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 AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages Subject The topic of the resource Artificial Intelligence; Autism Spectrum Disorder; Feature Extraction; Functional MRI; MRI; Structural MRI Description An account of the resource High-dimensional data, lower detection accuracy, susceptibility to manual errors, and the requirement of clinical experts are some drawbacks of conventional classification models available for Autism Spectrum Disorder (ASD) detection. To address these challenges and explore the affiliated information from advanced imaging modalities such as Magnetic Resonance Imaging (MRI) in structural MRI (sMRI) and resting state-functional MRI (rs-fMRI), the study applied an Artificial Intelligence (AI) approach. In this context, AI is used to automate the feature extraction process, which is crucial in the interpretation of medical images for diagnosis. The work aims to apply AI-based techniques to extract the features and identify the impact of each feature in the Autism diagnosis. The morphometric features were extracted using sMRI images and rs-fMRI scans were employed to fetch functional connectivity features. Surface-based, region-based, and seed-based analyses are performed for the whole brain, followed by feature selection techniques such as Recursive Feature Elimination (RFE) with correlation, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and graph theory are implemented to extract and distinguish features. The effectiveness of the extracted features was measured as classification accuracy. Support Vector Machine (SVM) with RFE is the best classification model, with 88.67% accuracy for high-dimensional data. SVM is a supervised learning model that outperforms other classification models due to its capability to handle high-dimensional data with a larger feature set. Medical imaging modalities provide detailed insights and visual differences related to various cognitive conditions that must be recognized accurately for efficient diagnosis. The study presented an empirical analysis of various Feature extraction approaches and the significance of the extracted features in high-dimensional data scenarios for Autism classification. 2024 Meenakshi Malviya Chandra J and Nagendra N. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Creator An entity primarily responsible for making the resource Malviya M.; Chandra J.; Nagendra N. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-20, No. 11, pp. 1495-1503. Publisher An entity responsible for making the resource available Science Publications 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/10.3844/jcssp.2024.1495.1503" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/jcssp.2024.1495.1503</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205241551&amp;doi=10.3844%2Fjcssp.2024.1495.1503&amp;partnerID=40&amp;md5=1ca6595019a37f260bae244ce847e7d8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205241551&amp;doi=10.3844%2fjcssp.2024.1495.1503&amp;partnerID=40&amp;md5=1ca6595019a37f260bae244ce847e7d8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15493636 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 Malviya M., Department of Computer Science Christ, Deemed to be University, Bangalore, India; Chandra J., Department of Computer Science Christ, Deemed to be University, Bangalore, India; Nagendra N., Department of Computer Science Christ, Deemed to be University, Bangalore, India