The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Sustainable Waste Management OOA-Enhanced MobileNetV2-TC Model for Trash Image Classification
|
|
Author(s): B. Manjunatha (New Horizon College of Engineering, India), K. Dinesh Kumar (Amrita Vishwa Vidyapeetham, India), Sam Goundar (RMIT University, India), Balasubramanian Prabhu Kavin (SRM Institute of Science and Technology, India)and Gan Hong Seng (XJTLU Entrepreneur College, Xi'an Jiaotong-Liverpool University, China)
Copyright: 2024
Pages: 21
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.ch012
Purchase
|
Abstract
E-waste is an invisible, indirect waste that contaminates natural resources like the air, water, and soil, endangering the ecosystem, people, and animals. Long-term waste accumulation and contamination can harm the resources found in the environment. Since traditional waste management systems are very inefficient and the number of people living in urban areas is increasing, waste management systems in these areas face challenges. However, by combining a variety of sensors with deep learning (DL) models, waste resources can be used effectively. For this chapter, firstly, the Trashnet dataset with 2527 images in six classes and the VN-trash dataset, which comprises three classes and 5904 images, are collected. Then the collected images are preprocessed using truncated gaussian filter. After that, pre-trained convolutional neural network (CNN) models (Resnet20 and VGG19) are applied to the images in order to extract features. In order to enhance the predictive performance, this study then creates a MobileNetV2 model for trash classification (TC) called MNetV2-TC.
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.
|
|
|