NEURO SORT: AI-DRIVEN WASTE INTELLIGENCE SYSTEM

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Surpurkar Abhinav Narendra
Maniyar MD Umar Nizamaoddin
Korekar Supriya Satish
Muskan Tayyab Sayyad
Umbarje Yash Rajkumar
Kharosekar Aishwarya Sudhir

Abstract

Effective waste operation is pivotal for environmental sustainability, yet homemade sorting remains time- consuming and error-prone. NeuroSort AI- Powered Waste Bracket System leverages artificial intelligence to automate waste identification with over 95 delicacy. By classifying waste into four primary orders — Recyclable,Non-recyclable, Organic, and Dangerous the system enhances waste isolation effectiveness. druggies can input waste data via image uploads or real- time camera prisoner, making the platform largely accessible across multiple bias, including mobile phones. Advanced analytics give real- time waste distribution perceptivity, performance shadowing, and literal data analysis, enabling informed decision- making for businesses and environmental associations. By reducing sorting time by 40 and perfecting recycling delicacy by 60, NeuroSort significantly lowers waste operation costs and minimizes environmental impact, contributing to a cleaner and further sustainable future.

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How to Cite
Surpurkar Abhinav Narendra, Maniyar MD Umar Nizamaoddin, Korekar Supriya Satish, Muskan Tayyab Sayyad, Umbarje Yash Rajkumar, & Kharosekar Aishwarya Sudhir. (2025). NEURO SORT: AI-DRIVEN WASTE INTELLIGENCE SYSTEM. JournalNX - A Multidisciplinary Peer Reviewed Journal, 11(4), 10–18. Retrieved from https://repo.journalnx.com/index.php/nx/article/view/5541