Results 91 to 100 of about 14,679 (231)

Can a Single Sounding Predict the Weather? An AI Downscaling Framework for Local‐Circulation‐Dominated Flow Over Taiwan's Complex Topography

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract Typical AI downscaling models ingest large data sets, obscuring physical insight and straining computational resources. Guided by meteorological theory, a compact yet physically informative input set alleviates these limitations. Analyses of TaiwanVVM simulations reveals that the synoptic‐scale upstream sounding largely controls the evolution ...
Fucent Hsuan Wei Hsu, Min‐Ken Hsieh, Chien‐Ming Wu   +2 more
wiley   +1 more source

Graph‐guided frequency‐enhanced state space network for 3D spine segmentation from MR images

Journal of Applied Clinical Medical Physics, Volume 27, Issue 2, February 2026.
Abstract Background Accurate spinal MRI segmentation is essential for computer‐aided diagnosis of spinal diseases. Existing methods have limitations in global semantic modeling and boundary delineation due to complex anatomy and imaging artifacts. Purpose Our work aimed to propose a novel Graph‐Guided Frequency‐Enhanced State Space Network (GF‐SSNet ...
Linghui Hong, Zhengchao Zhou, Wanbo Xu, Pingping Wang, Benzheng Wei   +4 more
wiley   +1 more source

Automated AI‐Based Lung Disease Classification Using Point‐of‐Care Ultrasound

Applied AI Letters, Volume 7, Issue 1, February 2026.
Automated, AI‐based Lung Disease Classification using Point‐of‐Care Ultrasound. ABSTRACT Timely and accurate diagnosis of lung diseases is critical for reducing related morbidity and mortality. Lung ultrasound (LUS) has emerged as a useful point‐of‐care tool for evaluating various lung conditions.
Nixson Okila, Andrew Katumba, Joyce Nakatumba‐Nabende, Sudi Murindanyi, Jonathan Serugunda, Cosmas Mwikirize, Samuel Bugeza, Anthony Oriekot, Juliet Bosa, Eva Nabawanuka   +9 more
wiley   +1 more source

An Effective Approach for Recognition of Crop Diseases Using Advanced Image Processing and YOLOv8

Food Science &Nutrition, Volume 14, Issue 2, February 2026.
The performance of processed images is evaluated using mean‐squared‐error and peak‐signal‐to‐noise ratio. After the processing phase, an advanced deep learning model, YOLOv8, was used for the segmentation and classification of crop diseases. Using a large dataset comprising 32 diseases to train our model, we implemented Transfer Learning using YOLOv8 ...
Muhammad Nouman Noor, Muhammad Masab, Farah Haneef, Muzammil Hussain, Mateen Yaqoob, Tehseen Mazhar, Muhammad Amir Khan, Ghadah Aldehim   +7 more
wiley   +1 more source

Multi-scale capsule Swin Transformer-based method for SAR image target recognition

Tongxin xuebao
A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR
HOU Yuchao, WANG Jie, LI Hongtao, HAO Yan, DUAN Xiaoqi, HUANG Kaiwen, TIAN Youliang   +6 more
doaj  

Pavement Crack Detection Based on the Improved Swin-Unet Model

Buildings
Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip ...
Song Chen, Zhixuan Feng, Guangqing Xiao, Xilong Chen, Chuxiang Gao, Mingming Zhao, Huayang Yu   +6 more
doaj   +1 more source

Automated Glaucoma Detection Using Vision and Swin Transformers: Advancing Ophthalmic AI [PDF]

Purpose:Glaucoma is one of the most common causes of permanent blindness in the world; early detection and precise diagnosis are essential to successful treatment.Convolutional Neural Networks (CNNs) are one of the deep learning techniques that have ...
Gireesh, Dr.N., Sakunthala, D.
core   +2 more sources

FML-Swin: An Improved Swin Transformer Segmentor for Remote Sensing Images

IEEE Access
Semantic segmentation of urban remote sensing images is a very challenging task. Due to the complex background, occlusion overlap and small scale target of urban remote sensing image, the semantic segmentation results have some defects such as target confusion and similarity, target boundary ambiguity, and small scale target omission.
Tianren Wu, Wenqin Deng, Rui Lin, Junzhe Jiang, Xueyun Chen   +4 more
openaire   +2 more sources

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging‐Based Dementia Diagnosis and Prognosis

Human Brain Mapping, Volume 47, Issue 2, February 1, 2026.
Artificial intelligence has the potential to enhance clinical decision making, but complex black box models can be difficult to interpret and trust. Explainable artificial intelligence methods aim to open the black box by highlighting which features are driving the model's prediction.
Sophie A. Martin, An Zhao, Jiongqi Qu, Phoebe Imms, Andrei Irimia, Frederik Barkhof, James H. Cole, Alzheimer's Disease Neuroimaging Initiative   +7 more
wiley   +1 more source

Off-line identifying Script Writers by Swin Transformers and ResNeSt-50

ELCVIA Electronic Letters on Computer Vision and Image Analysis
In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50.
Afef Kacem Echi, Takwa Ben Aïcha Gader
doaj   +1 more source