Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Du Ni, Zhi Xiao, Ming Lim

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    49 Downloads (Pure)

    Abstract

    Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries.
    Original languageEnglish
    Pages (from-to)7907-7927
    Number of pages21
    JournalSoft Computing
    Volume25
    Issue number12
    Early online date25 Jan 2021
    DOIs
    Publication statusPublished - Jun 2021

    Bibliographical note


    The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-021-05579-7


    Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

    This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

    Funder

    National Natural Science Foundation of China (Grant Nos. 71671019, 72071021, and 71871034).

    Keywords

    • Algorithms
    • Literature review
    • Machine learning
    • Recycling

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Geometry and Topology

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