Results 31 to 40 of about 294 (197)

Building an Intelligent Cardiovascular System Platform: Embedding Artificial Intelligence across All Facets of Cardiovascular Medicine

Advanced Intelligent Systems, EarlyView.
This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong, Xingwei Zhao, Qiuting Li, Yang Yu, Chunxiang Zhang   +4 more
wiley   +1 more source

Dual‐Scale Transformer Fusion With Meta Learning for Micro Metastasis Detection in Thyroid Cancer

Advanced Intelligent Systems, EarlyView.
A dual‐scale transformer model enhanced by meta‐learning enables accurate detection of tiny metastatic lesions in thyroid cancer. By combining cellular and tissue‐level features, the method outperforms existing models and shows strong adaptability to rare cases with limited data.
Jingtao Wang, Junhai Shi, Hengli Ni, Limei Zhao, Jing Huang, Jianming Li   +5 more
wiley   +1 more source

Hierarchical Complementary Enhanced Autoencoder Integrating Spatio‐Temporal Interaction Feature for Soft Sensor

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT To address the issues of neglecting the spatiotemporal correlations among process variables, low‐level features are vulnerable to noise interference, and the gradual loss of key information layer by layer during deep network training in traditional stacked autoencoder‐based soft‐sensor models, this paper proposes a hierarchical complementary ...
Xiaoping Guo, Jinghong Guo, Yuan Li
wiley   +1 more source

Harnessing deep learning to quantify microstructural complexity in confocal images of plant protein gels

Food Biomacromolecules, EarlyView.
Abstract Understanding plant protein gel microstructure is key to designing functional food systems. This study introduces a deep learning framework using a U‐Net model with a ResNet34 encoder to segment and quantify confocal laser scanning microscopy (CLSM) images of plant protein gels.
Zhi Yang
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

Artificial Intelligence in Multimedia Content Generation: A Review of Audio and Video Synthesis Techniques

Journal of the Society for Information Display, EarlyView.
Modern AI systems can now synthesize coherent multimedia experiences, generating video and audio directly from text prompts. These unified frameworks represent a rapid shift toward controllable and synchronized content creation. From early neural architectures to transformer and diffusion paradigms, this paper contextualizes the ongoing evolution of ...
Charles Ding, Rohan Bhowmik
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

Accelerating Multiparametric Quantitative MRI Using Self‐Supervised Scan‐Specific Implicit Neural Representation With Model Reinforcement

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a self‐supervised scan‐specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). Methods We propose REFINE‐MORE (REference‐Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement ...
Ruimin Feng, Albert Jang, Xingxin He, Fang Liu   +3 more
wiley   +1 more source

From “Mirror Flower, Water Moon” to Multi‐Task Visual Prospective Representation Learning for Unmanned Aerial Vehicles Indoor Mapless Navigation

Journal of Field Robotics, EarlyView.
ABSTRACT Vision‐based deep learning models have been widely adopted in autonomous agents, such as unmanned aerial vehicles (UAVs), particularly in reactive control policies that serve as a key component of navigation systems. These policies enable agents to respond instantaneously to dynamic environments without relying on pre‐existing maps.
Yingxiu Chang, Yongqiang Cheng, John Murray, Muhammad Khalid, Umar Manzoor   +4 more
wiley   +1 more source

Distributed AutoML framework for multi‐objective optimization of concrete crack segmentation models

Structural Concrete, EarlyView.
Abstract Monitoring cracks in concrete surfaces is essential for structural safety. While machine vision techniques have received significant interest in this domain, selecting optimal models and tuning hyperparameters remain challenging. This paper proposes a Distributed Automated Machine Learning (AutoML) framework for efficiently designing and ...
Armin Dadras Eslamlou, Shiping Huang, Belén Riveiro, M. Z. Naser   +3 more
wiley   +1 more source