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AI-Driven Biomedical Waste Management System for Hospitals Using CNN-Based Classification and Sustainable Disposal Recommendations
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Author(s): Sabiyath Fatima Nakeeb (B.S. Abdur Rahman Crescent Institute of Science and Technology, India), Syed Masood Mohamed Mustafa (B.S. Abdur Rahman Crescent Institute of Science and Technology, India), Sabura Faheema Syed Masood (B.S. Abdur Rahman Crescent Institute of Science and Technology, India)and Shamira Anjum Babulal (B.S. Abdur Rahman Crescent Institute of Science and Technology, India)
Copyright: 2026
Pages: 34
Source title:
Evaluation and Assessment of AI-Driven Systems in Hospitals
Source Author(s)/Editor(s): Anandavalli M. (King Khalid University, Saudi Arabia), Prabhu Chakkaravarthy (SRM Institute of Science and Technology, India)and Dhanalakshmi J. (SRM Institute of Science and Technology, India)
DOI: 10.4018/979-8-3373-2787-7.ch011
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Abstract
In the pursuit of greener, safer, and more efficient hospital waste management systems, this chapter introduces an AI-driven biomedical waste management framework designed specifically for hospitals. The proposed solution integrates a CNN-based for real-time biomedical waste classification with an ontology-enhanced content-based recommender system that provides sustainable and regulation-compliant disposal suggestions. The system employs a Raspberry Pi camera and ultrasonic sensors to classify waste into key biomedical categories—Infectious & Pathological Waste, Contaminated Recyclables, Sharps, and Glassware—while simultaneously monitoring bin capacity to prevent overfilling and cross-contamination. Upon detecting hazardous or full-bin conditions, the recommender system intelligently maps the classified waste to appropriate disposal methods such as autoclaving, incineration, secure landfill, or recycling. Experiments show that the RWC-Net model achieves 95.26% classification accuracy, while the recommender system demonstrates a 97% accuracy rate in suggesting disposal pathways.
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