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Comparative Analysis of Machine Learning Tools: A Review

Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha


Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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[1] Muhammad Imran Razzak, Saeeda Naz and Ahmad Zaib, ‘Deep Learning for Medical Image Processing: Overview, Challenges and Future’, Deep Learning for Medical Imaging, pp. 323– 350, 2017.
[2] P V Rajaraman, M Prakash, ‘Deepreply - An Automatic Email Reply System with Unsupervised Cloze Translation And Deep Learning’, ICTACT Journal on Soft Computing, pp. 2090 – 2095, 2020.
[3] Michael J. Bianco, Peter Gerstoft, James Traer, EmmaOzanich, Marie A. Roch, Sharon Gannot, Charles-AlbanDeledalle, ‘Machine learning in acoustics: Theory and applications’, The Journal of the Acoustical Society of America, 2019.
[6] 149374935f3c
[7] Mr. Chintu Kumar, “Machine Learning Concept, Algorithms and Applications: A Survey”, International Journal of Advance Research and Innovative Ideas in Education, pp.901-907,2020.
[8] Sunpreet Kaur, Sonika Jindal, “A Survey on Machine Learning Algorithms”, International Journal of Innovative Research in Advanced Engineering, pp.6-14,2016
[9] - learning - tools/
[10] Nguyen, G., Dlugolinsky, S., Bobák, M. et al., ‘Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey’. Artif Intell Rev , pp.77–124,2019.
[11] TensorFlow, ,2018.
[12] TF,,2018.
[13] TensorFlowLite,, 2018.
[14] 2018.
[15] network-api-explained.html
[17] O. Obulesu, M. Mahendra and M. ThrilokReddy, ‘Machine Learning Techniques and Tools: A Survey’, International Conference on Inventive Research in Computing Applications (ICIRCA),pp. 605-611, 2018.
[18] S Sonnenburg, ‘The SHOGUN Machine Learning Toolbox’, Journal of Machine Learning Research,pp. 1799-1802, 2010.
[19] Benjamin Hillmann, Gabriel A Al-Ghalith, Robin R Shields-Cutler, Qiyun Zhu, Rob Knight, Dan Knights, ‘SHOGUN: a modular, accurate and scalable framework for microbiome quantification’, Bioinformatics, pp.4088-4090,2020.
[20] S. Sarumathi, N. Shanthi, ‘Comprehensive Analysis of Data Mining Tools’ International Journal of Computer and Information Engineering, pp. 837-847,2015
[21] machine-learning-algorithms-using-apache-mahout/
[23] Tools_Handbook_ML_project.pdf
[24] S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila,’ A Review: Comparative Study of Diverse Collection of Data Mining Tools’, International Journal of Computer and Information Engineering, pp. 1028-1033, 2014.
[25] Rapid Miner (Online). Available at:
[26] Kulwinder Kaur, Shivani Dhiman, ‘Review of Data Mining with Weka Tool’, International Journal of Computer Sciences and Engineering, pp.41-44, 2016.
[27] Weka (Online). Available at:
[28] Weka (Online). Available at:
[30] T. Carneiro, R. V. Medeiros Da NóBrega, T. Nepomuceno, G. Bian, V. H. C. De Albuquerque and P. P. R. Filho, ‘Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications’, pp. 61677-61685, 2018.
[31] Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort. Et. Al., ‘Scikit-Learn: Machine Learning in Python’, Journal of Machine Learning Research’,2012.
[33] Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, and Tawfiq Hasanin, ‘A survey of open source tools for machine learning with big data in the Hadoop ecosystem’,Journal of Big Data,2015.
[36] fun-using-orange