IJBTT

An Improved Model for Waste Management Recommender System in Rivers State Using Deep Learning Approach
	© 2020 by IJCTT Journal
	Volume-68 Issue-4
	Year of Publication : 2020
	Authors : Onuodu, Friday Eleonu, Nlerum, Promise Anebo
	DOI :  10.14445/22312803/IJCTT-V68I4P130

How to Cite?

Onuodu, Friday Eleonu, Nlerum, Promise Anebo, "An Improved Model for Waste Management Recommender System in Rivers State Using Deep Learning Approach," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 193-203, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P130

Abstract
The unavailability of Waste Sort Recyclables (WSR) is a serious waste management problem in Rivers State, Nigeria. WSR is a deep learning process that involves the classification of waste into four recycling categories which include glass, paper, metal and plastic. Secondly, there is need for a recommender system for relevant waste management agencies in Nigeria. In this study, we developed an improved model for Waste Management Recommender System (IWMRS) in Nigeria using Deep Learning approach. Software Development and Lifecycle Methodology (SDLC) was utilized in this approach. Furthermore, we implemented with Hypertext Pre-processor, JavaScript Programming Languages and MySQL Relational Database as backend. The parameters for our results performance achieved an overall performance rate of 94% when compared with the most recent Waste Management System. The parameters for the comparative analysis included Time Complexity (TC), Life- Cycle Assessment (LCA), Benchmarking (B), Multi-Criteria Decision Making (MCDM), Risk Assessment (RA), Cost Benefit Analysis (CBA) and Speed (S) which was also presented as TC, LCA, B, MCDM, RA, CBA, S = 20, 36, 14, 10, 7, 2, 5 respectively as compared with the existing parameters values of 14, 31, 14, 10, 7, 2 and 5 and further confirmed outperformance of the existing system by the proposed system. The obtained results show also the importance of Deep Learning techniques in Recommender Systems. This is because we live in a World of Information and Big Data. In addition, we boldly recommend this study to seekers of waste management information through recommender systems that utilizes Deep Learning Techniques.

Keywords
Deep Learning Approach, IWMRS, Recycling, SDLC, WSR.

Reference
[1] K. Alexander, ?Artificial Intelligence in Automated Sorting in Trash Recycling?, an International Conference Paper Published at https://www.researchgate.net/publication/326994757, 2018
[2] B. Glouche, A Smart Waste Management System with selfdescribing objects, in the second International Conference on Smart Systems, Devices and Technologies (SMART‘ 13), 2013
[3] H. Raveesh, K. Lola, J. Goin, Waste Management Initiatives in India for Human Well-Being, European Scientific Journal, Special Edition: ISSN: 1857 – 7881, pp. 105 -127, 2018
[4] D. Cenk, RecycleNet: Intelligent Waste Sorting using Deep Neutral Networks, An International Conference Paper submitted at https://www.researchgate.net/325626219, 2018
[5] J. Kate, Recommendation for Waste, Waste Reduction and Recycling, International Journal of Computer Applications (IJCA), vol. 4, no. 3, pp. 117 – 129, 2019
[6] O. Ahmed, Solid Management Practices in two Northern Manitoba First Nations Communities: Community Perspective on the Issues and Solutions, a Thesis Submitted to the Faculty of Graduate Studies, University of Manitoba, 2016
[7] J. Awomeso, Waste Management Disposal and Pollution Management in Urban Areas: A Workable Remedy for the Environment in Developing Countries, American Journal of Environmental Sciences, vol. 6, no. 1, 26 – 32, 2016
[8] V. Danilo, Sustainable Solid Waste Management System: The CLSU International Journal of Science and Technology, vol. 1, no. 2, pp. 15 – 25, 2016
[9] R. Elena, Analysis and Measures to Improve Waste Management in Schools, an International Article for the University of Trento, 2019
[10] A. Taiwo, Waste Management Disposal and Pollution Management in Urban Areas: A Workable Remedy for the Environment in Developing Countries, American Journal of Environmental Sciences, vol. 6, no. 1, pp. 26 – 32, 2014.