AI Medical Compendium Topic:
Supervised Machine Learning

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Supervised machine learning aided behavior classification in pigeons.

Behavior research methods
Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disc...

An artificial intelligence-based risk prediction model of myocardial infarction.

BMC bioinformatics
BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI ...

MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks.

Neural networks : the official journal of the International Neural Network Society
In many machine learning applications, data are coming with multiple graphs, which is known as the multiple graph learning problem. The problem of multiple graph learning is to learn consistent representation by exploiting the complementary informati...

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

IEEE transactions on medical imaging
Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow ...

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series.

IEEE transactions on neural networks and learning systems
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This article presents a systematic and comprehensive evaluation of unsuperv...

Semisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection.

IEEE transactions on neural networks and learning systems
Abnormal behaviors in industrial systems may be early warnings on critical events that may cause severe damages to facilities and security. Thus, it is important to detect abnormal behaviors accurately and timely. However, the anomaly detection probl...

Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals.

Sensors (Basel, Switzerland)
Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on con...

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge.

Sensors (Basel, Switzerland)
Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeas...

Optical flow estimation of coronary angiography sequences based on semi-supervised learning.

Computers in biology and medicine
Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce....

Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization.

Medical image analysis
In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require accurate pixel-level annotations that are both time-consuming and laborious to generate. Typical app...