Article: Student performance prediction model using HLRO-DMN Journal: International Journal of Bio-Inspired Computation (IJBIC) 2026 Vol.27 No.3 pp.179 - 191 Abstract: This research introduced the proposed hybrid leader remora optimisation algorithm with deep maxout network (HLRO_DMN) for accurately predicting the students' performance. Initially, the input data acquired from the dataset is transformed into a suitable format using Yeo-Johnson's transformation. Then, the dice coefficient is employed for selecting optimal features, which combines the feature score obtained from the Fisher score and the Tversky index. In addition, the data augmentation is completed by the bootstrapping method, and the performance prediction is carried out by the DMN, wherein the weight of the DMN is tuned by the HLRO algorithm. Besides, the experimentation of HLRO_DMN attained the best result using certain metrics, like mean square error (MSE), Root mean square error (RMSE), and mean absolute error (MAE), and the accuracy of the corresponding values noted by the devised scheme are 5.4032, 0.175, 0.4444, and 91.314, respectively. Inderscience Publishers - linking academia, business and industry through research

Title: Student performance prediction model using HLRO-DMN

Authors: L. Srinivasan; D. Kalaivani; C. Nalini; I. Gugan

Addresses: Department of Computer Science and Engineering, Dr N.G.P Institute of Technology, Dr. N.G.P. Nagar, Kalapatti Main Rd., Coimbatore, Tamil Nadu 641048, India ' Department of Information Science and Engineering, New Horizon College of Engineering, Kaverappa Layout, Kadubeesanahalli, Bengaluru, Karnataka 560103, India ' Department of Artificial Intelligence and Machine Learning, Kongu Engineering College, Thoppupalayam, Kumaran Nagar, Perundurai, Tamil Nadu 638060, India ' Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Dr.N.G.P. Nagar, Kalapatti Main Rd., Coimbatore, Tamil Nadu 641048, India

Abstract: This research introduced the proposed hybrid leader remora optimisation algorithm with deep maxout network (HLRO_DMN) for accurately predicting the students' performance. Initially, the input data acquired from the dataset is transformed into a suitable format using Yeo-Johnson's transformation. Then, the dice coefficient is employed for selecting optimal features, which combines the feature score obtained from the Fisher score and the Tversky index. In addition, the data augmentation is completed by the bootstrapping method, and the performance prediction is carried out by the DMN, wherein the weight of the DMN is tuned by the HLRO algorithm. Besides, the experimentation of HLRO_DMN attained the best result using certain metrics, like mean square error (MSE), Root mean square error (RMSE), and mean absolute error (MAE), and the accuracy of the corresponding values noted by the devised scheme are 5.4032, 0.175, 0.4444, and 91.314, respectively.

Keywords: remora optimisation algorithm; ROA; deep maxout network; hybrid leader-based optimisation; HLBO; Yeo-Johnson's transformation; dice coefficient.

DOI: 10.1504/IJBIC.2026.153410

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.3, pp.179 - 191

Received: 07 Feb 2024
Accepted: 07 Jan 2025
Published online: 07 May 2026 *

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Title: Student performance prediction model using HLRO-DMN

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