AIMC Topic: Machine Learning

Clear Filters Showing 23721 to 23730 of 34417 articles

Tractography and machine learning: Current state and open challenges.

Magnetic resonance imaging
Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-model-free, and easily incorporate anatomical...

Classifying intracranial stenosis disease severity from functional MRI data using machine learning.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has been hampered by well-known artifacts associated with delayed blood arrival times and reduced microvascular compliance. Using machine learning and suppor...

Cognitive Action Laws: The Case of Visual Features.

IEEE transactions on neural networks and learning systems
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on...

Evolving neural networks to follow trajectories of arbitrary complexity.

Neural networks : the official journal of the International Neural Network Society
Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic bio...

Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions.

Environment international
BACKGROUND: Accurate estimation of nitrogen dioxide (NO) and nitrogen oxide (NO) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acut...

A cross-lingual approach to automatic ICD-10 coding of death certificates by exploring machine translation.

Journal of biomedical informatics
Automatic ICD-10 coding is an unresolved challenge in terms of Machine Learning tasks. Despite hospitals generating an enormous amount of clinical documents, data is considerably sparse, associated with a very skewed and unbalanced code distribution,...

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomp...

Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm.

Human genetics
The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of the pathogenicity of genes and the total number of Mendelian disease genes are both important questi...

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

Journal of medical Internet research
BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motiva...

Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study.

BMC medical informatics and decision making
OBJECTIVE: Assessing risks of bias in randomized controlled trials (RCTs) is an important but laborious task when conducting systematic reviews. RobotReviewer (RR), an open-source machine learning (ML) system, semi-automates bias assessments. We cond...