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@article{203146,
        author = {Arun Kumar and Muguntha and Mohammed irfan and Moheshwaran},
        title = {EEG-BASED EPILEPTIC SEIZURE PREDICTION USING ENSEMBLE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12192-12197},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203146},
        abstract = {Epilepsy is a neurological disorder that is described by recurrent seizures due to unusual electrical discharges in the brain. This unpredictable nature of seizures puts the patient in a position of great risk or injury. This project aims at designing a smart system for predicting seizures based on Electroencephalogram (EEG) signals using machine learning and Internet of Things (IoT) technology. In this project, the proposed system will be able to process the EEG signals by applying different techniques for feature extraction. The features extracted from the signals will be used for predicting seizures by applying different machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting on the extracted features. The prediction will be done by using ensemble learning for improving the accuracy of the proposed system. The proposed system will be implemented using IoT technology by integrating ESP32 and a Flask server. The proposed system will be able to send alerts to the patient before the seizure occurs.},
        keywords = {Epilepsy, Electroencephalogram (EEG), Seizure Prediction, Machine Learning, Ensemble Learning, Internet of Things (IoT), Real-Time Monitoring.},
        month = {May},
        }