Volume-06 , Special Issue-06 , Jul - 2018 Go Back

Social media plays a central role in society nowadays. Extremism is also a hot topic of discussion. Violent extremist activities causes major issues. It is difficult to identify people engaging in extremist activities thorough public cyber spaces like internet cafes, institutions etc... With the help of machine learning concepts the proposed system tries to analyze the online text content and classify it in to Violent or Nonviolent content. SVM Classifier is used here. Then analyzing the user activities till now it will predict the user status, which helps to detect the extremists. The proposed system helps to detect the extremism contents and extremists as well as the extremism supporting users

SVM,Extremism,Violentextremism,Violent,Nonviolent

[1] Ferrara E., Wang WQ, Varol O., Flammini A., Galstyan A.,”Predicting online extremism, content adopters, and interaction reciprocity”, Social Informatics, vol.10047), 2016
[2] A. Fisher, “How jihadist networks maintain a persistent online presence,” Perspectives on Terrorism, vol. 9, no. 3, 2015
[3] Q. Schiermeier, “Terrorism: Terror prediction hits limits.” Nature, vol. 517, no. 7535, p. 419, 2015.
[4] S. Reardon, “Terrorism: science seeks roots of terror,” Nature, vol. 517, no. 7535, pp. 420–421, 2015.
[5] J. Stern and J. M. Berger, ISIS: The state of terror. Harper, 2015
[6] P. Cockburn, “The rise of Islamic State: ISIS and the new Sunni revolution”. Verso Books, 2015.
[7] M. Weiss and H. Hassan, ISIS: Inside the army of terror. Simon and Schuster, 2015.
[8] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” The Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[9] Yifang Wei,Lisa Singh and Susan Martin, “Identication of Extremism on Twitter”, International Conference on Advances in Social Networks Analysis and Mining, 2016.
[10] Imran Awan, “Cyber-Extremism: Isis and the Power of Social Media”, SOCIAL SCIENCE AND PUBLIC POLICY, Vol. 54, pp. 138–149, 2017.
[11] Budak Arpinar, Ugur Kursuncu and Dilshod achilov, “Social Media Analytics to Identify and Counter Islamist Extremism: Systematic Detection, Evaluation, and Challenging of Extremist Narratives Online”, International Conference on Collaboration Technologies and Systems, vol. 18, no. 7,pp. 1527–1554, 2016.
[12] Swati Agarwal, Ashish Sureka, and Vikram Goyal ,Open Source Social Media Analytics for Intelligence and Security Informatics Applications, LNCS, vol. 9498,pp. 21–37, 2015.
[13] J.Berger and B.Strathearn, “Who matters online: measuring infuence, evaluating content and countering violent extremism in online social networks”, Int. Centre for the Study of Radicalisation,2013.
[14] Zi-qiang Wang, Xia Sun, and De-xian Zhang, “An Optimal SVM-Based Text Classification Algorithm”, International Conference on Machine Learning and Cybernetics, 2006.
[15] L.Breiman, “Random forests, Machine learning”, vol. 45, no. 1, pp. 532, 2001.
[16] https://machinelearningmastery.com/support-vector-machines-for-machine-learning
[17] Conway, “Determining the Role of the Internet in Violent Extremism and Terrorism: Six Suggestions for Progressing Research”, .StudiesinConflictandTerrorism, 2016.
[18] Hussain,G., Saltman, “Jihad Trending: A Comprehensive Analysis of Online Extremism and How to Counter it, Quilliam Foundation” , The United Kingdom,2014.
[19] Klausen, Tweeting the Jihad: social media networks of western foreign fighters in Syria and Iraq,StudiesinConflictandTerrorism,volume.38,pp.1-22,2015.
[20] H. Chen, W. Chung, J. Qin, E. Reid, M. Sageman, and G. Weimann, “Uncovering the dark web: A case study of jihad on the web,” Journal of the American Society for Information Science and Technology, vol. 59, no. 8, pp. 1347–1359, 2008.

