Results 171 to 180 of about 60,338 (274)

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

GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer. [PDF]

Int J Mol Sci
Fu Z, Li Y, Du Z, Zhang D, Liu L, Jin Q, Chen X, Xie H.   +7 more
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

Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches. [PDF]

AIMS Neurosci
Noor NSEM.
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

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

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

Image super-resolution method using a generative adversarial network incorporating attention and residual density. [PDF]

Sci Rep
Zhang Q, Hang Y, Zhang H, Lu X, Qiu J.
europepmc   +1 more source

Recommender Systems: Taxonomy, Applications and Current Research Trends

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
Integrating taxonomy, application developments, open‐source software, and publication trends, this paper identifies and outlines promising future directions for recommender systems research. ABSTRACT Recommender Systems play an essential role in assisting users to navigate the immense amount of information and services available online, aiding them in ...
Daniel Ranchal‐Parrado, Carlos Porcel, Jesús Alcalá‐Fdez   +2 more
wiley   +1 more source

Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks. [PDF]

Sci Rep
Yin G, Yin M, Guo G, Yuan J, Ma X, Wang L, Zhao P, Shi D, Zhao Y, Zhao Z, Wang B, Tan S.   +11 more
europepmc   +1 more source