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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Intelligence-Based Approaches for Anticipating Financial Market Index Trends Description An account of the resource The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. Creator An entity primarily responsible for making the resource Jose E.; Kappil S.R.; Cheriyan A. Source A related resource from which the described resource is derived Artificial Intelligence in Forecasting: Tools and Techniques, pp. 173-189. Publisher An entity responsible for making the resource available CRC Press 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/10.1201/9781003399292-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-12</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195975359&amp;doi=10.1201%2F9781003399292-12&amp;partnerID=40&amp;md5=b3759b0f0366a6140017423a6cd50205" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195975359&amp;doi=10.1201%2f9781003399292-12&amp;partnerID=40&amp;md5=b3759b0f0366a6140017423a6cd50205</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104005150-4; 978-103250615-9 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jose E., Department of Data Science, CHRIST University, India; Kappil S.R., Department of Data Science, CHRIST University, India; Cheriyan A., School of Business and Management, CHRIST University, India