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An Improved Expeditious Meta-Heuristic Clustering Method for Classifying Student Psychological Issues with Homogeneous Characteristics

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  • Muhammad Suhail Shaikh

    (School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China)

  • Xiaoqing Dong

    (School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China)

  • Gengzhong Zheng

    (School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China)

  • Chang Wang

    (School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China)

  • Yifan Lin

    (School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China)

Abstract

Nowadays, cluster analyses are widely used in mental health research to categorize student stress levels. However, conventional clustering methods experience challenges with large datasets and complex issues, such as converging to local optima and sensitivity to initial random states. To address these limitations, this research work introduces an Improved Grey Wolf Clustering Algorithm ( i GWCA). This improved approach aims to adjust the convergence rate and mitigate the risk of being trapped in local optima. The i GWCA algorithm provides a balanced technique for exploration and exploitation phases, alongside a local search mechanism around the optimal solution. To assess its efficiency, the proposed algorithm is verified on two different datasets. The dataset-I comprises 1100 individuals obtained from the Kaggle database, while dataset-II is based on 824 individuals obtained from the Mendeley database. The results demonstrate the competence of i GWCA in classifying student stress levels. The algorithm outperforms other methods in terms of lower intra-cluster distances, obtaining a reduction rate of 1.48% compared to Grey Wolf Optimization (GWO), 8.69% compared to Mayfly Optimization (MOA), 8.45% compared to the Firefly Algorithm (FFO), 2.45% Particle Swarm Optimization (PSO), 3.65%, Hybrid Sine Cosine with Cuckoo search (HSCCS), 8.20%, Hybrid Firefly and Genetic Algorithm (FAGA) and 8.68% Gravitational Search Algorithm (GSA). This demonstrates the effectiveness of the proposed algorithm in minimizing intra-cluster distances, making it a better choice for student stress classification. This research contributes to the advancement of understanding and managing student well-being within academic communities by providing a robust tool for stress level classification.

Suggested Citation

  • Muhammad Suhail Shaikh & Xiaoqing Dong & Gengzhong Zheng & Chang Wang & Yifan Lin, 2024. "An Improved Expeditious Meta-Heuristic Clustering Method for Classifying Student Psychological Issues with Homogeneous Characteristics," Mathematics, MDPI, vol. 12(11), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1620-:d:1399189
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    References listed on IDEAS

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    1. Marina Antonopoulou & Maria Mantzorou & Aspasia Serdari & Konstantinos Bonotis & Giorgos Vasios & Eleni Pavlidou & Christina Trifonos & Konstantinos Vadikolias & Dimitris Petridis & Constantinos Giagi, 2020. "Evaluating Mediterranean diet adherence in university student populations: Does this dietary pattern affect students' academic performance and mental health?," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(1), pages 5-21, January.
    2. Tran Manh Tuan & Luong Thi Hong Lan & Shuo-Yan Chou & Tran Thi Ngan & Le Hoang Son & Nguyen Long Giang & Mumtaz Ali, 2020. "M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing," Mathematics, MDPI, vol. 8(5), pages 1-24, May.
    3. Xiang Feng & Yaojia Wei & Xianglin Pan & Longhui Qiu & Yongmei Ma, 2020. "Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment—Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method," IJERPH, MDPI, vol. 17(6), pages 1-16, March.
    4. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
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