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 Articles 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/. Title A name given to the resource Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition Subject The topic of the resource artificial butterfly optimization; convolutional neural networks; human activity recognition; one-shot learning; polarized full attention mechanism Description An account of the resource Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). All Rights Reserved. Creator An entity primarily responsible for making the resource Bommagani N.J.; Venkataramana A.; Vemulapalli R.; Singasani T.R.; Pani A.K.; Challageri M.B.; Kayam S. Source A related resource from which the described resource is derived Mathematical Modelling of Engineering Problems, Vol-11, No. 3, pp. 631-640. Publisher An entity responsible for making the resource available International Information and Engineering Technology Association Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18280/mmep.110306" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/mmep.110306</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189687280&amp;doi=10.18280%2Fmmep.110306&amp;partnerID=40&amp;md5=473197928ef96fa187c5f969e44060ec" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189687280&amp;doi=10.18280%2fmmep.110306&amp;partnerID=40&amp;md5=473197928ef96fa187c5f969e44060ec</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 23690739 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bommagani N.J., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India; Venkataramana A., Department of Computer Applications, New Horizon College of Engineering, Ring Road, Bellandur Post, Bengaluru, 560103, India; Vemulapalli R., Department of Information Technology, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, 520007, India; Singasani T.R., School of Computer and Information Sciences, University of Cumberlands, Louisville, 40769, KY, United States; Pani A.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Challageri M.B., Department of Mechanical Engineering, New Horizon College of Engineering, Ring Road, Bellandur Post, Bengaluru, 560103, India; Kayam S., Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, India