Abstract- Today, developments in technology have changed everyone's lifestyle. Although this innovation is beneficial, it creates serious effects on human health and environmental health. One of the main reasons for this is "e-waste" from electronic products. The use of electronic products worldwide has increased the amount of "e-waste" or electronic waste, which has now become a serious problem. Improper disposal of e-waste has now become an environmental and public health problem as these wastes have become the largest portion of water litter in the world's cities. Therefore, correct classification and management of e-waste requires the recovery of important information about waste. This growing waste is inherently hard and rich in metals such as neodymium, indium, palladium, tantalum, platinum, gold, silver, lead and copper, which can be recovered and brought back into the cycle of production and daily use. In this project, a deep learning model is used to identify e-waste and general waste using image processing. The design model, on the other hand, selects the waste with good accuracy and takes less time. Wastes are divided into two groups according to the amount or value in the waste. By using this model effectively, we can solve e-waste management problems, improve recycling and contribute to environmental sustainability.
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Contents - e-waste management, Internet of Things, machine learning, imaging, smart green city etc.
Cite:
Dr. R. A. Burange*, Parikshit D. Chakole, Om P. Agre, Umendra Thakre, "Development of E Waste Management System Using Machine Learning", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13364.
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DOI:
10.17148/IJARCCE.2024.13364
