathematical concepts. One of them and the most widespread schemes is Visual Cryptography. This considers images as one of the important element in its methodology. However, this notion is different from other imagery concepts used in security. The below information gives reader a clear view on visual cryptographic schemes available, and also it provides an understanding on different heterogeneous applications oriented to the same. This paper also focuses on an application oriented aspect of Data Provenance with respect to secure communication.

Visual Encryption, Data Provenance, Cyber Security, Attack Investigations

[1] Jithi, P. V., & Nair, A. T. (2013, March). Progressive visual cryptography with watermarking for meaningful shares. In Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on (pp. 394-401). IEEE.
[2] Basavegowda, R., & Seenappa, S. (2013, April). Electronic Medical Report Security Using Visual Secret Sharing Scheme. In Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference on (pp. 78-83). IEEE.
[3] Pope, C., Kaur, K.: ‘Is it human or computer? Defending e-commerce with CAPTCHAs’, IT Professional, 2005, 7, (2), pp. 43–40
[4] CAPTCHA: Telling Humans and Computers Apart Automatically. Available at: http://www.captcha.net/
[5] Lee, J. S., & Hsieh, M. H. (2013). Preserving user-participation for insecure network communications with CAPTCHA and visual secret sharing technique.IET networks, 2(2), 81-91.
[6] Wu, X., & Sun, W. (2013). Generalized random grid and its applications in visual cryptography.
[7] Zhong, G. S., & Wang, J. J. (2013, May). Region incrementing visual secret sharing scheme based on random grids. In Circuits and Systems (ISCAS), 2013 IEEE International Symposium on (pp. 2351-2354). IEEE.
[8] Yang, Ching-Nung, and Daochun Wang. "Property Analysis of XOR Based Visual Cryptography." (2014): 1-1.
[9] Daoshun, W., et al. "Optimal Contrast Greyscale Visual Cryptography Schemes with Reversing." (2013): 1-1.
[10] Chiu, P. L., Lee, K. H., Peng, K. W., & Cheng, S. Y. (2013, July). User-friendly visual cryptography with complementary cover images. In Signal and Information Processing (ChinaSIP), 2013 IEEE China Summit & International Conference on (pp. 641-644). IEEE.
[11] Tan, X., & Zhang, Q. (2013, September). A Kind of Verifiable Visual Cryptography Scheme. In Emerging Intelligent Data and Web Technologies (EIDWT), 2013 Fourth International Conference on (pp. 215-219). IEEE.
[12] Iwamoto, M. (2012). A weak security notion for visual secret sharing schemes. Information Forensics and Security, IEEE Transactions on, 7(2), 372-382.
[13] Premkumar, S., & Narayanan, A. E. (2012, March). New visual Steganography scheme for secure banking application. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on (pp. 1013-1016). IEEE.
[14] Lin, K. T. (2012, July). Based on Binary Encoding Methods and Visual Cryptography Schemes to Hide Data. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2012 Eighth International Conference on (pp. 59-62). IEEE.
[15] Surekha, B., Swamy, G., & Reddy, K. R. L. (2012, July). A novel copyright protection scheme based on Visual Secret Sharing. In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on (pp. 1-5). IEEE.
[16] Prema, G., & Natarajan, S. (2013, February). Steganography using Genetic Algorithm along with Visual Cryptography for wireless network application. In Information Communication and Embedded Systems (ICICES), 2013 International Conference on (pp. 727-730). IEEE.
[17] W.P. Fang and J.C. Lin, “Visual cryptography with extra ability of hiding confidential data,” Journal of Electronic Imaging , Vol. 15, No. 2, pp.0230201–023020 7, 2006.
[18] Huang, Y. J., & Chang, J. D. (2013, February). Non-expanded visual cryptography scheme with authentication. In Next-Generation Electronics (ISNE), 2013 IEEE International Symposium on (pp. 165-168). IEEE.
[19] Kaur, K., & Khemchandani, V. (2013, February). Securing Visual Cryptographic shares using Public Key Encryption. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp. 1108-1113). IEEE.
[20] Lee, K., and P. Chiu. "Image size invariant visual cryptography for general access structures subject to display quality constraints." (2013): 1-1.
