Artificial Intelligent and Internet of Things framework for sustainable hazardous waste management in hospitals.

Journal: Waste management (New York, N.Y.)
Published Date: Apr 30, 2025

Abstract

Healthcare activities in hospitals generate numerous types of post-use waste materials that can be classified as hazardous. This study proposes an Artificial Intelligence (AI) and Internet of Things (IoT) integrated framework for secure and efficient hazardous waste management in hospitals. Smart bins with IoT-enabled locks ensure waste collection, while Convolutional Neural Network (CNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) improve detection and classification accuracy. A kinematic waste sorting mechanism is proposed to manage space constraints in hospitals. Deep Reinforcement Learning optimises disinfection scheduling and waste storage, and Federated Learning ensures secure decentralised data handling. Preliminary models demonstrate significant improvements in classification accuracy, reduced manual intervention, and compliance with safety policies. This theoretical framework provides a scalable solution for hazardous waste management in healthcare and other industries, with a small-scale experiment that validates AI models.

Authors

  • Amit Krishan Kumar
    State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China. Electronic address: fste_11@yahoo.com.
  • Yasir Ali
    Loughborough University, School of Architecture, Building and Civil Engineering, Loughborough LE11 3TU, United Kingdom. Electronic address: y.y.ali@lboro.ac.uk.
  • Rahul R Kumar
    School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Suva, Fiji. Electronic address: rahul.kumar@usp.ac.fj.
  • Mansour H Assaf
    School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Suva, Fiji. Electronic address: mansour.assaf@usp.ac.fj.
  • Sadia Ilyas
    Process Metallurgy, Minerals and Metallurgical Engineering Division, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå 97187, Sweden; Wallenberg Initiative Materials Science for Sustainability (WISE), Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå 97187, Sweden. Electronic address: sadia.ilyas@ltu.se.

Keywords

External Resources

Popular Topics
Recent Journals