… of the selected e-waste categories … , e-waste collection companies can prepare a collection
plan by assigning a sufficient number of vehicles and payload capacity for a specific e-waste …

… The main purpose of this study is to design and introduce a robust E-waste management
approach, named FrHHGO-based ShCNN. The key concerns of the research work are …

… E-waste classification is fulfilled using fractional Henry … deep convolutional neural network
(FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste …

… e-waste. This paper presents the utilization of ML (Machine Learning) for E-Waste Management
… boosting regression tree (GBRT) and Neural Network (NN) ML calculations to gauge …

… is the third-largest e-waste generator. The management of e-waste is complex because of
less awareness. This paper aims to improve e-waste management by segregating them from …

… a smart e-waste management system that … train a deep learning model to perform real-time
e-waste object detection, providing a more comprehensive view of the e-waste management …

… known as e-waste management by exploiting the dynamic convolutional neural network (DCNN). …
Machine learning models have the drawback of just having a few examples of each …

… , the integration of deep learning classifiers emerges as a promising avenue to optimize
the planning of e-waste collection. By harnessing the capabilities of deep learning, these …

… as e-waste. The model utilized a CNN model to classify and an RCNN model to identify several
types of e-waste. … systems using a deep learning system, but waste management with IoT …

… deep learning technique has been introduced to enable a novel robotic application in shredded
electronic waste (e-waste… ; commonly found in shredded e-waste. Speed and accuracy …