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 Rajak, Akash; Kumar, Amit; Singh, Shweta; Vidushi; Verma, Ankit; Mishra, Siddheshwari Dutt Title A name given to the resource Harnessing Behavioural Insights for Autism Spectrum Disorder Prediction via Machine Learning 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 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRITO66076.2025.11241561" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRITO66076.2025.11241561</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029842779?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029842779?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 Rajak A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Kumar A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Singh S., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Vidushi, Christ (Deemed to be University), Bengaluru, India; Verma A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Mishra S.D., Mriirs, Faridabad, India Description An account of the resource This paper exploration has been done for predicting autism spectrum disorder or ASD while using certain machine learning classifiers. The multisource dataset used, covers behavioral, genetic, neuroimaging and sensor data. The classifiers used are: Random Forest, AdaBoost, Extra Trees, Logistic Regression, K Neighbors, and Bernoulli Naive Bayes. The results achieved are: The Bernoulli Naive Bayes (92.10%), Random Forest (91.53%) and Logistic Regression (91.34%). AdaBoost and Extra Trees performed well with accuracies 90.34% and 90.20%, respectively. K Neighbors Classifier had the lowest accurate outcome with 87.65%. This study explores improving ASD diagnosis, highlighting the effectiveness of various models and emphasizing the need for further research to address challenges such as model interpretability and data quality. 2025 IEEE. Subject The topic of the resource autism spectrum disorder; machine learning; neural networks; random forest; stress management; support vector machines Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155421-7; 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