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 Johny, Akshay; Senthil Vadivu, M.; Jeevanand, E.S. Title A name given to the resource Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning 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 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.1049-1054 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11330264" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11330264</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034854796?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034854796?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 Johny A., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Senthil Vadivu M., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Jeevanand E.S., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India Description An account of the resource Train delays are one of the most persistent issue faced by Indian Railways and it has still been the issue with all the modern infrastructure and increasing travel demands. Traditional methods mainly depend on historical averages and simple modelling which fail to capture complex patterns in delays caused. This research aims to build machine learning and neural network models to analyse historical data from past train journeys and make predictions for future train journeys. Machine learning models include Decision Trees, XGBoost, Random Forest, Extra Trees and a neural network model LSTM to predict the delay for a particular train on a given day based on the previous running status. The highest accuracy of 94.02% was found using LSTM model and a lowest of 72.65% for Decision Tree Regressor algorithm. 2025 IEEE. Subject The topic of the resource Decision Trees; Machine Learning; RNN; Train Delay; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; 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