Smart Waste Management and Classification System for Smart Cities using Deep Learning

Hasan, M K, Khan, M A, Issa, G, Atta, A, Akram, A S and Hassan, M (2022) Smart Waste Management and Classification System for Smart Cities using Deep Learning. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 16-17 Feb. 2022, Dubai, United Arab Emirates.

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Abstract

For modern city environments to be renewable and clean, waste management and recycling are essential. Solid waste management, disposal, and recycling are issues in many Pakistani cities, particularly Karachi and Lahore. The combination of the IoTs and deep learning offers a modular technique to data categorization and real-time examining. This article illustrates a capable “Smart trash management and categorization system” based on the “internet of things (IoT)” and DL. The article provides an architectural idea for a microchips-based garbage bin that uses numerous measuring instruments to connect with the method to gather wastes as quickly as possible. The “Internet of Things (IoT)” is used in the suggested data monitoring solution to offer real-time data control. In addition, in this smart waste management and categorization scheme, a waste classification model based on convolutional neural networks was deployed. This waste classification technique will be used to sort rubbish into several categories at the waste-collecting plant to increase recycling. This proposed system offers complete trash management and recycling solution in smart cities, from waste collection to waste management and classification.

Affiliation: Skyline University College
SUC Author(s): Issa, G
All Author(s): Hasan, M K, Khan, M A, Issa, G, Atta, A, Akram, A S and Hassan, M
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: IoT,GA,RFID,MENA,MLP
Subjects: B Information Technology > BP Internet of Things
Depositing User: Mr Veeramani Rasu
Date Deposited: 25 May 2022 10:02
Last Modified: 25 May 2022 10:02
URI: https://research.skylineuniversity.ac.ae/id/eprint/227
Publisher URL: https://doi.org/10.1109/ICBATS54253.2022.9759087
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