Maintain and Evaluate students’ performance Using Machine Learning

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2020 by IJCTT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Mujthaba G.M, Gulam Mubasheer Mujtaba, Mohammed Rahmath
  10.14445/22312803/IJCTT-V68I6P110

MLA

MLA Style: Mujthaba G.M, Gulam Mubasheer Mujtaba, Mohammed Rahmath  "Maintain and Evaluate students’ performance Using Machine Learning" International Journal of Computer Trends and Technology 68.6 (2020):57-63.

APA Style Mujthaba G.M, Gulam Mubasheer Mujtaba, Mohammed Rahmath  International Journal of Computer Trends and Technology, 68(6),57-63.

Abstract
Machine learning is an obligatory branch of Artificial Intelligence in computer science is used to analyse data, evaluate the analytical models, visualize & predict outputs and decision making. These operations requires the best learning approaches which hassles machine learning algorithms. The numerous machine learning algorithms are categorized into primarily supervised, unsupervised and reinforcement algorithms. The main objective of these algorithms is to lessen the human interactions with appropriate decision making. This paper highpoints how these machine algorithms can be beneficial in students learning. The effective use of supervised machine algorithms like regression, decision tree, and logistic representations will be applied on to learning class rooms for higher education. To implement these algorithms this research paper chooses R programming language. As R is known to be insightful and influential language in creating data sets, identify patterns, model & visualize data, predict and make proper decision making. These machine learning algorithms will transform the student’s data into appropriate decisions. All the learning operations are performed by using R language and generate the results.

Reference
[1] Lotfi, Farhad Hosseinzadeh, et al. "Introductions and Definitions of R." Data Envelopment Analysis with R. Springer, Cham, 2020. 19-52.
[2] Mujthaba, G. M., Al Ameen, A., Kolhar, M., & Rahmath, M. “Data Science Techniques, Tools and Predictions”.
[3] Forth, Katharine, and Erez Lieberman Aiden. "Identifying fall risk using machine learning algorithms." U.S. Patent No. 10,542,914. 28 Jan. 2020.
[4] Snoek, Jasper, Hugo Larochelle, and Ryan P. Adams. "Practical bayesian optimization of machine learning algorithms." Advances in neural information processing systems. 2012.
[5] Zhao, Gouheng, et al. "A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks." IEEE Access 8 (2020): 65462-65471.
[6] King, Fraser, et al. "Application of machine learning techniques for regional bias correction of SWE estimates in Ontario, Canada." Hydrology and Earth System Sciences Discussions (2020): 1-26.
[7] Elassad, Zouhair Elamrani Abou, et al. "The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review." Engineering Applications of Artificial Intelligence 87 (2020): 103312.
[8] Biswas, Aniruddha, et al. "A Critical Approach to R Programming in the Analysis of lncRNA in Bioinformatics Study." Available at SSRN 3526024 (2020).
[9] Zuccolotto, Paola, and Marica Manisera. Basketball Data Science: With Applications in R. CRC Press, 2020.
[10] Kim, Yeonuk, et al. "CH4 flux gap-filling approaches for eddy covariance data: a comparison of three machine learning algorithms and marginal distribution sampling method with and without principal component analysis." Geophysical Research Abstracts. Vol. 21. 2019.
[11] Sathya, D., V. Sudha, and D. Jagadeesan. "Application of Machine Learning Techniques in Healthcare." Handbook of Research on Applications and Implementations of Machine Learning Techniques. IGI Global, 2020. 289-304.
[12] Luengo, Julián, et al. "Big Data: Technologies and Tools." Big Data Preprocessing. Springer, Cham, 2020. 15-43.
[13] Aphalo, Pedro J. "Learn R." (2020).
[14] Chinnamgari, Sunil Kumar. R “Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5”. Packt Publishing Ltd, 2019.
[15] Singh, Amanpreet, Narina Thakur, and Aakanksha Sharma. "A review of supervised machine learning algorithms." 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). Ieee, 2016.
[16] Belavagi, Manjula C., and Balachandra Muniyal. "Performance evaluation of supervised machine learning algorithms for intrusion detection." Procedia Computer Science 89.2016 (2016): 117-123.
[17] Gravesteijn, Benjamin Y., et al. "Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury." Journal of clinical epidemiology (2020).
[18] Alsolami, Fawaz, et al. "Preliminary Results for Decision and Inhibitory Trees, Tests, Rules, and Rule Systems." Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Springer, Cham, 2020. 45-73.
[19] Asthana, Pallavi, and Bramah Hazela. "Applications of Machine Learning in Improving Learning Environment." Multimedia Big Data Computing for IoT Applications. Springer, Singapore, 2020. 417-433.
[20] Flach, Peter. “Machine learning: the art and science of algorithms that make sense of data.” Cambridge University Press, 2012.
[21] Sharon Machlis.” Beginner's guide to R: Easy ways to do basic data analysis”, Executive Editor, Data & Analytics, Computerworld | AUG 18, 2017 9:49 AM PDT.
[22] Peyakunta Bhargavi,” Machine Learning Algorithms in Big data Analytics”, International journal of computer sciences and engineering · January 2018.
[23] Dr. S. Subatra Devi "Big Data - Benefits and its Growth" International Journal of Computer Trends and Technology 68.5 (2020):14-17.
[24] Sangeeta.K, G.V.S.S.Naveen Babu, Madhuri. G "Classification and Prediction of Slow Learners Using Machine Learning Algorithms." International Journal of Computer Trends and Technology 68.2 (2020):54-58.

Keywords
Machine Learning (M.L.), Linear Regression, Multiple Regression, Decision trees, Logistic representations and R Programming.