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 Paul, P. Mano; Praveen, R.V.S.; Vemuri, Harikrishna; John, Tegil J; Manoj Senthil, K.; Muralidharan, A. Title A name given to the resource A Hybrid Deep Learning and Ensemble Framework for Real-Time Cyclone Path and Intensity Prediction in Disaster-Prone Regions 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 Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.1825-1830 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOSEC67334.2025.11459493" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOSEC67334.2025.11459493</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037105426?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037105426?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 Paul P.M., Dayananda Sagar Academy Of Technology And Management, Department Of CSE-Artificial Intelligence, Karnataka, India; Praveen R.V.S., LTIMindtree, Department Of Utilities (Director, Utilities America), Houston, TX, United States; Vemuri H., Deloitte Consulting Llp, Department Of Enterprise Performance, Houston, TX, United States; John T.J., Christ University, Department Of Computer Science, Karnataka, Benguluru, India; Manoj Senthil K., Kongu Engineering College, Department Of Electronics And Communication Engineering, Erode, India; Muralidharan A., Hindusthan Institute Of Technology, Department Of Computer Science And Engineering, Tamil Nadu, Coimbatore, India Description An account of the resource Predicting the path and strength of cyclones involves significant issues in meteorology, as mistakes can greatly affect disaster management and evacuation strategies. Current models frequently encounter difficulties in achieving accurate real-time forecasting, particularly in representing complicated spatial-temporal dynamics of cyclones. The proposed study presents an innovative hybrid architecture combining deep learning and ensemble methods, using convolutional layers, LSTM units, and a gradient boosting meta-learner to improve prediction efficiency. The system was trained and verified utilising multi-year cyclone datasets obtained from Kaggle, which included atmospheric and oceanic factors. The model architecture attained exceptional accuracy, with a track error of 28 km, a mean absolute error (MAE) of 3.2 hPa for pressure, 4.5 km/h for wind speed, and a root mean square error (RMSE) of 35.4 km. The suggested approach consistently outperformed baseline models, including ConvLSTM, GRU, and XGBoost, across all critical criteria. The deployment in real-time was enabled by a containerised, low-latency API that can integrate with disaster early warning systems. This research enhances cyclone forecasting by offering a scalable, precise, and operationally feasible solution for disaster-prone areas, demonstrating practical superiority over current methodologies. The results highlight the capability of hybrid AI models to improve the accuracy and dependability of meteorological forecasts. 2025 IEEE. Subject The topic of the resource Atmospheric Data Analysis; Convolutional Neural Networks; Cyclone Prediction; Deep Learning; Disaster Management; Ensemble Learning; Gradient Boosting; Hybrid Model; LSTM; Meteorology AI; Real-Time Forecasting; RMSE Cyclone Path; Spatial-Temporal Modelling; Track Error Reduction; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159859-4; 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