Deep belief bi-directional LSTM network-based intelligent student's performance prediction model with entropy weighted fuzzy rough set mining. (5th June 2023)

• Record Type: Journal Article
• Authors: Sateesh, Nayani; Rao, P. Srinivasa; Lakshmi, D. Rajya
• Is Part Of: International journal of intelligent information and database systems. Volume 16:Number 2(2023)
• Page Start: 107
• Page End: 142
• Publication Date: 2023-06-05
• Subjects: intelligent student's performance prediction model -- entropy weighted fuzzy rough set mining -- EW-FRSM -- deep belief bi-directional LSTM network -- probability-based coyote electric fish optimisation -- P-CEFO; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

To analyse the student's academic performance, a new prediction model is developed. This proposed model collects the student's data from standard online sources. At first, these gathered data are pre-processed by certain methods like outlier removal and filling. Then, the filtered data undergoes feature mining by developing the entropy weighted fuzzy rough set mining (EW-FRSM), where certain parameters are tuned with the proposed hybrid optimisation algorithm with probability-based coyote electric fish optimisation (P-CEFO). The refined features are utilised for performance prediction, where the deep belief-Bi-LSTM (DB-Bi-LSTM) is used for student performance prediction. The parameters in DBN and Bi-LSTM are tuned with the same P-CEFO algorithm to improve the prediction performance. Simulation is performed to examine the efficacy of the designed prediction method with diverse performance metrics by evaluating with diverse baseline approaches to prove the usefulness of the model.