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<p>The rising speed of urbanization, together with growing populations and changing consumer behavior, has created excessive waste production that needs sustainable methods of resolution. This research develops a circular economy (CE) system that integrates CNN and IoT technologies into waste elimination by utilizing the VGG16 deep learning algorithm with 97.52% precision for material recognition and IoT devices for immediate surveillance operations. Through their partnership, AI, IoT, and cloud computing systems optimize waste sorting procedures and aid recycling processes, as well as lead to data-driven business decisions. The framework integrates waste sorting done by self-operating systems with vehicle route calculation based on probabilities, together with IoT automated system maintenance, which results in diminished usage of landfills alongside reduced environmental impacts and operating expenses. The AI model analyzes 10,000 waste images during its training process to enhance waste sorting capabilities, while cloud platforms ensure secure, time-sensitive data processing. Studies within the hospitality sector in Singapore show that the approaches work effectively, and a combination of discussions about AI data equivalence alongside ethical AI issues, together with societal acceptance hindrances, demonstrate implementation difficulties. This study introduces advanced technologies that provide innovative waste management solutions, supporting sustainability practices and circular economies. It also delivers essential knowledge to public officials, industrial entities, and community members, helping to strengthen global waste programs, protect ecosystems, and drive economic progress.</p>

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An AI-Driven framework for smart waste classification and recycling: enabling circular economy through CNN and IoT integration

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • M. Mustafa,
  • B. Louhichi,
  • Q. S. Khalid,
  • M. Rizwan

摘要

The rising speed of urbanization, together with growing populations and changing consumer behavior, has created excessive waste production that needs sustainable methods of resolution. This research develops a circular economy (CE) system that integrates CNN and IoT technologies into waste elimination by utilizing the VGG16 deep learning algorithm with 97.52% precision for material recognition and IoT devices for immediate surveillance operations. Through their partnership, AI, IoT, and cloud computing systems optimize waste sorting procedures and aid recycling processes, as well as lead to data-driven business decisions. The framework integrates waste sorting done by self-operating systems with vehicle route calculation based on probabilities, together with IoT automated system maintenance, which results in diminished usage of landfills alongside reduced environmental impacts and operating expenses. The AI model analyzes 10,000 waste images during its training process to enhance waste sorting capabilities, while cloud platforms ensure secure, time-sensitive data processing. Studies within the hospitality sector in Singapore show that the approaches work effectively, and a combination of discussions about AI data equivalence alongside ethical AI issues, together with societal acceptance hindrances, demonstrate implementation difficulties. This study introduces advanced technologies that provide innovative waste management solutions, supporting sustainability practices and circular economies. It also delivers essential knowledge to public officials, industrial entities, and community members, helping to strengthen global waste programs, protect ecosystems, and drive economic progress.