[21] Babu, C. R., Sridhar, M., & Babu, B. R. (2013, March). Information hiding in gray scale images using pseudo-randomized visual cryptography algorithm for visual information security. In Information Systems and Computer Networks (ISCON), 2013 International Conference on (pp. 195-199). IEEE.
[22] Rajendra, A. B., & Sheshadri, H. S. (2013, August). Visual Cryptography in Internet Voting System. In Innovative Computing Technology (INTECH), 2013 Third International Conference on (pp. 60-64). IEEE.
[23] Lee, K., and P. Chiu. "Digital Image Sharing by Diverse Image Media." (2014): 1-1.
[24] Liu, X. Y., Chen, M. S., & Zhang, Y. L. (2013, August). A new color visual cryptography scheme with perfect contrast. In Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on (pp. 449-454). IEEE.
[25] Kester, Q. A., Nana, L., & Pascu, A. C. (2013, November). A novel cryptographic encryption technique of video images using quantum cryptography for satellite communications. In Adaptive Science and Technology (ICAST), 2013 International Conference on (pp. 1-6). IEEE.
[26] Kester, Q. A., Nana, L., & Pascu, A. C. (2013, November). A new hybrid asymmetric key-exchange and visual cryptographic algorithm for securing digital images. In Adaptive Science and Technology (ICAST), 2013 International Conference on (pp. 1-5). IEEE.
[27] Yadav, G. S., & Ojha, A. (2013, December). A novel visual cryptography scheme based on substitution cipher. In Image Information Processing (ICIIP), 2013 IEEE Second International Conference on (pp. 640-643). IEEE.
[28] Thankappan, A., & Wilscy, M. (2013, December). (2, 3) RIVC Scheme in Visual Cryptography without Pixel Expansion. In Advanced Computing, Networking and Security (ADCONS), 2013 2nd International Conference on (pp. 259-263). IEEE.
[29] De Prisco, R., & De Santis, A. (2014). On the Relation of Random Grid and Deterministic Visual Cryptography. IEEE transactions on information forensics and security, 9(3-4), 653-665.
[30] Bunker, S. C., Barasa, M., & Ojha, A. (2014, February). Linear equation based visual secret sharing scheme. In Advance Computing Conference (IACC), 2014 IEEE International (pp. 406-410). IEEE.
[31] Jana, B., & Jana, S. (2014, February). Cheating prevention in Visual Cryptographic Schemes using message embedding: A hardware based practical approach. In Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on (pp. 319-324). IEEE.
[32] Stanisavljevic, Zarko, et al. "COALA-System for Visual Representation of Cryptography Algorithms." 1-1.
[33] Wang, Xiang, Qingqi Pei, and Hui Li. "A Lossless Tagged Visual Cryptography Scheme." (2014): 1-1.
[34] Roy, S., & Venkateswaran, P. (2014, March). Online payment system using steganography and visual cryptography. In Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students` Conference on (pp. 1-5). IEEE.
[35] Chavan, P. V., Atique, M., & Malik, L. (2014, March). Signature based authentication using contrast enhanced hierarchical visual cryptography. InElectrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students` Conference on (pp. 1-5). IEEE.
[36] M. Naor and A. Shamir, "Visual Cryptography," Advances in Cryptography: Eurocrypt`94, Springer-Verlag, Berlin, pp. 1-12.
[37] Yan, B., Xiang, Y., & Hua, G. (2019). Improving the Visual Quality of Size-Invariant Visual Cryptography for Grayscale Images: An Analysis-by-Synthesis (AbS) Approach. IEEE Transactions on Image Processing, 28(2), 896-911.
[38] Yang, C. N., Wu, C. C., & Lin, Y. C. (2017). k out of n Region-Based Progressive Visual Cryptography. IEEE Transactions on Circuits and Systems for Video Technology.
[39] Jia, X., Wang, D., Nie, D., & Zhang, C. (2018). Collaborative visual cryptography schemes. IEEE Transactions on Circuits and Systems for Video Technology, 28(5), 1056-1070.
[40] I. M. Yazici, E. Karabulut and M. S. Aktas (2018), "A Data Provenance Visualization Approach," 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), Guangzhou, China, pp. 84-91
[41] Kumar, K. P., & Cherukuri, R. C. (2016, May). The secured data provenance: Background and application oriented analysis. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 1212-1216). IEEE.
[42] Kumar, K. P., & Cherukuri, R. C. (2018, July). Secured Electronic Transactions Using Visual Encryption: An E-Commerce Instance. In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1341-1345). IEEE.

Kukatlapalli Pradeep Kumar, Ravindranath C Cherukuri, "Application of Visual Encryption and its Inferences in Data Provenance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1088-1094, 2019.

In this paper, the system proposed a Lightweight Sharable and Traceable (LiST) secure framework in which tolerant information is scrambled end-to-end from a patient’s cell phone to clients. In this system, a sensor attached on the patient body to collect all the signals from the wireless sensors and sends them to the base station; they are able to sense the heart rate, blood pressure and so on. This system can detect the abnormal conditions, issue an alarm to the Patient and send an SMS/E-mail to the physician. The main advantage of this system in comparison to previous systems is to reduce energy consumption and security. In this system, DROPS methodology divides a file into fragments and replicates the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker. The attribute authorities (AAs) are responsible for performing user legitimacy verification and generating intermediate keys for legitimacy verified users.

Access control, search-able encryption, tractability, user revocation, mobile health system, cloud security, fragmentation, replication, performance, WBSN, EHR

[1] L. Guo, C. Zhang, J. Sun, Y. Fang. A privacy-preserving attribute based authentication System for Mobile Health Networks, IEEE Transactions on Mobile Computing, 2014, vol. 13, no. 9, pp. 1927- 1941.

[2] A. Abbas, S. Khan, A review on the state-of-the-art privacy preserving approaches in e-health clouds, IEEE Journal of Biomedical Health Infor-matics, 2014, vol. 18, pp. 1431-1441.

[3] J. Yang, J. Li, Y. Niu, A hybrid solution for privacy preserving medical data sharing in the cloud environment, Future Generation Computer Systems, 2015, vol. 43-44, pp. 74-86.

[4] http://www.pbs.org/newshour/updates/has-health-care-hacking -become-an-epidemic/.

[5] V. Goyal, O. Pandey, A. Sahai, B. Waters, Attribute-based encryption for fine-grained access control of encrypted data, Proc. 13thm ACM Conf. Computer and Comm. Security (CCS06), Vol. 10, pp. 89-98, 2006.

[6] R. Ostrovsky, A. Sahai, B.Waters, Attribute-based encryption with non-monotonic access structures, in: Proceedings of the 14th ACM Conference on Computer and Communications Security, ACM, 2007, Vol.12, pp. 195-203.
[7] J. Han, W. Susilo, Y. Mu. Improving privacy and security in decentralized ciphertext-policy attribute-based encryption, IEEE Transactions on on Information Forensics and Security, 2015, vol. 10, no. 3, 665-678
[8] M. Li, S. Yu, Y. Zheng, K. Ren,W. Lou. Scalable and secure sharing of personal health records in cloud computing using attributebased encryption, IEEE transactions on parallel and distributed systems, 2013, 24(1): 131-143.

[9] M. Green, S. Hohenberger, B. Waters, Outsourcing the decryption of ABE ciphertexts, in Proc. USENIX Security Symp., San Francisco, CA, USA, 2011.

[10] J. Lai, R. H. Deng, C. Guan, J. Weng, Attribute-based encryption with verifiable outsourced decryption, IEEE Trans. Inf. Forensics Security, vol. 8, no. 8, pp. 1343-1354, Aug. 2013.

[11] B. Qin, R. H. Deng, S. Liu, S. Ma, Attribute-based encryption with efficient verifiable outsourced decryption, IEEE Trans. Inf. Forensics Security, vol. 10, no. 7, pp. 1384-1394, JULY. 2015

[12] Spvryans International Journal of Engineering Sciences & Technology (SEST) ISSN : 2394-0905 Design of a Cloud Based Emergency Health-care Service Model

Tejaswi Wani, Roshani Raut, "Auditable Health Records Levering DROPS in Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1095-1100, 2019.

