Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. Machine Learning (ML) and deep learning (DL) techniques have been increasingly employed to develop accurate workload estim...
We propose TIC-FusionNet, a trend-aware multimodal deep learning framework for time series forecasting with integrated visual signal analysis, aimed at addressing the limitations of unimodal and short-range dependency models in noisy financial enviro...
Skeleton-based action recognition has emerged as a promising field within computer vision, offering structured representations of human motion. While existing Graph Convolutional Network (GCN)-based approaches primarily rely on raw 3D joint coordinat...
The b-value in the diffusion magnetic resonance image(dMRI) reflects the degree to which the water molecules are affected by the magnetic field gradient pulse in the tissue, and the different b-values not only affect the image contrast but also the a...
The open radio access network (O-RAN) architecture facilitates intelligent radio resource management via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are integrated into RICs to address dynamic O-RAN slicing challe...
In the energy sector, accurate forecasting of natural gas production and liquid level detection is crucial for efficient resource management and operational planning. This study proposes an integrated deep learning model by incorporating bidirectiona...
Inspection and diagnosis of construction projects involves health monitoring of buildings and related facilities, and the utilization of renewable energy sources, such as solar energy, is critical to the smooth operation of modern construction projec...
This research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participat...
Journal of chemical information and modeling
Oct 8, 2025
Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules exhibiting resonance, where multiple valid Lewis st...
Breast cancer continues to be a global public health challenge. An early and precise diagnosis is crucial for improving prognosis and efficacy. While deep learning (DL) methods have shown promising advances in breast cancer classification from mammog...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.