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To address this issue, a smart e-waste management system based on the Internet of Things (IoT) and Deep Learning (DL) based object detection is designed and developed in this paper. Three state-of-the-art object detection models, namely YOLOv5s, YOLOv7-tiny and YOLOv8s, have been adopted in this study for e-waste object detection. The results demonstrate that YOLOv8s achieves the highest mAP@50 of 72% and map@50-95 of 52%. This innovative system offers the potential to manage e-waste more efficiently, supporting green city initiatives and promoting sustainability. By realizing an intelligent green city vision, we can tackle various contamination problems, benefiting both humans and the environment.<\/jats:p>","DOI":"10.3233\/scs-230007","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:45:32Z","timestamp":1692369932000},"page":"77-98","source":"Crossref","is-referenced-by-count":21,"title":["Smart e-waste management system utilizing Internet of Things and Deep Learning approaches"],"prefix":"10.1177","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7544-9210","authenticated-orcid":false,"given":"Daniel","family":"Voskergian","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Al-Quds University, Jerusalem, Palestine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7223-7256","authenticated-orcid":false,"given":"Isam","family":"Ishaq","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Al-Quds University, Jerusalem, 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