Revolution in Domestic Waste Management system using Machine Learning Algorithm
Syeda Umme Kulsum, Sreekantha B, Shantarani S B, Syed Rashid Ahmed, Tousif Saqlain, Sujith Kumar N
Social Score, Web Application, Scheduled Clean up Notifications, Machine Learning, Internet of Things, Community Engagement.
In an effort to improve community involvement and expedite the waste management process, this initiative presents a trans formative strategy. By uploading images, users may easily report issues relating to waste. Trash cans are monitored by smart cameras that are positioned strategically to provide real-time data on the amount of waste and the cans' fill levels. This technology application makes it easier to classify different kinds of waste. In addition, the technology rapidly alerts authorities to the accumulation of excessive waste in a given location, facilitating prompt cleanup actions. The companion web application encourages more community participation in maintaining cleanliness by providing user-friendly features like scheduled cleanup notifications. The project's development of the 'Social Score,' an innovative incentive system that rewards people for taking environmentally beneficial acts, is one of its distinctive features. This innovation encourages more people to take an active role in the campaign. In addition to managing waste, the project aims to promote community development. It highlights diversity and calls on everyone to contribute to making the environment greener and cleaner. The project intends to aid in the creation of more visually beautiful and healthful living environments by streamlining waste management procedures and emphasizing community involvement. The addition of smart cameras which can determine waste amounts and garbage can status is a major improvement. In line with the project's objective of developing cleaner and more sustainable communities, this technological advancement guarantees a more effective and efficient approach to environmental stewardship.
Article Details
Unique Paper ID: 163509

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2169 - 2173
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