Waste management using machine learning and deep learning algorithms

Sami, Khan Nasik and Amin, Zian Md Afique and Hassan, Raini (2020) Waste management using machine learning and deep learning algorithms. International Journal on Perceptive and Cognitive Computing (IJPCC), 6 (2). pp. 97-106. E-ISSN 2462-229X

PDF - Published Version Restricted to Registered users only Download (722kB) | Request a copy

Abstract

Waste management is one of the essential issues that the world is currently facing, and it does not matter if the country is developed or underdeveloped. The key issue in this waste segregation is that the trash bin at open spots gets flooded well ahead of time before the beginning of the cleaning process. The cleaning process involves with the isolation of waste that could be due to unskilled workers, which is less effective, time-consuming, and not plausible because the reality is, there is a lot of waste. So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. The goal of this task is to gather a dataset and arrange it into six classes consisting of glass, paper, metal, plastic, cardboard, and waste. The model that we have used are the classification models. For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. For our model, we found CNN accomplished high characterization on classification accuracy, which is around 90%, while SVM indicated an excellent transformation to various kinds of waste, with 85% classification accuracy, and Random Forest and Decision Tree have accomplished 55% and 65% classification accuracy respectively.

Item Type:	Article (Journal)
Additional Information:	4964/86320
Uncontrolled Keywords:	Waste Classification, Image Classification, Machine Learning, Deep Learning, Random Forest, Support Vector Machine, Decision Tree, Convolutional Neural Network, Sustainable Development Goals
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Raini Hassan
Date Deposited:	16 Dec 2020 13:15
Last Modified:	22 Dec 2020 17:33
URI:	http://irep.iium.edu.my/id/eprint/86320

Actions (login required)

View Item