Amlan Jyoti Baruah, Dr. Siddhartha Baruah. Data augmentation and Deep Neuro-Fuzzy network for student performance prediction with MapReduce framework. International Journal of Automation and Computing.
Citation: Amlan Jyoti Baruah, Dr. Siddhartha Baruah. Data augmentation and Deep Neuro-Fuzzy network for student performance prediction with MapReduce framework. International Journal of Automation and Computing.

Data augmentation and Deep Neuro-Fuzzy network for student performance prediction with MapReduce framework

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  • Author Bio:

    Amlan Jyoti Baruah is currently working as an Assistant Professor in Department of Computer Science and Engineering, Assam Kaziranga University, Jorhat. He pursued his Bachelor of Technology in Computer Scienceand Engineering from NERIST, Arunachal Pradesh and Master of Technology from KIIT University, Bhubaneswar. He is also pursuing his PhD from Jorhat Engineering College, under ASTU. He has around 8 years of teaching experience. His research interests are Educational Data mining, Deep Learning and Artificial Intelligence

    Dr. Siddhartha Baruah is currently working as a Professor in Department of Computer Application, Jorhat Engineering College. He pursued his B. Sc with honors in Physics from Science College Jorhat (Currently known as JIST) and Master of Computer Application from Jorhat Engineering College. He pursued his PhD from Guwahati University. He has around 28 years of teaching experience and 12 years of research experience. He has completed several projects in MODROB approved by AICTE and played the key role in starting Ph. D Program in MCA Department of JEC in 2018. He has publishedseveral papers as well as attended different International Conference in India and Abroad. His research interests areEmbedded System, Educational Data mining, Deep Learning and Artificial Intelligence

  • Corresponding author: amlanbaruah2007@gmail.comsbaruain@gmail.com
  • Received Date: 2021-05-11
  • Accepted Date: 2021-08-18
  • 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 0.3383, 0.5817, and 0.3915, respectively.

     

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