Artificial Intelligence Medical Compendium

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

Showing 49,231 to 49,240 of 224,513 articles

Adaptive Autocorrelation Based Heart Rate Estimation from Single-Axis Seismocardiogram: A Comprehensive Benchmark Across Six Diverse Datasets.

IEEE journal of biomedical and health informatics Feb 18, 2026
We introduce AACFD (Adaptive Autocorrelation Function Detector), a lightweight, fully automatic pipeline for estimating window-averaged heart rate (HR) from a single-axis seismocardiogram (SCG) without ECG calibration or machine learning. AACFD combi... read more 

High-Fidelity Seismic Super-Resolution Using Prior-Informed Deep Learning with 3D Awareness.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Feb 18, 2026
The limitations of seismic vertical resolution pose significant challenges for the identification of thin beds. Improving the vertical resolution of seismic data using deep learning methods often encounters challenges related to unrealistic outputs a... read more 

HoloQA: Full Reference Video Quality Assessor of Rendered Human Avatars in Virtual Reality.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Feb 18, 2026
We present HoloQA, a new state-of-the-art Full Reference Video Quality Assessment (VQA) model that was designed using principles of visual neuroscience, information theory, and self-supervised deep learning to accurately predict the quality of render... read more 

Max-Min Robust Unsupervised Feature Selection via Sparse Subspace.

IEEE transactions on cybernetics Feb 18, 2026
Feature selection is one of the hot issues in machine learning. It reduces storage pressure by effectively screening features and has become a very practical data preprocessing method. At present, most feature selection algorithms apply $\ell _{2,1}$... read more 

DSMDTN: A Data-Selective Multiscale Dual Transfer Network for Fault Diagnosis of Key Components in Rotating Machinery.

IEEE transactions on cybernetics Feb 18, 2026
Rotating machinery often operates under varying working conditions, which poses significant challenges to achieving reliable bearing fault diagnosis using traditional deep learning-based models. To enhance the diagnostic performance for rolling beari... read more 

Model-Predictive Control for Constrained Wastewater Treatment Processes With Stochastic Sampling Intervals.

IEEE transactions on cybernetics Feb 18, 2026
The existence of stochastic sampling phenomena in wastewater treatment processes (WWTPs) breaks the assumption that the existing control strategies use periodic data, and the operational constraints of equipment and the requirements for effluent wate... read more 

Decentralized Constrained Optimization Over Time-Varying Directed Networks via Subgradient Rescaling.

IEEE transactions on cybernetics Feb 18, 2026
In this article, we investigate a decentralized constrained optimization problem over time-varying directed networks. The nodes in the network aim to collaboratively minimize the aggregate of all locally known convex cost functions, subject to local ... read more 

Improved Stability Criteria for Delayed Neural Networks: Further Utilization of Information on Time-Varying Delays and Activation Functions.

IEEE transactions on cybernetics Feb 18, 2026
This article focuses on the low-conservative stability criteria of delayed neural networks (DNNs). To achieve this goal, new techniques are developed to effectively utilize more system-related information. To use the time-varying delay information, s... read more 

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization.

IEEE transactions on visualization and computer graphics Feb 18, 2026
Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present ... read more 

Unsupervised Deep Spectral Basis Learning for Generalized Eigendecomposition and Spectral Embedding.

IEEE transactions on neural networks and learning systems Feb 18, 2026
Spectral embedding has been widely used in statistical learning and geometric processing. Existing deep neural networks (DNNs) construct nonlinear mappings from node descriptors to embedding coordinates, relieving the scalability and generalization p... read more 
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