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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Literature Analysis: Machine Learning and AIDriven Real-Time Waste Classification Technologies.

Author(s):Dr. Amith Shekhar C 1, P Asha Bhat2, Tejas Nagaraj Hegde3, Skanda J4

Affiliation: 1,2,3,4 Dept. of CSE, BNM Institute of Technology, Bangalore, Karnataka, India

Page No: 43-50

Volume issue & Publishing Year: Volume 2 Issue 1,Jan-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.17674123

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Article Indexing:

Abstract:
Waste segregation is an increasingly key difficulty in today's world, particularly with the rise of urbanization. Effective waste management is essential in maintaining an ecological balance. Proper disposal of waste at dumping sites is essential, and sorting waste at the initial stage is a key component of this process. However, traditional waste sorting methods require more time and manpower. Image processing offers a promising approach to automate the analysis and classification of waste, making it a productive solution for waste management. This paper aims to review existing global research on the topic, providing insights into the challenges faced, the algorithms employed, and the methods used in various studies. By examining these studies, the paper seeks to identify the most suitable algorithms for future research. Additionally, it discusses the different approaches and proposed systems for waste segregation, highlighting the limitations of current systems and the algorithms they utilize. Ultimately, this paper provides opportunities to generate
new knowledge and develop improved waste management systems.

Keywords: Urbanization, Automate, Image Processing, Segregation, Waste Classification.

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