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 Article Faculty Publications -Articles 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 Deepa, S.; Siddalingappa, Rashmi; Kalpana, P.; Loveline, Zeema.J.; Vinay, M.; Jayapriya; Suganthi, J.J.; Priya Stella Mary, I. Title A name given to the resource A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification 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 International Journal of Engineering Trends and Technology;Volume;73;Issue;10;pp.107-116 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/22315381/IJETT-V73I10P108" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/22315381/IJETT-V73I10P108</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022820131?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022820131?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 Deepa S., Department of Computer Science, Christ University, Bengaluru, India; Siddalingappa R., York St Jhon University, London, United Kingdom; Kalpana P., Department of Computer Science, Christ University, Bengaluru, India; Loveline Z.J., Department of Computer Science, Christ University, Bengaluru, India; Vinay M., Department of Computer Science, Christ University, Bengaluru, India; Jayapriya, Department of Computer Science, Christ University, Bengaluru, India; Suganthi J.J., Department of Computer Science, Christ University, Bengaluru, India; Priya Stella Mary I., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource The research examines how L1, L2, and L1L2 weight regularization methods affect neural network performance, generalization, and sparsity using the CIFAR 10 dataset. A Convolutional Neural Network (CNN) trained with the same environment for each regularization method to evaluate test accuracy, weight sparsity, and computational speed. The study shows that L1 regularization produces sparse weights, which makes models more interpretable, and L2 regularization helps prevent overfitting while improving model generalization. The combination of L1L2 regularization enables individual image classification methods to reach test accuracy. The results indicate that the weight regularization plays a vital role in creating neural networks that are both stable and efficient. They are interpretable, and L2 regularization improves generalization and reduces overfitting. The combined L1L2 regularization achieves the balance between sparsity and performance, leading to higher test accuracy compared to individual techniques for image classification. The research results demonstrate that weight regularization stands as an essential factor for Creating Neural Networks that are robust, efficient, and interpretable, thus helping to enhance Deep Learning model performance. 2025 Seventh Sense Research Group. Subject The topic of the resource Convolutional Neural Networks (CNN); Deep Learning; Generalization; L1 Regularization; L1L2 Regularization; L2 Regularization; Overfitting; Weight Regularization Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23490918; Language A language of the resource English Type The nature or genre of the resource Article 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