Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance

Alanazi, Munid; Alsharif, Bader; Altaher, Ali Salem; Altaher, Ahmed; Ilyas, Mohammad

doi:10.1109/honet59747.2023.10374662

2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET) 2023

DOI: 10.1109/honet59747.2023.10374662

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Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance

Munid Alanazi,

Bader Alsharif,

Ali Salem Altaher

et al.

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2024

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Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things

Alsolami,

Alsharif,

Ilyas

2024

Sensors

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This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

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Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things

Alsolami,

Alsharif,

Ilyas

2024

Sensors

View full text Add to dashboard Cite

show abstract

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Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance

Cited by 1 publication

References 25 publications

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things

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