Abstract
For the development of smart cities, electronic waste generation is one of the significant issues to tackle. According to the United Nations, India is the third-largest e-waste generator. The management of e-waste is complex because of less awareness. This paper aims to improve e-waste management by segregating them from general waste, tracking their movement and illegal recoveries. We have taken two types of image data. The electronic dataset contains desktop–laptop, mouse, batteries and keyboard. Data with name other contains paper, plastic and cardboard. We have classified images into two classes, using various deep learning techniques, and the CNN model gives 94% training accuracy. We have analyzed multiple filters and kernel sizes to improve the classification further. The assumption is, if the waste is other than electronic waste, the smart bins will not open. Additionally, we are working on an advanced setup that will manage the e-waste according to its nature.
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Naushin, M., Saraswat, A., Abhishek, K. (2022). Deep Learning Approach to Deal with E-Waste. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_9
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DOI: https://doi.org/10.1007/978-981-19-0840-8_9
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