Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 56,351 to 56,360 of 226,846 articles

Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.

IEEE transactions on bio-medical engineering Jan 30, 2026
OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) using electroencephalography (EEG). However,... read more 

Padé Neurons for Efficient Neural Models.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Jan 30, 2026
Neural networks commonly employ the McCulloch-Pitts neuron model, which is a linear model followed by a point-wise non-linear activation. Various researchers have already advanced inherently non-linear neuron models, such as quadratic neurons, genera... read more 

Representation Learning for Tabular Data: A Comprehensive Survey.

IEEE transactions on pattern analysis and machine intelligence Jan 30, 2026
Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabular data have continuously evolved, with Deep Neural Networks (DNNs) rec... read more 

A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data.

PloS one Jan 30, 2026
Advancements in deep learning technologies and an increase in medical data have enhanced the accuracy of disease diagnosis and treatment strategies. Notably, significant progress has been made in the use of deep learning-based time-series prediction ... read more 

Graph neural networks and belief rule base collaborative modeling for automated and interpretable fault diagnosis in proton exchange membrane fuel cells.

PloS one Jan 30, 2026
Proton exchange membrane fuel cells (PEMFC) are critical for clean energy conversion, but their reliability is severely compromised by complex faults, creating a pressing need for accurate and interpretable diagnostic methods. While the Belief Rule B... read more 

Theory-Driven Experimental Discovery of M-N-C Electrocatalysts.

Accounts of chemical research Jan 30, 2026
ConspectusAtomically dispersed M-N-C catalysts, owing to their high metal utilization and well-defined local structure, have been extensively applied in oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction... read more 

Fast identification of the charging pile plug materials using laser-induced breakdown spectroscopy.

PloS one Jan 30, 2026
The electric vehicles (EVs) is showing rapid growing, with charging piles playing a critical role as essential infrastructure. The performance and reliability of charging plugs directly influence grid efficiency, while conventional copper-based mater... read more 

The autonomy paradox in AI-generated content adoption: Creative-specific alternative to TAM model in China's micro-short drama industry.

PloS one Jan 30, 2026
In China's booming micro-short drama industry, Artificial Intelligence Generated Content (AIGC) presents creators with an 'autonomy paradox': improving efficiency while sparking fears of lost control, amplified by collectivist culture that heightens ... read more 

Developing a machine learning model to map new-build gentrification: A mixed-methods approach.

PloS one Jan 30, 2026
New-build gentrification, a type of gentrification which is connected to newly built development, has radically transformed the appearance of neighborhoods across the United States. However, the literature is lacking discussion on the built component... read more 

Reinforcement Operator Learning (ROL): A hybrid DeepONet-guided reinforcement learning framework for stabilizing the Kuramoto-Sivashinsky equation.

PloS one Jan 30, 2026
This study presents Reinforcement Operator Learning (ROL)-a hybrid control paradigm that marries Deep Operator Networks (DeepONet) for offline acquisition of a generalized control law with a Twin-Delayed Deep Deterministic Policy Gradient (TD3) resid... read more 
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