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 Kumar Singh, Samit; Chaurasia, Amrita; Ruzimbaevich, Aybek Kalandarov; Kavitha, G.L. Title A name given to the resource AI-Powered Smart Waste Management Systems Using Cloud Analytics 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 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234652" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234652</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030099221?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030099221?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 Kumar Singh S., Bhoj Reddy Engineering College for Women, Department of Mechanical Engineering - H &amp; S, Hyderabad, India; Chaurasia A., Christ ( Deemed to be University), School of Commerce, Finance and Accountancy, Delhi NCR, India; Ruzimbaevich A.K., Mamun University, Department of Roman-Germanic Philology, Khiva, Uzbekistan; Kavitha G.L., Kmct Institute of Emerging Technology and Management, Department Cse, Kerala, India Description An account of the resource The increasing volume of trash and urbanisation has made it essential for waste management to be innovative, sustainable and effective. This study offers a smart waste management system that optimises garbage collection, sorting &amp; recycling using AI, Internet of Things and cloud computing. Real-time monitoring, automated garbage categorisation, predictive fill-level forecasting &amp; intelligent route planning are possible with a hybrid edge-cloud architecture. Technology improves operational efficiency &amp; environmental sustainability over conventional trash systems by incorporating machine learning algorithms and real-time sensor data. Multi-modal data fusion, scalability, cloud analytics &amp; adaptive feedback mechanisms enable system improvement. Sensor upgrades and staged implementation tactics assist in handling IoT adoption issues, including challenging trash categorisation accuracy and high startup expenses. This comprehensive strategy supports smart city development and global sustainability. Future improvements include blockchain for waste tracking and 5 G-powered edge AI for automatic garbage processing. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Cloud Analytics; Hybrid Edge Cloud Architecture; Internet of Things; Route Planning; Waste Management Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; 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