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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM40491

Waste Management System ECOSORT -AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM40491

Volume:

9 |

1 |

Jan 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM40491

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SSM

Sahana Sharma M

ASP

Ankita S Patil

RH

Raksha H

SS

Sameer Singh

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Abstract

Efficient waste management is crucial for sustainable urban living. However, challenges such as improper segregation, low recycling rates, and reliance on manual systems hinder progress toward environmental goals. This paper introduces EcoSort AI, an AI-driven waste management solution that combines computer vision, IoT technologies, and machine learning to automate and optimize waste segregation. Leveraging convolutional neural networks (CNNs), the system identifies, classifies, and sorts waste materials into appropriate categories, ensuring improved recycling rates and reduced landfill burden. EcoSort AI features IoT-enabled smart bins for real-time classification and integrates seamlessly into existing urban infrastructures. Experimental results demonstrate significant improvements in sorting accuracy, efficiency, and public engagement.

KEYWORDS

Waste segregation artificial intelligence IoT-enabled bins CNNs smart cities recycling optimization sustainable development.

Cite This Article

Sahana Sharma M, Ankita S Patil, Raksha H, Sameer Singh, "Waste Management System ECOSORT -AI," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 1, Jan 2025. https://doi.org/10.55041/IJSREM40491

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Publication Date

1/13/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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