Machine learning-based heart disease diagnosis: A systematic literature review
Heart disease is one of the significant challenges in today's world and one of the leading
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
causes of many deaths worldwide. Recent advancement of machine learning (ML) …
[HTML][HTML] Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
JB Li, ZY Chen, X Li, LJ **g, YP Zhang, HH ** study
V Werner de Vargas, JA Schneider Aranda… - … and Information Systems, 2023 - Springer
Abstract Machine Learning (ML) algorithms have been increasingly replacing people in
several application domains—in which the majority suffer from data imbalance. In order to …
several application domains—in which the majority suffer from data imbalance. In order to …
Building energy performance prediction: A reliability analysis and evaluation of feature selection methods
The advancement of smart meters using evolving technologies such as the Internet of
Things (IoT) is producing more data for the training of energy prediction models. Since many …
Things (IoT) is producing more data for the training of energy prediction models. Since many …
Significance of Visible Non‐Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques
Introduction. Heart disease is emerging as the single most critical cause of death worldwide
and is one of the costliest chronic conditions. Purpose. Stimulated by the increasing heart …
and is one of the costliest chronic conditions. Purpose. Stimulated by the increasing heart …
[HTML][HTML] Significance and methodology: Preprocessing the big data for machine learning on TBM performance
HH **ao, WK Yang, J Hu, YP Zhang, LJ **g… - Underground …, 2022 - Elsevier
This paper addresses the significance of preprocessing big data collected during a tunnel
boring machine (TBM) excavation before it is used for machine learning on various TBM …
boring machine (TBM) excavation before it is used for machine learning on various TBM …
Data-centric ai for healthcare fraud detection
Automated methods for detecting fraudulent healthcare providers have the potential to save
billions of dollars in healthcare costs and improve the overall quality of patient care. This …
billions of dollars in healthcare costs and improve the overall quality of patient care. This …
Evaluating the performance of ensemble classifiers in stock returns prediction using effective features
MR Toochaei, F Moeini - Expert Systems with Applications, 2023 - Elsevier
Stock market prediction is considered as an important yet challenging aspect of financial
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …