Results 91 to 100 of about 8,226 (198)

Quantifying Shuttlecock Skill Progression via Multimodal Action Recognition and Dynamics‐Guided Trajectory Prediction

Engineering Reports, Volume 8, Issue 10, October 2026.
This study proposes a multimodal feature fusion‐based action recognition and trajectory prediction framework to quantitatively evaluate the effectiveness of compensatory exercises in accelerating shuttlecock skill acquisition. The system comprehensively employs TrackNet, Faster R‐CNN, and HRNet to extract shuttlecock trajectories, player positions, and
Chao Zhang, Fei Zeng
wiley   +1 more source

A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs

Scientific Reports
Oral diseases are increasing now-a-days and there is a high demand for the automatic diagnostic system that helps the clinician to detect these oral diseases with more accuracy and reduced human error.
Ramasubramanian Bhoopalan, S. Mirdula, Priyadharshini Kannusamy, D. Gayathri, D. Manikandan, Krishnaraj Ramaswamy   +5 more
doaj   +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

A Comprehensive Review of GAN‐CNN Hybrid Methods for Intelligent Fault Diagnosis of Rotating Machinery

Engineering Reports, Volume 8, Issue 10, October 2026.
This paper reviews recent advances in hybrid deep learning for intelligent fault diagnosis of rotating machinery, focusing on GAN‐ and CNN‐based architectures and their specialized variants. Hybrid GAN‐CNN methods are categorized into three groups: (1) optimized original GANs with CNN variants, (2) improved GAN architectures integrated with CNNs, and ...
Temesgen Tadesse Feisa, Fasikaw Kibrete, Yitbarek Gashaw Tadesse, Abdu Mohammed Seid, Sewale Yasabu Enyew, Getachew Getu Enyew, Mulugeta Linger Tariku, Zewdie Alemayehu Mulat   +7 more
wiley   +1 more source

A two‐stage framework for fast proton spot map generation in pencil beam scanning prostate SBRT planning

Medical Physics, Volume 53, Issue 10, October 2026.
Abstract Background In pencil beam scanning (PBS) proton therapy, treatment plans are encoded as proton spot maps (PSMs). Although deep‐learning methods can rapidly predict three‐dimensional (3D) dose distributions from patient anatomy and planning intent, there is still no widely adopted direct, non‐iterative approach to convert doses into physically ...
Xueyan Tang, Hok Wan Chan Tseung, Mark Pepin, Jiasen Ma, David M. Routman, Doug J. Moseley, Brandon Reber, Jed E. Johnson, Jing Qian   +8 more
wiley   +1 more source

Automated Lawn Maintenance: An Agronomic and Operational Review of Turf Health, Biodiversity, and Field Performance

Journal of Field Robotics, Volume 43, Issue 7, Page 4603-4623, October 2026.
ABSTRACT Grass mowing is one of the most resource‐consuming activities in green maintenance, whether in private areas such as home gardens or in public spaces like urban parks. In recent years, concerns related to climate change, human health, and sustainability have become increasingly prominent in green maintenance, leading manufacturers and industry
Andrea Palladini, Lorenzo Guerrini, Marco Fontanelli, Lucia Bortolini   +3 more
wiley   +1 more source

Downscaling of Tropical Cyclone Surface Wind Fields With a Hybrid Attention Transformer

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five.
Chunhua Wang, Dazhi Xi, Na He, Shuai Wang, Kekuan Chu   +4 more
wiley   +1 more source

Transfer Learning With U-Swin Transformer for Adaptive Ground Roll Attenuation in Seismic Records From Sabkha Environments

IEEE Access
Ground roll is a dominant coherent noise in land seismic data, characterized by low frequency, low velocity, and high amplitude. often overlaps with reflection arrivals, thereby reducing the reliability of seismic imaging and interpretation.
Ahmed Eleslambouly, Guibin Zhao, Fateh Bouchaala, Mohammed Y. Ali, Jun Matsushima   +4 more
doaj   +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

EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection

CoRR
Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs.
Hung Mai, Loi Dinh, Duc Hai Nguyen, Dat Do, Luong Doan, Khanh Nguyen Quoc, Huan Vu, Phong Ho, Naeem Ul Islam, Tuan Do   +9 more
openaire   +2 more sources