Results 31 to 40 of about 379 (188)

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods

International Journal of Gynecology &Obstetrics, EarlyView.
Abstract Objectives The current study evaluates the efficacy of artificial intelligence (AI)–assisted measurement of cervical length (CL) in predicting spontaneous preterm birth (sPTB), comparing the traditional single‐line and two‐line methods with the innovative AI‐line method in the first trimester of pregnancy. Materials and Methods This study is a
Yi‐yun Tai, Bor‐Yann Tseng, Zhu‐Han Yang, Chi‐Hua Yu, Liona C. Poon   +4 more
wiley   +1 more source

BiT‐MoFE: Unified illumination correction in capsule endoscopy

Journal of Intelligent Medicine, EarlyView.
Abstract As a prevalent non‐invasive screening technique, Wireless Capsule Endoscopy is often hindered by poor image quality, including under‐/overexposure and low light condition. While illumination correction based on diffusion modeling or frequency‐domain decomposition has shown effectiveness, existing methods often (1) underexploit structural ...
Haoyu Ding, Yuntong Tian, Tianling Liu, Hongying Liu, Liang Wan   +4 more
wiley   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

Using U‐Nets to Predict the Effects of Head Motion on Simulated Specific Absorption Rate for Ultra‐High Field Magnetic Resonance Imaging With Parallel Transmission

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Ultrahigh‐field MRI requires careful management of the specific absorption rate (SAR), which is subject and subject‐position dependent. Within‐scan subject motion may exacerbate local SAR exposure, necessitating large safety margins to prevent SAR underestimation, which hampers imaging performance.
Katherine Anna Blanter, Alix Plumley, Alper Gungor, Shaihan Malik, Emre Kopanoglu   +4 more
wiley   +1 more source

Self‐Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi‐b Prostate Diffusion MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Convolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi‐b DW images with simultaneous signal modeling. Methods Prostate diffusion images from 46 individuals acquired at 20 linearly distributed b‐values (bmax=2000s/mm2)$$ {b}_{\mathrm{max}}=2000\kern0.3em \mathrm{s}/{\
Mustafa Abbas, Wenyin Zhou, Stephan E. Maier, Fredrik Langkilde, Stefan Kuczera   +4 more
wiley   +1 more source

Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global–Local Two‐Branch Deep Learning Model

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Phosphocreatine (PCr) is an essential marker of muscle metabolism, and accurate quantification of its (fs) and its exchange rate (ksw) is essential for diagnosing various muscular and neuromuscular diseases. Although chemical exchange saturation transfer (CEST) MRI can detect the saturation transfer effect from PCr, quantification of ...
Malvika Viswanathan, Leqi Yin, Yashwant Kurmi, Shuyang Chai, Xiaoyu Jiang, Yuankai Huo, Junzhong Xu, Limin Chen, John C. Gore, Zhongliang Zu   +9 more
wiley   +1 more source

GABA+‐Edited Magnetic Resonance Spectroscopy Deep Learning Quality Assessment Framework

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Motivated by the need to improve GABA+‐edited magnetic resonance spectroscopy (MRS) quality, we developed a three‐module framework to improve transient averaging based on quality. We hypothesized that training a deep learning (DL) model to differentiate spectrum quality could improve transient averaging compared to traditional ...
Hanna Bugler, Roberto Souza, Ashley D. Harris   +2 more
wiley   +1 more source

Detecting Plateau Zokor (Eospalax baileyi) Mounds in UAV Imagery of Alpine Meadows Using Deep Learning Algorithms

Remote Sensing in Ecology and Conservation, EarlyView.
We developed PZM‐YOLO to automatically detect plateau zokor mounds in UAV imagery of alpine meadows. The model achieved reliable detection of small and densely distributed mounds under complex backgrounds, outperforming the baseline YOLOv5s. This framework supports mound counting, mound position, rodent impact assessment, and grassland restoration ...
Yang Yang, Limin Hua, Lianguo Wang, Jiazhen Li, Rui Dong, Siwei Yang   +5 more
wiley   +1 more source

Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments

Small, EarlyView.
This study presents an interpretable, lightweight hybrid deep learning model for real‐time analysis of breast cancer histopathology in IoMT‐enabled diagnostic systems. By integrating MobileNetV2 and EfficientNet‐B0 with a novel contextual recurrent attention module (CRAM), the framework achieves near‐perfect accuracy while providing transparent Grad ...
Roseline Oluwaseun Ogundokun, Rotimi‐Williams Bello, Pius Adewale Owolawi, Rytis Maskeliūnas, Abdulsatar Abduljabbar Sultan   +4 more
wiley   +1 more source