Kumar Amit Krishan, Ali Yasir, Kumar Rahul R, Assaf Mansour H, Ilyas Sadia
Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.
Department of Telecommunication and Teleinformatics, Joint Doctoral School, Silesian University of Technology, Gliwice 44-100, Poland; School of Automation, Beijing Institute of Technology, Beijing, 100081, China; Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, 450000, China.
Waste Manag. 2025 Jul 15;203:114816. doi: 10.1016/j.wasman.2025.114816. Epub 2025 Apr 30.
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.
医院中的医疗活动会产生多种可归类为危险废物的使用后废料。本研究提出了一种人工智能（AI）与物联网（IoT）集成框架，用于医院危险废物的安全高效管理。配备物联网锁的智能垃圾桶确保废物收集，而卷积神经网络（CNN）和自适应神经模糊推理系统（ANFIS）提高检测和分类准确性。提出了一种动态废物分类机制来管理医院的空间限制。深度强化学习优化消毒调度和废物存储，联邦学习确保安全的分散数据处理。初步模型显示在分类准确性、减少人工干预和符合安全政策方面有显著改进。这一理论框架为医疗保健及其他行业的危险废物管理提供了一个可扩展的解决方案，并通过小规模实验验证了人工智能模型。
