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 Conference Papers 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/. Title A name given to the resource Evaluation of machine and deep learning models for utility mining-based stock market price predictions Subject The topic of the resource Deep Learning; LSTM; Neural Networks; Non-Linearity; RNNM; Stock Market Prediction Description An account of the resource Considering the extreme volatility of stock market returns and hazards, accurate price prediction has attracted the attention of both financial institutions and regulatory bodies. Stocks, due to their historically strong returns, have long been considered by investors to be an excellent asset allocation strategy. Predicting stock prices has never ceased being a hot topic of study. Many early-day economists sought to foretell future stock values. In subsequent years, as computer technology has advanced rapidly and mathematical theory has been extensively studied, it has been shown that mathematical models, like the time series model, may be very effective in predicting due to their simplicity and superiority. Over time, the time series model is put into practice. Over time, the horizon widened. Support vector machines and other ML techniques have challenges when applied to stock data because of its non-linearity. In subsequent years, thanks to advancements in deep learning, models like RNN and LSTM Neural Networks were able to analyze non-linear input, remember the sequence, and remember valuable information,Stock data forecasting cannot be done without it. 2024 Author(s). Creator An entity primarily responsible for making the resource Rajeshwari G.M.; Manasa N.; Rajimol K.P.; Naidu K.; Samal A.; Garg N. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-3214, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics 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/10.1063/5.0239965" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0239965</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210417925&amp;doi=10.1063%2F5.0239965&amp;partnerID=40&amp;md5=eb3f7aad795f2c42f826bea2451b579d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210417925&amp;doi=10.1063%2f5.0239965&amp;partnerID=40&amp;md5=eb3f7aad795f2c42f826bea2451b579d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajeshwari G.M., Department of Studies in Business Administration, Pooja Bhagavat Memorial Mahajana Education Centre, Mysuru, India; Manasa N., Department of Business and Management, CHRIST (Deemed to Be University) Yeshwantpur Campus, Bangalore, India; Rajimol K.P., Department of MBA, Atria Institute of Technology, Karnataka, Bangalore, India; Naidu K., Department of Management Technology, Shri Ramdeobaba College of Engineering and Management, Nagpur, India; Samal A., Dept of MBA, Sri Venkateshwara College of Engineering, Karnataka, Bangalore, India; Garg N., Department of Studies in Business Administration, RIMS, Maharashtra, India