A spatiotemporal deep learning framework for detecting unusual human activity in surveillance videos. [PDF]
Kumar M +5 more
europepmc +1 more source
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Pixel shift-based reversible steganography for privacy-preserving medical images. [PDF]
Huang X, Zhang B, Zhu X.
europepmc +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models. [PDF]
Chen L, Jin Z, Shao Y, Zhou B.
europepmc +1 more source
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images
A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski +5 more
wiley +1 more source
Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists. [PDF]
Thompson B +4 more
europepmc +1 more source
How much material loss is concealed beneath a layer of corrosion products? This proof‐of‐concept study demonstrates how concealed volume loss is inferred from the optical appearance and as‐exposed surface topography of AZ31 corrosion imprints. By predicting postcleaning depth maps, a multimodal U‐Net model estimates hidden volume loss without chromic ...
Lars Dammann +8 more
wiley +1 more source
YOLO12 Down Feather Quality Classification Method Based on A2C2f-CGLU Feature Enhancement. [PDF]
Fan Z, Jing S, Tong L, Sun X.
europepmc +1 more source
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source

