Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rao, Devnarayan G.; Sangeetha, G.; Sandeep, J.; Sreeja, C.S.; Vincent, Agnes Nalini; Mary, Teena Title A name given to the resource Proposing an AI-Enabled Waste Segregation System for Domestic Settings Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1378 LNNS;pp.21-33 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5781-0_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5781-0_3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015711959?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015711959?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rao D.G., Department of Computer Science, Christ University, Bangalore, India; Sangeetha G., Department of Computer Science, Christ University, Bangalore, India; Sandeep J., Department of Computer Science, Christ University, Bangalore, India; Sreeja C.S., Department of Computer Science, Christ University, Bangalore, India; Vincent A.N., Department of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius; Mary T., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource This paper proposes an innovative AI-based system for automated domestic waste segregation. Utilizing Teachable Machine and MobileNet, the system accurately categorizes waste into dry and wet components, laying the foundation for sustainable waste management practices. Embedded in a Raspberry Pi 4, the system integrates real-time image processing with various sensors to streamline the sorting process. While the model has been simulated due to budgetary constraints, future implementation envisions real-world application. Potential advancements include expanding the dataset, enabling multi-category waste classification, and exploring low-power alternatives. This research contributes to the evolving landscape of smart waste management, addressing environmental sustainability and the pressing need for automated, efficient waste segregation at the domestic level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence for sustainability; IoT; Smart waste management; Transfer learning; Waste segregation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965780-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online