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Dual-CNN-Based Waste Classification System Using IoT and HDS Algorithm
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Author(s): A. V. Kalpana (SRM Institute of Science and Technology, India), S. Suchitra (SRM Institute of Science and Technology, India), Ram Prasath (SRM Instiute of Science and Technology, India), K. Arthi (SRM Institute of Science and Technology, India), J. Shobana (SRM Institute of Science and Technology, India)and T. Nadana Ravishankar (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 23
Source title:
Computational Intelligence for Green Cloud Computing and Digital Waste Management
Source Author(s)/Editor(s): K. Dinesh Kumar (Amrita Vishwa Vidyapeetham, India), Vijayakumar Varadarajan (The University of New South Wales, Australia), Nidal Nasser (College of Engineering, Alfaisal University, Saudi Arabia)and Ravi Kumar Poluru (Institute of Aeronautical Engineering, India)
DOI: 10.4018/979-8-3693-1552-1.ch015
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Abstract
Efficient waste management is crucial in today's environmental landscape, necessitating comprehensive approaches involving recycling, landfill practices, and cutting-edge technological integration. The proposed approach introduces a sophisticated waste management system, harnessing dual or twofold convolutional neural networks (D-CNN or TF-CNN) and a histogram density segmentation (HDS) algorithm. This intelligent system equips users with the means to enact essential safety protocols while handling waste materials. Notably, this research presents groundbreaking contributions: Firstly, a geometrically designed smart trash box, incorporating ultrasonic and load measurement sensors controlled by a microcontroller, aimed at optimizing waste containment and collection. Secondly, an intelligent method leverages deep learning for the precise classification of digestible and indigestible waste through image processing. Lastly, a cutting-edge real-time waste monitoring system, employing short-range Bluetooth and long-range IoT technology through a dedicated Android application was proposed.
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