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Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework

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  • AI Basic Theory
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Abstract

The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover the attributes that manipulate the performance of students. Student performance prediction is a major issue in education and training, specifically in the educational data mining system. This research presents the student performance prediction approach with the MapReduce framework based on the proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network. The proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network is derived by integrating fractional calculus with competitive multi-verse optimization. The MapReduce framework is designed with the mapper and the reducer phase to perform the student performance prediction mechanism with the deep learning classifier. The input data is partitioned at the mapper phase to perform the data transformation process, and thereby the features are selected using the distance measure. The selected unique features are employed for the data segmentation process, and thereafter the prediction strategy is accomplished at the reducer phase by the deep neuro-fuzzy network classifier. The proposed method obtained the performance in terms of mean square error, root mean square error and mean absolute error with the values of 0.338 3, 0.581 7, and 0.391 5, respectively.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Assam Kaziranga University, Jorhat, 785006, India

    Amlan Jyoti Baruah

  2. Department of Computer Application, Jorhat Engineering College, Jorhat, 785007, India

    Siddhartha Baruah

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  1. Amlan Jyoti Baruah
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  2. Siddhartha Baruah
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Correspondence to Amlan Jyoti Baruah.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Amlan Jyoti Baruah received the B. Tech. degree in computer science and engineering from North Eastern Regional In- stitute of Science and Technology, Arunachal Pradesh, India in 2011, received the M. Tech. degree in computer science and engineering from Kalinga Institute of Industrial Technology (Deemed University), India in 2013. He is also a Ph. D. degree candidate from Jorhat Engineering College, Assam Science and Technology, India. He is currently working as an assistant professor in Department of Computer Science and Engineering, Assam Kaziranga University, India. He has around 8 years of teaching experience.

His research interests include educational data mining, deep learning and artificial intelligence.

Siddhartha Baruah received the B. Sc. degree with honors in physics from Science College Jorhat (Currently known as Jorhat Institute Of Science and Technology (JIST)), India in 1987, the MCA degree in computer application from Jorhat Engineering College, India in 1990, and the Ph.D. degree in computer science from Guwahati University, India in 2010. He is currently working as a professor in Department of Computer Application, Jorhat Engineering College, India. He has around 28 years of teaching experience and 12 years of research experience. He has completed several projects in modernisation and removal of obsolenscence (MODROB) approved by All India Council for Technical Education (AICTE) and played the key role in starting Ph. D. Program in MCA Department of JEC in 2018. He has published several papers as well as attended different international conferences in India and Abroad.

His research interests include embedded system, educational data mining, deep learning and artificial intelligence.

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Baruah, A.J., Baruah, S. Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework. Int. J. Autom. Comput. 18, 981–992 (2021). https://doi.org/10.1007/s11633-021-1312-1

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