IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Dual-CNN-Based Waste Classification System Using IoT and HDS Algorithm

Dual-CNN-Based Waste Classification System Using IoT and HDS Algorithm
View Sample PDF
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

Purchase

View Dual-CNN-Based Waste Classification System Using IoT and HDS Algorithm on the publisher's website for pricing and purchasing information.

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.

Related Content

Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid. © 2026. 32 pages.
Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid. © 2026. 32 pages.
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi. © 2026. 32 pages.
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi. © 2026. 36 pages.
Salaheldin Mohamed Ibrahim Edam. © 2026. 42 pages.
Rubi Kadyan, Sunita Rani, Vinod Kr. Saroha. © 2026. 46 pages.
Mamoon M. Saeed, Zeinab E. Ahmed, Rania A. Mokhtar, Rashid A. Saeed. © 2026. 34 pages.
Body Bottom