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 Kumar Chandar, S.; Punjabi, Hitesh; Malik, Mayur Title A name given to the resource Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.1602-1612 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAUC68182.2026.11441097" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAUC68182.2026.11441097</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037436794?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037436794?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 Kumar Chandar S., Christ University, School of Business and Management, India; Punjabi H., K J Somaiya Institute of Management, India; Malik M., International Open University, India Description An account of the resource In the domains of economic management and energy analysis, forecasting the price of crude oil is increasing popularity. It is essential to the facilitating rapid and cost-effective development with improved quality. Accurate prediction of the crude oil market is essential for steady and fast economic development because of its enormous influence on the global economy and society. Moreover, precise crude oil price prediction aids the traders in making accurate decision to maximize profits. In this work, a machine learning method for forecasting future global price data for crude oil is provided based on past data. The proposed model consists of three phases: primarily, historical data of selected crude oil data are gathered and normalized using data normalization technique. Secondly, technical indicators are derived from the crude oil data. Finally, a Feed Forward Neural Network (FFNN) is designed and trained using these technical indicators to forecast the price of crude oil in the future. Daily, weekly, and monthly data from Brent crude oil and West Texas Intermediate (WTI) are used to evaluate the generated model's prediction ability. To find the most effective FFNN configuration, the model's efficacy is evaluated by adjusting hidden layer number and hidden neurons. Performance of the model is also analyzed by varying number of training and testing samples. The experimental outcomes demonstrates that the designed model exhibits excellent performance for both WTI and Brent data. Notably, the model proves to be effective in predicting crude oil prices, when technical indicators are used as input variables. 2026 IEEE. Subject The topic of the resource and Technical Indicators; Crude Oil Price Prediction; Feed Forward Neural Network; Machine Learning; Prediction Accuracy Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155851-2; 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