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Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Author

Listed:
  • Surbhi Bhatia Khan

    (Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Mohammed Alojail

    (Department of Management Information Systems, College of Business Administration, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia)

  • Moteeb Al Moteri

    (Department of Management Information Systems, College of Business Administration, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia)

Abstract

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems.

Suggested Citation

  • Surbhi Bhatia Khan & Mohammed Alojail & Moteeb Al Moteri, 2023. "Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization," Mathematics, MDPI, vol. 12(1), pages 1-20, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:119-:d:1310127
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    References listed on IDEAS

    as
    1. Beth Sprunt & Manjula Marella, 2021. "Combining Child Functioning Data with Learning and Support Needs Data to Create Disability-Identification Algorithms in Fiji’s Education Management Information System," IJERPH, MDPI, vol. 18(17), pages 1-13, September.
    2. Xin Gu & Fariza Sabrina & Zongwen Fan & Shaleeza Sohail, 2023. "A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems," IJERPH, MDPI, vol. 20(15), pages 1-25, August.
    3. Aditya Jain & Juliet Hassard & Stavroula Leka & Cristina Di Tecco & Sergio Iavicoli, 2021. "The Role of Occupational Health Services in Psychosocial Risk Management and the Promotion of Mental Health and Well-Being at Work," IJERPH, MDPI, vol. 18(7), pages 1-24, March.
    4. Lorenzo Lippi & Alessandro de Sire & Arianna Folli & Alessio Turco & Stefano Moalli & Antonio Ammendolia & Antonio Maconi & Marco Invernizzi, 2022. "Environmental Factors in the Rehabilitation Framework: Role of the One Health Approach to Improve the Complex Management of Disability," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
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