Varuna. T.V , "Prediction of Violent Extremism from Online Textual Contents", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.103-106, 2018.

Nowadays, illegal counterfeit coins are considerably affecting the financial transactions in society. This work proposes an efficient image based fake coin detection, which can be applied to ensure the authenticity of coins. Although several types of fake currency detectors are already existing, fake coin detection still remains as a challenging problem. Image based approach have benefits in terms of cost and ease of usage. The fake coin detection uses a vector space approach, termed as dissimilarity space. It is a vector space constructed by measuring the dissimilarity between the coin image and the prototype. Dissimilarity between the coin images is obtained using the combination of Difference Of Gaussian (DOG) detector and Scale Invariant Feature Transform (SIFT). The proposed system adapts to coin rotation and scaling. In this work, one class learning method is used, so for training the classifier, only genuine Indian coins are needed.

Fake coin, Fake coin detection, One class learning, Dissimilarity space

[1]. B. M. Dobbins and J. E. Vaks, “Method and apparatus for improved coin, bill and other currency acceptance and slug or counterfeit rejection,” U.S. Patent 5 167 313, Dec. 1, 1992.
[2]. T. Nara and T. Ueki, “Coin discriminating apparatus,”U.S. Patent 5 263 566, Nov. 23, 1993.
[3]. Y. Furuya et al., “Method and apparatus for sorting coins utilizing coin-derived signals containing different harmonic components,” U.S. Patent 4 971 187, Nov. 20, 1990.
[4]. K. Okada, “Method and apparatus for discriminating coins or bank notes,” U.S. Patent 4 556 140, Dec. 3, 1985.
[5]. M. Hida, T. Mitsui, and Y. Minami, “Forensic investigation of counterfeit coins, ” Forensic Sci. Int., vol. 89, no. 1, pp. 21–26, 1997.
[6]. M. Hida, H. Sato, H. Sugawara, and T. Mitsui, “Classification of counterfeit coins using multivariate analysis with X-ray diffraction and X-ray fluorescence methods,” Forensic Sci. Int., vol. 115, no. 1, pp. 129–134, 2001.
[7]. Li Liu, Yue Lu, Senior Member, IEEE, and Ching Y. Suen, Life Fellow, IEEE, “An Image-Based Approach to Detection of Fake Coins,” IEEE transactions on information forensics and security, vol. 12, no. 5, may 2017.
[8]. K. Sun et al., “Detection of counterfeit coins based on shape and lettering features,” in Proc. Int. Conf. Comput. Appl. Ind. Eng., 2015, pp. 165–170.
[9]. R. Huber, H. Ramoser, K. Mayer, H. Penz, and M. Rubik, “Classification of coins using an eigenspace approach,” Pattern Recognit. Lett., vol. 26, no. 1, pp. 61–75, 2005.
[10]. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.

K. Swathi, K. P. Mohanan, "Image Based Fake Indian Coin Detection", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.107-109, 2018.

A fully automated systemwhere embedded large pools of sensors in the existing electricity grid systems for monitoring and controlling it by making use of modern information technology is what is known as Smart Grid. By deriving and processing new information from these data in real time it can be made more applicable. Energy consumption prediction, which is a significant part of smart grid, may be difficult to handle with huge energy usage data in the grid. This is because the redundancy from feature selection cannot be avoided. Our aim is to predict the commercial energy consumption by a building based on its previous consumption history. First, we apply a correlation based feature selection method in order to filter out the most relevant attributes. Out of the resulting dataset so formed, for the purpose of dimensionality reduction we use a Kernel Principle Component Analysis methodology. What we obtain will be a set of principal components which will be our new dataset. To predict the energy usage, we use a Support Vector Regression method that uses kernel technique that determines a suitable point as the predicted value. Finally, we evaluate the performance of the predictor based on different evaluators to understand the efficiency of the technique.

