AIMC Topic: Humans

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AI can see you: Machiavellianism and extraversion are reflected in eye-movements.

PloS one
INTRODUCTION: Recent studies showed an association between personality traits and individual patterns of visual behaviour in laboratory and other settings. The current study extends previous research by measuring multiple personality traits in natura...

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, ...

Learnable Brain Connectivity Structures for Identifying Neurological Disorders.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. ...

NLP-based ergonomics MSD risk root cause analysis and risk controls recommendation.

Ergonomics
An ergonomics assessment of the physical risk factors in the workplace is instrumental in predicting and preventing musculoskeletal disorders (MSDs). Using Artificial Intelligence (AI) has become increasingly popular for ergonomics assessments becaus...

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishi...

Heatstroke death identification using ATR-FTIR spectroscopy combined with a novel multi-organ machine learning approach.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
With global warming, the number of deaths due to heatstroke has drastically increased. Nevertheless, there are still difficulties with the forensic assessment of heatstroke deaths, including the absence of particular organ pathological abnormalities ...

Accelerated T2-weighted MRI of the Bowel at 3T Using a Single-shot Technique with Deep Learning-based Image Reconstruction: Impact on Image Quality and Disease Detection.

Academic radiology
RATIONALE AND OBJECTIVE: A single-shot T2-weighted deep-learning-based image reconstruction (DL-HASTE) has been recently developed allowing for shorter acquisition time than conventional half-Fourier acquisition single-shot turbo-spin echo (HASTE). T...

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Physiology (Bethesda, Md.)
Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, chal...

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Journal of computational chemistry
Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. T...

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Complex-valued convolutional neural networks (CVCNNs) have been demonstrated effectiveness in classifying complex signals and synthetic aperture radar (SAR) images. However, due to the introduction of complex-valued parameters, CVCNNs tend to become ...