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 Thiruthuvanathan, Michael Moses; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep; Murthy, Hari; Pillai, Vinay Jha Title A name given to the resource Forecasting Flight Delays with a Multilayered Memory Fusion Network 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 Procedia Computer Science;Volume;258;pp.1302-1315 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2025.04.364" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.04.364</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007155455?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007155455?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 Thiruthuvanathan M.M., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India, School of Engineering and Technology, M.S. Ramaiah University of Applied Sciences, Karnataka, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Murthy H., Department of Electronics and Communication Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Pillai V.J., Department of Electronics and Communication Engineering, India, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India Description An account of the resource One of the biggest worldwide sectors is aviation, hence delays in flight services not only perturb customers but also result in large losses for airlines. Forecasting these delays is still difficult because of the erratic character of elements like climate. Accurate projections are challenging even using accepted analytical methods. This work employs sophisticated deep learning methods to enhance the forecast of aircraft delays - more especially, those resulting from weather-related causes.We investigate their effect on aircraft delays using datasets from both the United States and India, including meteorological fluctuations. Built on a Multilayered Memory Fusion Network, the model captures intricate temporal patterns in the data by merging Bidirectional LSTM (Bi-LSTM) and Long Short-Term Memory (LSTM). This network generates more accurate forecasts and is meant to effectively manage several factors. For the United States dataset, the proposed network attained a Mean Absolute Error (MAE) score of 72.41 and Root Mean Square Error (RMSE) scores of 118.87 and 11.83 and 21.82 for India respectively. Our deep learning methodology clearly predicts flight delays as these performance measures are far better than those attained by conventional machine learning techniques, including linear regression. By using these cutting-edge algorithms, the research provides a more accurate way to forecast flight delays, hence perhaps lowering passenger discontent and airline financial losses. 2025 The Author(s). Subject The topic of the resource Bi-LSTM; Deep learning; Flight delay prediction; LSTM; Multi-layered Memory Fusion Network Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online