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 Hasan, Mohammed Faez; Sherin, K.; Champaneria, Tushar; Sidhu, Kawerinder Singh; Raja Mannar, B.; Acharjee, Purnendu Bikash Title A name given to the resource Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques 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 Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.740-745 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439293" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439293</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037436764?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037436764?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 Hasan M.F., Kerbala University, Finance and Banking Department, Iraq; Sherin K., St.Joseph's Institute of Technology, Dept of CSE, India; Champaneria T., Computer Engineering, L. D. College of Engineering, Ahmedabad, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Raja Mannar B., Graham International University, (Online), Viet Nam; Acharjee P.B., Computer Science, CHRIST University, Bengaluru, India Description An account of the resource This study proposed a hybrid stock market forecasting model which consists of Artificial Intelligence (AI) and metaheuristic feature selection algorithms to improve the accuracy in prediction and efficiency of the prototypical. It uses PSO (Particle Swarm Optimization) algorithm to pick the most relevant feature out of a pool of technical indicators and sentiment data and temporally learns the pattern using the LSTM (Long Short-Term Memory) network. The model yields better learning by diminishing noise and dimensionality and prevents over fitting. The efficiency of the anticipated system is seen through comparative analysis with such baseline models as SVM (Support Vector Machine), RF (Random Forest), and standard LSTM. This prototypical obtained MAE of 11.2RMSE of 18.18, and the mean absolute percentage error (MAPE) of 5.36 percent, with R2 of 0.91 and directional accuracy of 86.4 percent. The above results confirm the effectiveness of the suggested method, providing a solid and generalizable solution in terms of intelligent stock market prediction and investment decision support. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Feature Selection; Financial Prediction; Metaheuristic Algorithms; Stock Market Forecasting; Time Series Examination Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; 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