Abstract
Electronic garbage, or “e-waste,” may be hazardous to the environment due to its composition. Environmental scientists are focusing more on the dangerous heavy metal pollution in e-waste sites due to how deadly and persistent it is. The bulk of electronic waste has been disposed of improperly due to poor handling practises that compromise safety and environmental protection. In order to anticipate the composition of e-waste and metal, a Long Short-Term Memory (LSTM) with attention layer technique was proposed. Data from the E-waste recycling facility is used, and it has been preprocessed, to exclude extreme points that usually appeared when using alternative fuels. The preprocessed data is then obtained by the testing and training operations. Since the gates allow the input characteristics to flow through the hidden layers without modifying the output, the proposed LSTM network is simple to optimise. By examining the well-known feature maps from the prediction branch, the attention layer makes advantage of the correlation between the class labels.
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Raghavendra, T.S., Nagaraja, S.R., Mohan, K.G. (2023). LSTM with Attention Layer for Prediction of E-Waste and Metal Composition. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_50
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DOI: https://doi.org/10.1007/978-981-99-6702-5_50
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