… With the help of advanced technologies like artificial intelligence and machine learning,
we can decompose e-waste in a proper manner or we can easily recycle it and use for further …

… The deep learning architectures learn rich features from a large … comprehensive and direct
learning [18]. A Convolutional … used both traditional machine learning and deep learning for …

… e-waste management issues and challenges. Chapter 5 deals with the paradigm of machine
learning that includes deep learning … of e-waste disposal and its proper management are …

… Today, with advancements in deep learning algorithms, computer vision-based systems
have been used in various contexts to achieve operational excellence. Extant literature has …

… processed by machine learning (ML) and data-analytics algorithms. The results are parsed
to decision support systems (DSS) that outcome the organization’s administration strategy. …

… the e-waste management research topics at a global level in academia and explore the smart
management scenario of e-waste … [27] apply a deep learning convolutional neural network (…

… wastes, which are commonly referred to as e-waste. Due to unplanned location of dustbin as
well as … Object recognition can be made possible by employing machine learning methods. …

… the e-waste classification using a machine-learning model to … In this study, deep learning
methods can be observed to have … stage for waste management and waste disposal processes. …

… In contrast, it is non-biodegradable if it is in any one of the classes among E-waste, glass,
metal, and plastic. Table 1 represents sample images of bio-degradable wastes, and Table 2 …

… devices become e-waste. This research work aims at using a popular machine learning (ML) …
(CS) and tensile strength (TS) of E-waste aggregate-based concrete (EWAC). 279 and 105 …