AIMC Topic: Machine Learning

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Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves.

Sensors (Basel, Switzerland)
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a...

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair.

Sensors (Basel, Switzerland)
Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are n...

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation.

Oxidative medicine and cellular longevity
BACKGROUND: Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant c...

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network.

Computational intelligence and neuroscience
Due to the marked increase in the prevalence of overweight and obesity worldwide and an environment leading to a series of chronic diseases, physical exercise is an important way to prevent chronic diseases. Additionally, a good exercise smart bracel...

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.

PloS one
OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease...

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data.

PLoS computational biology
Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have...

Improving Speech Emotion Recognition With Adversarial Data Augmentation Network.

IEEE transactions on neural networks and learning systems
When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation network-namely adversarial data augmentation network...

Neural Network Potentials: A Concise Overview of Methods.

Annual review of physical chemistry
In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine...

A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint.

Journal of neuroscience methods
BACKGROUND: Predicting the evolution of the brain network, also called connectome, by foreseeing changes in the connectivity weights linking pairs of anatomical regions makes it possible to spot connectivity-related neurological disorders in earlier ...

CODER: Knowledge-infused cross-lingual medical term embedding for term normalization.

Journal of biomedical informatics
OBJECTIVE: This paper aims to propose knowledge-aware embedding, a critical tool for medical term normalization.