[1] Damopoulos, D., Menesidou, S.A., Kambourakis, G., Papadaki, M., Clarke, N., Gritzali, N., “Evaluation of Anomaly-Based IDS for Mobile Devices Using Machine Learning Classifiers”, Security Comm. Networks, Vol. 5, Issue. 1, pp. 3-14, 2011.
[2] Firdausi, I., Lim, C., Erwin, A., Nugroho, A.S. “Analysis of machine learning techniques used in behavior- based malware detection”, In: IEEE International Conference on Advances in Computing, Control, and Telecommunication Technologies, PP. 201-203, 2010.
[3] EI-Bendary, N., EI-Hariri, E., Hassanien, A.E., Badr, A.. “Using machine learning techniques for evaluating tomato ripeness. Expert Systems with Applications”, Vol. 42 Issue. 4, pp. 1892–1905, 2015.
[4] Phadikar, S., Sil, J., Das, A.K. “Rice diseases classification using feature selection and rule generation techniques”, Computers and Electronics in Agriculture, Vol. 90, Issue. C, pp. 76–85, 2013.
[5] Kundu, P.K., Panchariya, P.C., Kundu, M. “Classification and authentication of unknown water samples using machine learning algorithms”, ISA Transactions, Vol. 50, Issue.3, pp. 343-520, 2011.
[6] Sankaran, S., Mishra, A., Ehsani, R., Davis, C., “A review of advanced techniques for detecting plant diseases”, Computers and Electronics in Agriculture, Vol. 72, Issue. 1, pp. 1-13, 2010.
[7] Timmermans, A.J.M., and Hulzebosch, A.A., “Computer vision system for on-line sorting of pot plants using an artificial neural network classifier”, Computers and Electronics in Agriculture, Vol. 15, Issue.1, pp. 41–55, 1996.
[8] Butler, D.R., Wadia, K.D.R., Jadav, D.R., “Effects of leaf wetness and temperature on late leaf spot infection of groundnut”, Plant Pathology, Vol.43, Issue. 1 pp. 112–120, 1994.
[9] Chaudhary, A., Kolhe, S., Raj Kamal, “A Hybrid ensemble for classification in multiclass datasets: An application to Oilseed disease dataset”, Computers and Electronics in Agriculture, Vol. 124,pp. 65-72, 2016.
[10] Chaudhary, A., Kolhe, S., Raj Kamal, “An improved Random Forest Classifier for multi-class classification”, Information Processing in Agriculture, Elsevier Journal Information Processing in Agriculture, Vol. 3, Issue.4, pp. 215-222, 2016.
[11] Pujari, J.D., Yakkundimath, R., Byadgi, A.S., “Classification of fungal disease symptoms affected on cereals using color texture features”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 6, Issue. 6, pp. 321-330, 2013.
[12] Quinlan, J.R., “Generating Production Rules from Decision Trees”, In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 304–307, 1987.
[13] Zhao, Y., and Zhang, Y., “Comparison of decision tree methods for finding active objects”, Advances in Space Research, Vol. 41, Issue. 12, pp. 1955-1959, 2008.
[14] Quinlan, J.R., “Generating Production Rules from Decision Trees”, In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 304–307, 1987.
[15] Iba, W., and Pat, L., “Induction of One-Level Decision Trees”, In: Proceedings of the Ninth International Conference on Machine Learning, Aberdeen, Scotland, San Francisco, CA: Morgan Kaufmann, pp. 233–240, 1992.
[16] Breiman, L., “Random Forests”, Machine Learning, Vol. 45, Issue. 1,pp. 5–32, 2001.
[17] Seera, M., and Lim, C.P., “A hybrid intelligent system for medical data classification”, Expert Systems with Applications, Vol. 41, Issue. 5,pp. 2239–2249, 2014.
[18] Titapiccolo, J.I., Ferrario, M., Cerutti, S., Barbieri, C., Mari, F., Gatti, E., “Signorini, M.G., Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients”, Expert Systems with Applications, Vol. 40, Issue. 11, pp. 4679–4686, 2013.
[19] Azar, A.T., Elshazly, H.I., Hassanien, A.E., Elkorany, A.M., “A random forest classifier for lymph diseases”, Computer Methods and Programs in Biomedicine, Vol. 113, Issue. 2,pp. 465–473, 2014.
[20] Austin, P.C., Tu, J.V., Ho, J.E., Levy, D., Lee, D.S., “Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes”, Journal of Clinical Epidemiology, Vol. 66, Issue. 4, pp 398-407, 2013.
[21] Tripoliti, E.E., Fotiadis, D.I., Argyropoulou, M., Manis, G., “A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data”,. Journal of Biomedical Informatics, Vol. 43, Issue. 2, pp. 307–320, 2010.
[22] Ramírez, J., Górriz, J.M., Segovia, F., Chaves, R., Salas-Gonzalez, D., López, M., Álvarez, I., Padilla, P., “Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification”, Neuroscience Letters, Vol. 472, Issue. 2, pp. 99-103, 2010.
[23] Singh, Y., Bhatia, P.K., Sangwan, O.M., “A Review of studies on Machine Learning techniques”, In: International Journal of Computer Science and Security, Vol. 1, Issue. 1, pp. 70-84, 2007.
[24] Chaudhary, A., Kolhe, S., Raj Kamal, “Machine Learning Techniques for Mobile Devices: A Review”, International Journal of Engineering Research and Applications, Vol. 3, Issue. 6, pp. 913-917, 2016.
[25] Oubaha, J., Habbani, A., Elkoutbi, M., “Mobile Intelligent System (MIS) and a multi-criteria in MPLS networks”, International Journal of Next Generation Networks (IJNGN), Vol. 2, Issue. 4, pp. 61-71, 2010.
[26] Jain, P.,Bhat, K., Kesharwani, H., Bhate, P., Khurana, K.P., “Stock Market Analysis and Prediction using Hadoop and Machine Learning”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue. 5, pp. 578–584, 2018.
[27] Sharmila, R., Chellammal, S., “A conceptual method to enhance the prediction of heart diseases using big data techniques”, International Journal of Computer Sciences and Engineering, Vol. 6, Special Issue. 4, pp. 21–25, 2018.
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