Abstract
Abstract: The escalating global waste production has given rise to significant environmental challenges, encompassing pollution and health risks. Effective waste management hinges on the essential process of garbage categorization. Nonetheless, manual sorting proves to be arduous, error- prone, and time-intensive. To address this, automated garbage classification powered by deep learning has emerged as a promising remedy. This project introduces a garbage classification framework leveraging convolutional neural networks (CNNs) to achieve remarkable precision in distinguishing various garbage types. Comprising preprocessing, feature extraction, and classification stages, this system holds the potential to curtail the necessity for manual intervention in waste management operations. This, in turn, can enhance the efficiency and precision of garbage segregation procedures
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
