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 Athul Ghosh, P.; Chidambaram, S. Title A name given to the resource Hyperspectral Image Classification using Archimedes Optimized 3D CNN layers 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 Proceedings of 5th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2025;pp.151-157 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICUIS67429.2025.11380606" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICUIS67429.2025.11380606</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034832266?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034832266?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 Athul Ghosh P., Christ University, School of Engineering and Technology, Department of Ece, Bangalore, India; Chidambaram S., Christ University, School of Engineering and Technology, Department of Ece, Bangalore, India Description An account of the resource Background: Hyperspectral Image (HSI) classification involves analyzing images captured across numerous spectral bands to identify and categorize materials or objects. By exploiting spectral information, HSI enables precise detection of materials based on their unique spectral signatures. This extensive spectral information allows for accurate identification of materials and land cover types, making HSI classification essential for various applications. This research developed an optimization-based Deep Learning (DL) framework for effective classification of HSI. The methodology begins with a preprocessing step using Minimum Noise Fraction (MNF), a technique that leverages the noise covariance structure of the dataset to reduce dimensionality while preserving the most informative spectral features. To enhance classification performance, a novel model is proposed, known as Archimedes Optimization Algorithm-3D Convolutional Neural Network (AOA based 3D CNN). Archimedes Optimization Algorithm (AOA) is applied to fine-tune learning rate of 3D Convolutional Neural Network (3D CNN), thereby optimizing training convergence and enhancing accuracy. Result: The proposed AOA based 3D CNN approach exhibits outstanding performance, achieving 95.6% accuracy, 95.9% TPR, 94.1% TNR, 94.9% PPV, a Kappa coefficient of 95.4%, and an F1-score of 95.5%.Keywords: These results highlight the model's effectiveness, adaptability and robustness for HSI classification. 2025 IEEE. Subject The topic of the resource deep learning; HSI classification; Hyperspectral images; MNF; optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155284-8; 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