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 Govind, P Jai; Kumar, Naveen Title A name given to the resource Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers 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 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MAPCON65020.2025.11426455" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MAPCON65020.2025.11426455</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036394036?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036394036?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 Govind P.J., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India; Kumar N., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India Description An account of the resource This paper proposed a convolutional neural network (CNN) based deep learning (DL) approach to predict performance of sustainable microwave absorbers. This study explores the transformative potential of DL in predicting and optimizing microwave absorber performance, offering a datadriven alternative to traditional approaches. The absorber is a composite of tea and carbon powder considered as waste mixed in various composition percentages. The measured S21 data is used for training the proposed DL model. The prediction of absorber's S21 performance shows an accuracy of above 98 %. 2025 IEEE. Subject The topic of the resource absorber; CNN; deep learning; microwave; sustainable Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153722-7; 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