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Smart city article details

Title	E-Waste Management Using Machine Learning
ID_Doc	21550
Authors	Li H.; Jin Z.; Krishnamoorthy S.
Year	2021
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3469968.3469973
Abstract	In the realm of innovation, as individuals begin adjusting to new development, advancement begins clearing its way into individuals' lives. Each adjustment in innovation gets better than ever, with old gadgets being supplanted and deserted. Such Electronic and Electrical Equipments (EEEs) that are disposed of by clients are named e-waste. EEEs are comprised of a large number of segments, some containing poisonous substances that affect human wellbeing and climate, if not handled appropriately. The volume of EEEs that is delivered all through the world, has driven governments in different nations to make severe arrangements, to guarantee effective removal of the created e-waste. This paper presents the utilization of ML (Machine Learning) for E-Waste Management framework with a center to build up a prescient model by contrasting the presentation of gradient boosting regression tree (GBRT) and Neural Network (NN) ML calculations to gauge week by week e-wastage for every Urban sub-segments. Various information-driven strategies incorporate the current designed model and its alteration alongside regular machine learning calculations. The utilization of Machine Learning calculation gives improved arrangement exactness of 99.1 % when utilizing the best performing algorithm. Notwithstanding the prominent quantitative upgrades, the proposed plan can likewise help in enhancing long-haul e-waste management in smart-city ambient utilizing the recorded insights. © 2021 ACM.
Author Keywords	data mining; E-waste management; Random Forest algorithm

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