AIMC Topic: Machine Learning

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Machine learning prediction of combat basic training injury from 3D body shape images.

PloS one
INTRODUCTION: Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic...

Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition meth...

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

Neuron
A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neu...

Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters.

Forensic science international. Genetics
We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high D...

Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort.

Hypertension (Dallas, Tex. : 1979)
Visit-to-visit blood pressure variability (BPV) has been shown to be a predictor of cardiovascular disease. We aimed to classify the BPV levels using different machine learning algorithms. Visit-to-visit blood pressure readings were extracted from th...

Biosystems Design by Machine Learning.

ACS synthetic biology
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desi...

Near-optimal insulin treatment for diabetes patients: A machine learning approach.

Artificial intelligence in medicine
Blood glycemic control is crucial for minimizing severe side effects in diabetes mellitus. Currently, two opposing treatment approaches exist: in formulaic methods, insulin care is calculated by parameter-based computation (i.e., correction factor, i...

Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance.

Sensors (Basel, Switzerland)
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independe...

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China.

Environmental monitoring and assessment
Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literatu...