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 A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm Description An account of the resource The time series forecasting system can be used for investments in a safe environment with minimized chances of loss. The Holt-Winters algorithm followed various procedures and observed the multiple factors applied to the neural network. The final module helps filter the system to predict the various factors and provides a rating for the system. This research work uses real-time dataset of fifteen stocks as input into the system and, based on the data, predicts or forecasts future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting will give an accurate result for the customer investments. 2022 Iyyappan. M et al. Creator An entity primarily responsible for making the resource Iyyappan M.; Ahmad S.; Jha S.; Alam A.; Yaseen M.; Abdeljaber H.A.M. Source A related resource from which the described resource is derived Scientific Programming, Vol-2022 Publisher An entity responsible for making the resource available Hindawi Limited Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1155/2022/4808088" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2022/4808088</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128330200&amp;doi=10.1155%2F2022%2F4808088&amp;partnerID=40&amp;md5=ad4e5ad0dab4324e706ee58de8712bf2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128330200&amp;doi=10.1155%2f2022%2f4808088&amp;partnerID=40&amp;md5=ad4e5ad0dab4324e706ee58de8712bf2</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 10589244; CODEN: SCIPE 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 Iyyappan M., School of Sciences, Christ (Deemed to Be University), Delhi-NCR, Ghaziabad, 201003, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Jha S., School of Sciences, Christ (Deemed to Be University), Delhi-NCR, Ghaziabad, 201003, India; Alam A., Department of Computer Science, Bakhtar University, Kabul, Afghanistan; Yaseen M., Faculty of Information Technology, University of Central Punjab, Lahore, Pakistan; Abdeljaber H.A.M., Department of Computer Science, Faculty of Information Technology, Applied Science Private University, Amman, Jordan