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

作     者：Amlan Jyoti Baruah Siddhartha Baruah Amlan Jyoti Baruah;Siddhartha Baruah

作者机构：Department of Computer Science and EngineeringAssam Kaziranga UniversityJorhat 785006India Department of Computer ApplicationJorhat Engineering CollegeJorhat 785007India 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2021年第18卷第6期

页      面：981-992页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Educational data mining(EDA) MapReduce framework deep neuro-fuzzy network student performance data augmentation 

摘      要：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.