Ni, D., Xiao, Z. and Lim, M. K. (2021) Machine learning in recycling business: an investigation of its practicality, benefits and future trends. Soft Computing, 25(12), pp. 7907-7927. (doi: 10.1007/s00500-021-05579-7)
Full text not currently available from Enlighten.
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.
| Item Type: | Articles |
|---|---|
| Keywords: | Algorithms, Literature review, Machine learning, Recycling |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
| Authors: | Ni, D., Xiao, Z., and Lim, M. K. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | Soft Computing |
| Publisher: | Springer |
| ISSN: | 1432-7643 |
| ISSN (Online): | 1433-7479 |
| Published Online: | 25 January 2021 |
University Staff: Request a correction | Enlighten Editors: Update this record
Altmetric
Altmetric