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 Balaji, Lavanya; Anita, H.B.; Kumar, Balaji Ashok Title A name given to the resource A Comparative Analysis of Various Soft Computing Techniques for Indian Stock Market Prediction 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;3;pp.95-103 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/22315381/IJETT-V73I3P107" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/22315381/IJETT-V73I3P107</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001300504?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001300504?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 Balaji L., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Kumar B.A., Department of Commerce, Government First Grade College Vemagal, Karnataka, India Description An account of the resource Soft computing techniques have been increasingly used for stock market analysis in the past few years because they can capture nonlinear aspects which traditional econometric models do not adequately capture. With different techniques like Artificial Neural Networks, Deep Neural Networks and Stacked Autoencoders available, in this paper, the author tries to determine which of the above methods can model the Indian stock market with higher accuracy. In this study, high-frequency data from Nifty 50 is used, and various feature selection techniques such as PCA and linear regression are used for each of the above machine learning models to predict the Nifty 50 data. Finally, all predictions from the different techniques are compared with the actual index movement and the best method for Nifty 50 is suggested. 2025 Seventh Sense Research Group Subject The topic of the resource Artificial Neural Network; Deep Neural Network; Nifty 50; Principal Component Analysis (PCA); Stock market 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