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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks
Published 2025-06-01“…Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. …”
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Demographic variation in charitable giving and helping across 22 countries in the Global Flourishing Study
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Prevalence of sedative drug use and associated risk factors among medical students in Karachi, Pakistan
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NLP for computational insights into nutritional impacts on colorectal cancer care
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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction
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Genetic and Environmental Influences on Retinopathy of Prematurity
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Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data
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Prediction of dialysis adequacy using data-driven machine learning algorithms
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