Computational health informatics in the big data age: a survey
The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …
of research activity in the healthcare and data sciences fields. The conventional approaches …
On the uses of PCA to characterise molecular dynamics simulations of biological macromolecules: basics and tips for an effective use
J Palma, G Pierdominici‐Sottile - ChemPhysChem, 2023 - Wiley Online Library
Abstract Principal Component Analysis (PCA) is a procedure widely used to examine data
collected from molecular dynamics simulations of biological macromolecules. It allows for …
collected from molecular dynamics simulations of biological macromolecules. It allows for …
Clinical big data and deep learning: Applications, challenges, and future outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …
based on machine learning. In recent years, as a powerful technique for big data, deep …
Study of machine learning algorithms for special disease prediction using principal of component analysis
BD Kanchan, MM Kishor - … on global trends in signal processing …, 2016 - ieeexplore.ieee.org
The worldwide study on causes of death due to heart disease/syndrome has been observed
that it is the major cause of death. If recent trends are allowed to continue, 23.6 million …
that it is the major cause of death. If recent trends are allowed to continue, 23.6 million …
Landslide susceptibility modeling: an integrated novel method based on machine learning feature transformation
Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has
witnessed considerable improvement following advances in machine learning (ML) …
witnessed considerable improvement following advances in machine learning (ML) …
Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography
A Depeursinge, D Racoceanu, J Iavindrasana… - Artificial intelligence in …, 2010 - Elsevier
OBJECTIVE: We investigate the influence of the clinical context of high-resolution computed
tomography (HRCT) images of the chest on tissue classification. METHODS AND …
tomography (HRCT) images of the chest on tissue classification. METHODS AND …
Statistical machine learning approaches to liver disease prediction
Medical diagnoses have important implications for improving patient care, research, and
policy. For a medical diagnosis, health professionals use different kinds of pathological …
policy. For a medical diagnosis, health professionals use different kinds of pathological …
Anomaly-based detection of cyberattacks on line current differential relays
Currently, the architecture of Line Current Differential Relays (LCDRs) is designed to
respond to internal faults on the protected line using local and remotely-communicated …
respond to internal faults on the protected line using local and remotely-communicated …
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
Abstract Background and Objective Non-invasively predicting the risk of cancer metastasis
before surgery can play an essential role in determining which patients can benefit from …
before surgery can play an essential role in determining which patients can benefit from …
Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models
Condition monitoring and classification of machinery state is of great practical significance in
manufacturing industry, because it provides updated information regarding machine status …
manufacturing industry, because it provides updated information regarding machine status …