Intrusion Detection (ID) is a basic part of security, for example, versatile security machines. Earlier different ID procedures are utilized; however their execution is an issue. ID execution relies upon precision, which needs to enhance to diminish false alarms and to expand the detection rate. To determine concerns on execution, multi-layer network, SVM, Naïve Bayes and different procedures have been utilized in later. Such procedures demonstrate restrictions and are not proficient for use in huge data, for example, complex and system data. The ID framework is utilized in breaking down immense traffic data; thus, a proficient classification method is important to beat the issue. This issue is considered in this paper. Popular data mining and machine learning methods are used. They are SVM, and Random forest and KNN, Decision Tree, Extreme Learning Machine (ELM). These methods are outstanding a direct result of their capacity in classification. NSL_KDD dataset is used.

ID, Anomaly Detection, False Alarms, NSL_KDD dataset, Ensemble Approaches

[1] H.Wang,J.Gu,andS.Wang,‘‘An effective intrusion detection framework based on SVM with feature augmentation,’’ Knowl.-Based Syst., vol. 136, pp. 130–139, Nov. 2017, doi: 10.1016/j.knosys.2017.09.014.
[2] S. Duque, N.B Omar, “Using data Mining Algorithms for Developing a Model for ID System (IDS)”, Proceedings of Science direct: Procedia Computer Science 61, pp. (46-51), 2015.
[3] B. Sharma and H. Gupta, “A design and Implementation of ID System by using Data Mining”, IEEE Fourth International Conference on Communication Systems and Network Technologies, pp.700-704, 2015.
[4] U. Ravale, M. marathe, P. Padiya, “Feature Selection based Hybrid Anomoly ID System using K Means and RBF Kernal Function”, Proceedings of Science Direct: International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 428-435, 2015.
[5] W. C. Lin, S. W. Ke, C. F. Tsai, “CANN: An ID system based on combining cluster centers and nearest neighbors”, Proceedings of Science direct: Knowledge-Based Systems, pp. 13-21, 2015.
[6] J. Haque, K.W. Magld, N. Hundewale, “An Intelligent Approach for ID based on Data Mining Techniques”, Proceedings of IEEE, 2012.
[7] Liang Hu, Taihui Li, NannanXie, Jiejunhu, “False Positive Elemination in ID based on Clustering”, IEEE International Conference on Funny System and Knowledge Discovery (FSKD), pp. 519-523, 2015.
[8] Zhengjie Li, Yongzhong Li, Lei Xu, “Anomoly ID Method based on K-Means Clustering Algorithm with Particle Swarm Optimization”, IEEE International Conference of Information Technology, Computer Engineering and Management Sciences, pp. 157- 161, 2011.
[9] S. J. Horng, M.Y. Su, Y. H. Chen, T. W. Kao, R. J. Chen, J. L. Lai, C. D. Perkasa, “A novel ID system based on hierarchical clustering and support vector machines”, Proceedings of Science direct: Expert Systems with Applications, pp. 306-313, 2011.
[10] http://nsl.cs.unb.ca/NSL-KDD/
[11] K. Shivshankar E., “Combination of Data Mining Techniques for ID System”, IEEE International Conference on Computer, Communication and Control (IC4-2015).
[12] L. Dhanabal, S.P. Shantharajah, “A study of NSL-KDD Dataset for ID System based on Classification Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.4, Issue 6, pp. (446-452), June 2015.
[13] Chang, Y., Li, W., & Yang, Z. (2017). Network Intrusion Detection Based on Random Forest and Support Vector Machine. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). doi:10.1109/cse-euc.2017.118
[14] Basic Evaluation Measures From the Confusion Matrix. Accessed: May 20, 2018. [Online]. Available: http://WordPress.com and https:// classeval.wordpress.com
[15] Swati Paliwal and Ravindra Gupta. Article: Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm. International Journalof Computer Applications 60(19):57-62, December 2012.
[16] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, ‘‘Extreme learning machine: A new learning scheme of feedforward neural networks,’’ in Proc. IEEE Int. Joint Conf. Neural Netw., vol. 2, Jul. 2004, pp. 985–990, doi: 10.1109/IJCNN.2004.1380068.

K. Haritha, CH. Mallikarjuna Rao, "A survey on Detecting Network Intrusions Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1101-1105, 2019.