Results 171 to 180 of about 45,910 (264)

Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis. [PDF]

Front Med (Lausanne)
Vina A, Prajasree G, Venkatesh S, Sankaranarayanan S, Meenakshi K, Khan AR, Imam SBS, Wahab Sait AR.   +7 more
europepmc   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Synthetic artificial intelligence in cardiology: from generative models to clinical applications. [PDF]

Eur Heart J Open
Parise G, Ceravolo R, Lucà F, Gulizia MM, Tetta C, Parise O, Nardi F, Grimaldi M, Gelsomino S.   +8 more
europepmc   +1 more source

Advancing Marine Bioacoustics With Deep Generative Models: A Hybrid Augmentation Strategy for Southern Resident Killer Whale Detection

Marine Mammal Science, Volume 42, Issue 3, July 2026.
ABSTRACT Automated detection and classification of marine mammal vocalizations is critical for conservation and management efforts but is hindered by limited annotated datasets and the acoustic complexity of real‐world marine environments. Data augmentation has proven to be an effective strategy to address this limitation by increasing dataset ...
Bruno Padovese, Fabio Frazao, Michael Dowd, Ruth Joy   +3 more
wiley   +1 more source

Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept Study. [PDF]

JMIR Form Res
Md Zuki MA, Mohamad Ali N, Chaw JK.
europepmc   +1 more source

Cross‐Method Explanation Stability Under Prediction‐Preserving Perturbations in Explainable AI

Applied AI Letters, Volume 7, Issue 2, June 2026.
The cross‐method analysis showed common vulnerability patterns across gradient‐based and perturbation‐based explainers, whereas Grad‐CAM demonstrated a specific ability to be resilient. Further discussion revealed that, before prediction changes with increasing ε, explanation divergence could already have commenced, indicating that further explanation ...
Muhammad Hasnain, Tooba Gul, Muhammad Fawad Ahmed, Israr Ghani, Imran Ghani   +4 more
wiley   +1 more source

Comparative evaluation of generative artificial intelligence models for synthetic knee radiograph augmentation in clinical research. [PDF]

BMC Med Imaging
Chung K, Nam JH, Dosset A, Koh YG, Kim JM, Kim PS, Lee JW, Park KM, Kwon HM, Kang KT.   +9 more
europepmc   +1 more source

Impact of Dataset Size and Hyperparameters Tuning in a Variational Autoencoder for Structure–Property Mapping in Porous Metamaterials

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT This study extends a deep learning framework for the inverse reconstruction of open‐cell porous metamaterials targeting specific hydraulic properties such as porosity and intrinsic permeability. Building on our recent work that employed a property‐variational autoencoder (pVAE) for structure–property mapping, the current contribution examines ...
Phu Thien Nguyen, Badr‐eddine Ayouch, Yousef Heider, Fadi Aldakheel   +3 more
wiley   +1 more source

Point cloud generation adversarial network based on self-attention and curvature. [PDF]

PLoS One
Sun F, Shen C, Kong Y, Zhang Z, Hu M, Xiong F.   +5 more
europepmc   +1 more source

Diffusional magnetic resonance imaging anonymizing with variational autoencoder

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Anonymization is a crucial de‐identification technique that protects data privacy while ensuring its utility for model building. Current generative models such as generative adversarial networks and variational auto‐encoders (VAEs) have been applied to medical image anonymization but mainly focus on general image features, lacking specificity ...
Yunheng Shen, Ling Zheng, Ruohan Liu, Haoran Feng, Hairong Lv   +4 more
wiley   +1 more source