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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM47432
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
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 in the world’s cities. Therefore, correct classification and management of e-waste requires the recovery of important information about e-waste. This growing e-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 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.
KEYWORDS
Publication Date
10/4/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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