Dynamic convolutional neural network based e‐waste management and optimized collection planning
Electronic waste, also known as e‐waste, refers to electrical or electronic devices that are
discarded from households and workplaces. These used e‐wastes are meant to be …
discarded from households and workplaces. These used e‐wastes are meant to be …
Optimizing E-waste management: Deep learning classifiers for effective planning
E-waste demands urgent attention to devise effective and sustainable waste management
solutions. In response to this challenge, the integration of deep learning classifiers emerges …
solutions. In response to this challenge, the integration of deep learning classifiers emerges …
Real time electronic-waste classification algorithms using the computer vision based on convolutional neural network (cnn): Enhanced environmental incentives
An innovative approach is needed to boost the economic value of e-waste by improving
metal recovery and facilitating the separation of plastics and valuable metal components …
metal recovery and facilitating the separation of plastics and valuable metal components …
An optimal and smart E-waste collection using neural network based on sine cosine optimization
S Ravi, S Venkatesan… - Neural Computing and …, 2024 - Springer
Electronic waste (e-waste) is considered a major issue that our world is tackling nowadays.
This electronic waste causes various health issues to animals as well as human beings …
This electronic waste causes various health issues to animals as well as human beings …
[HTML][HTML] Application of deep learning object classifier to improve e-waste collection planning
P Nowakowski, T Pamuła - Waste Management, 2020 - Elsevier
This study investigates an image recognition system for the identification and classification
of waste electrical and electronic equipment from photos. Its main purpose is to facilitate …
of waste electrical and electronic equipment from photos. Its main purpose is to facilitate …
Design and development of smart Internet of Things–based solid waste management system using computer vision
SS Mookkaiah, G Thangavelu, R Hebbar… - … Science and Pollution …, 2022 - Springer
Municipal solid waste (MSW) management currently requires critical attention in ensuring
the best principles of socio-economic attributes such as environmental protection, economic …
the best principles of socio-economic attributes such as environmental protection, economic …
Machine learning for the classification and separation of e-waste
EP Zhou - 2022 IEEE MIT Undergraduate Research …, 2022 - ieeexplore.ieee.org
The amount of global e-waste is growing at a rapid rate and is projected to increase to 74.7
Mt by 2030. However, according to a recent United Nation's study in 2019, the collection and …
Mt by 2030. However, according to a recent United Nation's study in 2019, the collection and …
Optimized deep learning-based e-waste management in IoT application via energy-aware routing
P Ramya, V Ramya, M Babu Rao - Cybernetics and Systems, 2023 - Taylor & Francis
Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for
managing E-waste in use today. In essence, there is no ideal method of managing E-waste …
managing E-waste in use today. In essence, there is no ideal method of managing E-waste …
Comparison of different computer vision approaches for e-waste components detection to automate e-waste disassembly
AM Bassiouny, AS Farhan, SA Maged… - 2021 International …, 2021 - ieeexplore.ieee.org
Electronic Waste (E-waste) is generated in a tremendous amount due to our increasing
dependence on electronic devices and rapid upgrading in technological innovations …
dependence on electronic devices and rapid upgrading in technological innovations …
Efficient future waste management: a learning-based approach with deep neural networks for smart system (LADS)
Waste segregation, management, transportation, and disposal must be carefully managed to
reduce the danger to patients, the public, and risks to the environment's health and safety …
reduce the danger to patients, the public, and risks to the environment's health and safety …