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The proposed XGBoost model was developed for heart disease risk assessment and evaluated using accuracy, precision, recall, and F1-score, demonstrating its effectiveness and reliability for accurate heart disease prediction.
A machine learning approach to build a cybersecurity threat intelligence framework for effective multiclass intrusion detection, in which the Decision Tree classifier is used, which beats state-of-the-art deep learning and ML approaches in terms of performance, execution time, and computational complexity.
A comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS.
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