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 Precision Corn Price Prediction with Advanced ML Techniques Subject The topic of the resource Corn Market; Economic Indicators; Forecasting Accuracy; Machine Learning; Price Prediction Description An account of the resource In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. Creator An entity primarily responsible for making the resource Gaur S.; Mahajan J.; Sharma M.; Hussain D.; Kakani R.; Saxena A. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/TQCEBT59414.2024.10545288" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545288</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204492991&amp;doi=10.1109%2FTQCEBT59414.2024.10545288&amp;partnerID=40&amp;md5=943c6dfb6ae607e4729de70ef0576443" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204492991&amp;doi=10.1109%2fTQCEBT59414.2024.10545288&amp;partnerID=40&amp;md5=943c6dfb6ae607e4729de70ef0576443</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Gaur S., Christ University, India; Mahajan J., Christ University, Bengaluru, India; Sharma M., Christ University, Bengaluru, India; Hussain D., Christ University, Bengaluru, India; Kakani R., Sg Analytics, Bengaluru, India; Saxena A., Christ University, Bengaluru, India