Results 131 to 140 of about 206,029 (232)

Efficient Wheat Disease Identification Using Hybrid Swin-SHARP Vision Model

IEEE Access
Accurate identification of wheat diseases is an essential component for increasing crop yields and guaranteeing global food security. However, subjective opinions, errors, and laborious procedures frequently limit traditional approaches, which are based ...
Waqar Khalid, Yazeed Alkhrijah, Syed Muhammad Usman, Shehzad Khalid   +3 more
doaj   +1 more source

Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer

Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes.
Bofan Song, Dharma Raj KC, Shaobai Li, Rubin Yuchan Yang, Rongguang Liang, Chicheng Zhang   +5 more
core   +1 more source

AI‐Based Regional Emulation for Kilometer‐Scale Dynamical Downscaling

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract An AI‐based Limited‐Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities under diverse boundary conditions. The model is trained using 0.25° ${}^{\circ}$, 3‐hourly ERA5 as forcings and CONUS404 as targets in 1980–2019, producing 4‐km ...
Yingkai Sha, Tracy Hertneky, Ethan D. Gutmann, Seth McGinnis, Rachel McCrary, Lulin Xue, David John Gagne II, Kathryn Newman, Andrew Newman   +8 more
wiley   +1 more source

PhyTGNet: A Physics‐Prior‐Guided Interpretable Forecasting Method for Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract The sudden and localized nature of thunderstorm gusts makes pure data‐driven models struggle to maintain physical plausibility and interpretability, leading to nonphysical false alarms and spatial displacement in complex terrain. To address this, we propose PhyTGNet, a deep learning framework with embedded physics‐inspired guidance that ...
Zhaoyu Yan, Mingxuan Chen, Lu Yang, Lei Han, Linye Song, Xinze Li   +5 more
wiley   +1 more source

Architectural Scalability and Attention Stabilization for Robust Impulsive Acoustic Spectrogram Classification Using Swin Transformer V2

IEEE Access
Impulsive acoustic event classification based on spectrogram representations remains challenging due to the non-stationary and broadband characteristics of transient signals.
Pafan Doungpaisan, Peerapol Khunarsa
doaj   +1 more source

Performance Evaluation of Swin Vision Transformer Model using Gradient Accumulation Optimization Technique

, 2023
Vision Transformers (ViTs) have emerged as a promising approach for visual recognition tasks, revolutionizing the field by leveraging the power of transformer-based architectures.
Dorgham, Osama, Aburass, Sanad
core  

Benchmarking Auto‐Regressive Spatio‐Temporal Predictions of Wildfire Spread From the FireBench Simulation Data Set With a Machine Learning Competition

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract As wildfire severity increases, predicting wildfire spread is important for fire and landscape management. However, wildfire spread is a complex phenomenon that is challenging to capture because it depends on many factors, such as wind speed, slope, landscape topography, and fuel properties. Machine Learning (ML) models are powerful tools that
J. Z. Ho, A. Bazgir, A. Asaithambi, J. Hossain, R. Pawłowski, Z. Li, T. Dubail, Q. Wang, C. Gazen, B. Akoush, W. T. Chung, W. Reade, M. Ihme   +12 more
wiley   +1 more source

Capsule-enhanced hierarchical vision transformers for rare disease classification from medical images

Scientific Reports
Automated medical image analysis plays a vital role in rare disease detection, yet existing deep learning models often struggle with severe class imbalance, limited labeled data, and subtle morphological variations.
E. S. Phalguna Krishna, Gowtham Mamidisetti, Sai Srinivas Vellela, Kranthi Kumar Lella, Veeraiah Duggineni, N. Balakrishna   +5 more
doaj   +1 more source

Global Ionospheric TEC Forecasting Driven by a Unified Physical Index System via the Spectral Whitening Method

Space Weather, Volume 24, Issue 10, October 2026.
Abstract Accurate forecasting of global ionospheric total electron content (TEC) is important for Global Navigation Satellite Systems (GNSS) performance and satellite communications. This study proposes a driver framework based on the spectral whitening method (SWM). We combine a new high‐latitude geomagnetic disturbance index with previously developed
Zhou Chen, Jiahao Sun, Jing‐song Wang, Yiwan Deng, Jianxiang Zhang, Jiahao Zeng, Lewei Fan, Zichen Xu, Jian Song, Shourong Lu   +9 more
wiley   +1 more source

A Near‐Real‐Time Deep‐Learning Solar Flare Forecasting System Supported by SDO/HMI and ASO‐S/FMG Magnetograms

Space Weather, Volume 24, Issue 10, October 2026.
Abstract This study develops and evaluates five deep learning models, including image‐based (CNN, SwinT) and video‐based (CNN‐LSTM, SwinT‐LSTM, Video SwinT), to forecast ≥ ${\ge} $M‐class flares within the next 24 hr using cross‐validation (CV) data sets.
Yongshang Lv, Pengchao Yan, Yanfang Zheng, Xuebao Li, Suo Liu, Ting Li, Jiaben Lin, Rui Wang, Liang Dong, Zamri Zainal Abidin, Noraisyah Mohamed Shah, Honglei Jin, Hongwei Ye, Shunhuang Zhang, Xiaotian Wang, JinFang Wei, Zixian Wu, Xusheng Huang   +17 more
wiley   +1 more source