Results 71 to 80 of about 13,513 (170)

Use of Automation Technologies and Data Mining in Speech Recognition for Autism

Brain and Behavior, Volume 16, Issue 2, February 2026.
Pipeline analyzes clinical and naturalistic speech using LENA, wav2vec 2.0, and foundation‐model ASR (Whisper) to enable scalable ASD detection and severity estimation. Future work integrates benchmarking, privacy‐preserving collaboration (federated learning), and explainable, edge‐ready AI for clinically credible assessment and longitudinal monitoring.
Rongjie Mao, Yuncheng Zhu
wiley   +1 more source

Toward precision oncology: An integrative multi‐omics approach for prognosis prediction and inferred immunotherapy responsiveness in breast cancer

Clinical and Translational Discovery, Volume 6, Issue 1, February 2026.
We integrated four omics to build a breast cancer immunotherapy predictor. Survival‐associated biomarkers were compressed into 200 latent features via an autoencoder, then refined using three survival models. K‐means defined two subgroups (C1, high‐risk and C2, low‐risk).
Houda Bendani, Nasma Boumajdi, Lahcen Belyamani, Azeddine Ibrahimi   +3 more
wiley   +1 more source

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Applied Sciences
This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as ...
Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury   +7 more
doaj   +1 more source

segcsvdPVS: A Convolutional Neural Network‐Based Tool for Quantification of Enlarged Perivascular Spaces (PVS) on T1‐Weighted Images

Human Brain Mapping, Volume 47, Issue 2, February 1, 2026.
segcsvdPVS is a novel convolutional neural network (CNN)‐based perivascular spaces (PVS) segmentation tool for T1‐weighted images based on a hierarchical segmentation framework developed with strategically designed inputs and robust training. It demonstrates high accuracy, consistent performance, and improved detection of age‐related changes in PVS ...
Erin Gibson, Joel Ramirez, Lauren Abby Woods, Stephanie Berberian, Julie Ottoy, Christopher J. M. Scott, Vanessa Yhap, Fuqiang Gao, Roberto Duarte Coello, Maria Valdes Hernandez, Anthony E. Lang, Carmela M. Tartaglia, Sanjeev Kumar, Malcolm A. Binns, Robert Bartha, Sean Symons, Richard H. Swartz, Mario Masellis, Navneet Singh, Bradley J. MacIntosh, Joanna M. Wardlaw, Sandra E. Black, ONDRI Investigators, ADNI, CAHHM Investigators, CAIN Investigators, colleagues from the Foundation Leducq Transatlantic Network of Excellence, Andrew S. P. Lim, Maged Goubran   +28 more
wiley   +1 more source

Federated Multi‐Source Data Fusion for Semi‐Supervised Fault Detection in District Heating Substations

Expert Systems, Volume 43, Issue 2, February 2026.
ABSTRACT Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low‐frequency data, privacy concerns, and battery‐constrained gateways. We propose a novel hybrid semi‐supervised federated domain adaptation architecture for fault detection in DH.
Jonne van Dreven, Sadi Alawadi, Abbas Cheddad, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt   +5 more
wiley   +1 more source

Comparative analysis of autoencoder architectures for breast cancer detection using dynamic infrared thermography

Engineering Science and Technology, an International Journal
Breast cancer is the most diagnosed cancer among women worldwide. Early detection substantially improves treatment outcomes, especially when lesions are small and localized.
Burcu Acar Demirci, Mehmet Engin, Erkan Zeki Engin   +2 more
doaj   +1 more source

WOT-AE: Weighted Optimal Transport Autoencoder for Patterned Fabric Defect Detection

Symmetry
Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value ...
Hui Yang, Linyan Kang, Tianjin Yang
openaire   +1 more source

Real‐Time Optimal Control of High‐Dimensional Parametrized Systems by Deep Learning‐Based Reduced Order Models

International Journal for Numerical Methods in Engineering, Volume 127, Issue 1, 15 January 2026.
ABSTRACT Steering a system towards a desired target in a very short amount of time is a challenging task from a computational standpoint. Indeed, the intrinsically iterative nature of optimal control problems requires multiple simulations of the state of the physical system to be controlled. Moreover, the control action needs to be updated whenever the
Matteo Tomasetto, Andrea Manzoni, Francesco Braghin   +2 more
wiley   +1 more source

Intelligent Fault Diagnosis of Gas Pressure Regulator Based on AE-GWO-SVM Algorithm

Automation
A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production.
Shunyuan Hu, Zhixiang Dai, Keqing Zhang, Jingjian Liu, Qing Wen, Yanhua Qiu, Weidong Li   +6 more
doaj   +1 more source

Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction

Machines
To solve the problem of insufficient accuracy in tool wear process modeling and Remaining Useful Life (RUL) estimation, this study proposes a two-stage prediction method.
Lihua Shen, Baorui Du, He Fan, Hailong Yang   +3 more
doaj   +1 more source