Alourani Abdullah, Ashraf M Usman, Aloraini Mohammed
Department of Management Information Systems, College of Business and Economics, Qassim University, Buraydah, Saudi Arabia.
Computer Science, GC Women University Sialkot, Sialkot, Punjab, Pakistan.
PeerJ Comput Sci. 2025 Apr 1;11:e2777. doi: 10.7717/peerj-cs.2777. eCollection 2025.
The effective management of municipal solid waste is a critical global issue, affecting both urban and rural areas. To address the growing volume of solid waste, proactive planning is essential. Traditionally, solid waste is often disposed of without segregation, preventing recycling and the recovery of raw materials. Proper waste segregation is a fundamental requirement for effective solid waste management, allowing materials to be recycled efficiently. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) offer powerful tools for identifying recyclable materials like glass, plastic, and metal within solid waste. The primary goal of this research is to contribute to a cleaner environment, reduce infant mortality, improve maternal health, and support efforts to combat HIV/AIDS, malaria, and other diseases. This study introduces an intelligent and smart solid waste management system (iSSWMs) designed to smartly collect and segregate solid waste. The proposed system focuses on three types of materials: plastic, glass, and metal. The first phase involves waste collection using smart bins connected to a mobile application, which sends notifications when the bins are full. In the second phase, we develop a deep learning-based mechanical model to segregate the waste, using the VGG-19 model, which achieved a performance accuracy of 99.7% during training. To the best of our knowledge, iSSWMs is a promising framework that integrates both waste collection and segregation through the use of cutting-edge technologies, delivering high accuracy and efficiency.
城市固体废物的有效管理是一个关键的全球性问题，影响着城市和农村地区。为了应对日益增长的固体废物量，积极规划至关重要。传统上，固体废物常常未经分类就被处理，这阻碍了回收利用和原材料的回收。正确的垃圾分类是有效固体废物管理的基本要求，能使材料得到高效回收。人工智能（AI）、机器学习（ML）和物联网（IoT）等新兴技术为识别固体废物中的玻璃、塑料和金属等可回收材料提供了强大工具。本研究的主要目标是为营造更清洁的环境、降低婴儿死亡率、改善孕产妇健康以及支持抗击艾滋病毒/艾滋病、疟疾和其他疾病的努力做出贡献。本研究引入了一种智能固体废物管理系统（iSSWMs），旨在智能地收集和分类固体废物。该系统主要针对三种材料：塑料、玻璃和金属。第一阶段涉及使用连接到移动应用程序的智能垃圾桶收集垃圾，垃圾桶装满时会发送通知。在第二阶段，我们使用VGG - 19模型开发了一个基于深度学习的机械模型来分类垃圾，该模型在训练期间的性能准确率达到了99.7%。据我们所知，iSSWMs是一个很有前景的框架，它通过使用前沿技术整合了垃圾收集和分类，具有很高的准确性和效率。
