Dynamic convolutional neural network based e‐waste management and optimized collection planning

Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are discarded from households and workplaces. These used e‐wastes are meant to be renovated, reused, recycled, or disposed of, and the processing of these wastes often causes disease and harms the environment. A...

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Bibliographic Details
Published in:Concurrency and computation 2022-08, Vol.34 (17), p.n/a
Main Authors: Latha, C. Jenifa, Kalaiselvi, K., Ramanarayan, S., Srivel, R., Vani, S., Sairam, T. V. M.
Format: Article
Language:English
Subjects:
accuracy
and smart garbage bin
Artificial neural networks
convolution
dynamic CNN
Electronic devices
Electronic waste
electronics equipment
Errors
e‐waste
Garbage collection
Hones
Households
Image classification
Neural networks
sampling
Smartphones
Waste containers
Waste management
Wastes
Workplaces
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Summary:Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are discarded from households and workplaces. These used e‐wastes are meant to be renovated, reused, recycled, or disposed of, and the processing of these wastes often causes disease and harms the environment. As a result, it is important to handle waste and collect it from the disposal site on a regular basis. Besides, in order to separate precious metals from discarded waste, it is important to identify them by category. Therefore, this article proposes a novel method known as e‐waste management by exploiting the dynamic convolutional neural network (DCNN). This enhances the classification accuracy with the aid of exactly mapping the features of the images. Meanwhile, the collection of waste can be optimized in order to reduce the distance and time. The e‐wastes in the smart garbage bin are frequently monitored by smartphone applications to collect the waste on time. Moreover, it also significantly reduces the training error, classification error, localization error, and validation error on the test images. The experimental depicts that the proposed method hones up the classification accuracy to the great extent.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6941