[HTML][HTML] Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

U Saeed, SY Shah, J Ahmad, MA Imran… - Journal of …, 2022 - Elsevier
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …

Mortality prediction using data mining classification techniques in patients with hemorrhagic stroke

E Utami, S Raharjo - … international conference on cyber and IT …, 2020 - ieeexplore.ieee.org
Stroke is a major health problem in Indonesia and the world, and the cause of disability and
death. Hemorrhagic stroke has a higher mortality rate compared to ischemic stroke. The …

[HTML][HTML] Whale Optimization Algorithm-Enhanced Long Short-Term Memory Classifier with Novel Wrapped Feature Selection for Intrusion Detection

H AL-Husseini, MM Hosseini, A Yousofi… - Journal of Sensor and …, 2024 - mdpi.com
Intrusion detection in network systems is a critical challenge due to the ever-increasing
volume and complexity of cyber-attacks. Traditional methods often struggle with high …

Predicting students academic performance using a hybrid of machine learning algorithms

R Ayienda, R Rimiru, W Cheruiyot - 2021 IEEE AFRICON, 2021 - ieeexplore.ieee.org
Educational data mining (EDM) has become a very interesting field of study in machine
learning (ML), since it has enabled searchers to mine knowledge from educational …

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth

J Peng, J Chen, C Yin, P Zhang, J Yang - medRxiv, 2025 - medrxiv.org
Youth who experience concussions may be at greater risk for subsequent mental health
challenges, making early detection crucial for timely intervention. This study utilized …

A Novel Approach for Specification Testing on Heart Disease Detection Using Feed-Forward Neural Network

E Naresh, SLS Darshan, NN Srinidhi… - SN Computer …, 2023 - Springer
The proposed work highlights the importance of testing in machine-learning applications
and the ensuing need to increase model quality to decrease the likelihood of errors. The …

[PDF][PDF] Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets.

I Moatadid, I Abnane, A Idri - KDIR, 2023 - scitepress.org
Heart disease remains a significant global health challenge, necessitating accurate and
reliable classification techniques for early detection and diagnosis. Choosing a suitable …

Models for Detecting Frauds in Medical Insurance

H Mitrova, A Madevska Bogdanova - International Conference on ICT …, 2021 - Springer
Health insurance is important for many people, but unfortunately it is susceptible to frauds,
therefore expenditures for covering the funds show exponential growth. The victims of this …

Zeki sınıflandırma ve kümeleme yöntemlerinin tıbbi tanı ve tedavide kullanımı= The usage of intelligent classification and clustering methods in medical diagnosis and …

UE Kocamaz - 2024 - acikerisim.sakarya.edu.tr
Bu doktora tezinde, hastalıkların teşhis ve tedavisinde doktorların kararlarına destek olmak
amacı ile makine öğrenmesi içeren sınıflandırma ve kümeleme algoritmaları kullanılmıştır …

[PDF][PDF] Machine Learning Empowered COVID-19 Patients Monitoring Using Non-Contact Sensing: An Extensive

U Saeeda, SY Shahb, J Ahmadc, MA Imrand… - researchgate.net
COVID-19 has affected more than 190 million people worldwide and with the recent rise of a
new variant, the efficacy of the vaccine is an ultimate question. The goal is to limit the spread …