Smart Grid, Energy Consumption, Correlation Based Feature Selection, Kernel Principal Component Analysis, Support Vector Regression, Prediction

[1] Nagi, Jea, K. S. Yap, S. K. Tiong, S. K. Ahmed, and A. M. Mohammad. "Detection of abnormalities and electricity theft using genetic support vector machines." TENCON 2008-2008 IEEE Region 10 Conference, pp. 1-6. IEEE, 2008
[2] Angelos, Eduardo Werley S., Osvaldo R. Saavedra, Omar A. Carmona Cortés, and André Nunes de Souza. "Detection and identification of abnormalities in customer consumptions in power distribution systems." IEEE Transactions on Power Delivery 26, no. 4 (2011): 2436-2442.
[3] Costa, Breno C., Bruno LA Alberto, André M. Portela, W. Maduro, and Esdras O. Eler. "Fraud detection in electric power distribution networks using an ANN-based knowledge-discovery process." International Journal of Artificial Intelligence & Applications 4, no. 6 (2013): 17.
[4] Bu, Li, Dongbin Zhao, Yu Liu, and Qiang Guan. "A hierarchical classification algorithm for evaluating energy consumption behaviors." Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 1461-1466. IEEE, 2014.
[5] Mayhew, Michael, Michael Atighetchi, Aaron Adler, and Rachel Greenstadt. "Use of machine learning in big data analytics for insider threat detection." Military Communications Conference, MILCOM 2015-2015 IEEE, pp. 915-922. IEEE, 2015.
[6] Jindal, Anish, Amit Dua, Kuljeet Kaur, Mukesh Singh, Neeraj Kumar, and S. Mishra. "Decision tree and SVM-based data analytics for theft detection in smart grid." IEEE Transactions on Industrial Informatics 12, no. 3 (2016): 1005-1016.
[7] Haben, Stephen, Colin Singleton, and Peter Grindrod. "Analysis and clustering of residential customers energy behavioral demand using smart meter data." IEEE transactions on smart grid 7, no. 1 (2016): 136-144.
[8] MCA, Mrs J. Sukanya. "Applications of Big Data Analytics and Machine Learning Techniques in Health Care Sectors." International Journal Of Engineering And Computer Science 6, no. 7 (2017).
[9] Wang, Kun, Chenhan Xu, Yan Zhang, Song Guo, and Albert Zomaya. "Robust big data analytics for electricity price forecasting in the smart grid." IEEE Transactions on Big Data(2017).
[10] Wahid, Fazli, RozaidaGhazali, Abdul Salam Shah, and Muhammad Fayaz. "Prediction of energy consumption in the buildings using multi-layer perceptron and random forest." IJAST 101 (2017): 13-22.
[11] Xuemei, Li, Ding Lixing, LvJinhu, Xu Gang, and Li Jibin. "A novel hybrid approach of kpca and svm for building cooling load prediction." Knowledge Discovery and Data Mining, 2010. WKDD`10. Third International Conference on, pp. 522-526. IEEE, 2010.
[12] Dubey, Vimal Kumar, and Amit Kumar Saxena. "Hybrid classification model of correlation-based feature selection and support vector machine." Current Trends in Advanced Computing (ICCTAC), IEEE International Conference on, pp. 1-6. IEEE, 2016.
[13] Ince, Huseyin, and Theodore B. Trafalis. "Kernel principal component analysis and support vector machines for stock price prediction." IIE Transactions 39, no. 6 (2007): 629-637.
[14] Michalak, Krzysztof, and Halina Kwasnicka. "Correlation-based feature selection strategy in neural classification." Intelligent Systems Design and Applications, 2006. ISDA`06. Sixth International Conference on, vol. 1, pp. 741-746. IEEE, 2006.
[15] Kallas, Maya, Gilles Mourot, Didier Maquin, and José Ragot. "Fault estimation of nonlinear processes using kernel principal component analysis." Control Conference (ECC), 2015 European, pp. 3197-3202. IEEE, 2015.

Panchami Anil, Anas P V, Naseef Kuruvakkottil, Anusha K V, Balagopal N, "Energy Prediction Using Data Analytics in Smart Grid", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.110-115, 2018.