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@article{198416,
        author = {Dondeti Dinesh and CH. Venkata Kartheek and D.Divya Maheswari and D. Rishwanth Sai and Ms. S. Sarjun Beevi},
        title = {Smart Aviation Disruption Forecasting Using Hybrid Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9628-9637},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198416},
        abstract = {Flight schedule disturbances such as delays and cancellations cause inconvenience to passengers and operational challenges for airlines. This project develops a machine learning model to predict flight schedule disturbances using historical flight data and relevant operational and environmental features. The model analyzes patterns in flight operations to determine the likelihood of delay, cancellation, or on-time arrival. The system is further enhanced with a natural language processing based chatbot that enables users to interact with the platform through simple text queries. The chatbot identifies user intent and retrieves relevant flight information or prediction results accordingly. The integration of predictive analytics with a conversational interface improves accessibility and user experience. Overall, the proposed system provides accurate, timely, and intelligent flight assistance for better travel planning and decision making. This project applies and compares Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Sentiment Analysis for delay prediction. Models are evaluated on accuracy, precision, and recall to identify the most effective approach. The outcome supports airlines, passengers, and airport authorities with an intelligent decision-support system that improves efficiency, reduces losses, and enhances customer satisfaction. Flight delays have a cascading impact on the aviation ecosystem. Even a single delayed flight can lead to missed connections, overbooked gates, rescheduled crews, and dissatisfied passengers. For airlines, delays translate directly into financial losses due to increased fuel consumption, overtime pay, compensation claims, and inefficient use of aircraft. Airports also face challenges in gate allocation, air traffic management, and resource planning. Therefore, the ability to accurately predict delays is not just a matter of convenience—it is essential for maintaining operational stability and profitability.},
        keywords = {Logistic Regression, Random Forest, XGBoost, and TF IDF with Logistic Regression.},
        month = {April},
        }