Results 71 to 80 of about 4,684 (203)

Report on the 13th international workshop on the CCN family of genes and inaugural ARBIOCOM conference, Nice, France

Journal of Cell Communication and Signaling, Volume 20, Issue 3, September 2026.
Celebration of the 13th ICCNS meeting joint to the 1st ARBIOCOM Conference held on the site of Nice in December 2025. Abstract The 13th International Workshop on the CCN Family of Genes, held in Nice as part of the inaugural ARBIOCOM World Conference, brought together investigators with diverse research backgrounds, disciplines, and expertise. Building
Bernard Perbal, Ralf Weiskirchen, Annick Perbal, Brahim Chaqour   +3 more
wiley   +1 more source

Federated target trial emulation using distributed observational data for treatment effect estimation

npj Digital Medicine
Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is ...
Haoyang Li, Chengxi Zang, Zhenxing Xu, Weishen Pan, Suraj Rajendran, Yong Chen, Fei Wang   +6 more
doaj   +1 more source

Federated Learning Incentive Mechanism with Supervised Fuzzy Shapley Value

Axioms
The distributed training of federated machine learning, referred to as federated learning (FL), is discussed in models by multiple participants using local data without compromising data privacy and violating laws. In this paper, we consider the training
Xun Yang, Shuwen Xiang, Changgen Peng, Weijie Tan, Yue Wang, Hai Liu, Hongfa Ding   +6 more
doaj   +1 more source

A Systematic Review and Taxonomy of Fall Detection in Geriatric Care: Current State, Challenges, and Applications

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
Overview of fall‐detection approaches and technologies in geriatric care. ABSTRACT This systematic review provides a critical examination of fall detection (FD) technologies in geriatric care, analyzing both technological innovations and implementation challenges across diverse healthcare environments.
Hamid Ali, Ji Zhang, Xiaohui Tao, Raj Gururajan, Xujuan Zhou, Yuquan Gan, Josh Jia‐Ching Ying   +6 more
wiley   +1 more source

Federated learning for predicting compound mechanism of action based on image-data from cell painting

Artificial Intelligence in the Life Sciences
Having access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for various ...
Li Ju, Andreas Hellander, Ola Spjuth
doaj   +1 more source

Consumer Behavior Analysis in Digital Marketing Using AI and Big Data Analytics: A Narrative Review and Methodological Taxonomy

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
AI and Big Data in Consumer Behavior Analysis. ABSTRACT The rapid expansion of digital consumer data has challenged traditional approaches to understanding behavior in digital marketing. Existing reviews often focus on individual methods and give limited guidance on how analytical techniques compare or how they should be selected for specific marketing
Leonidas Theodorakopoulos, Alexandra Theodoropoulou   +1 more
wiley   +1 more source

ns3-fl: Simulating Federated Learning with ns-3

Proceedings of the 2022 Workshop on ns-3, 2022
Emily Ekaireb, Xiaofan Yu 0001, Kazim Ergun, Quanling Zhao, Kai Lee, Muhammad Huzaifa, Tajana Rosing   +6 more
openaire   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Federated Meta Reinforcement Learning for Personalized Tasks

Tsinghua Science and Technology
As an emerging privacy-preservation machine learning framework, Federated Learning (FL) facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and private ...
Wentao Liu, Xiaolong Xu, Jintao Wu, Jielin Jiang   +3 more
doaj   +1 more source

ALDP-FL for adaptive local differential privacy in federated learning

Scientific Reports
Federated learning, as an emerging distributed learning framework, enables model training without compromising user data privacy. However, malicious attackers may still infer sensitive user information by analyzing model updates during the federated learning process.
Cui, Lixin, Wu, Xu
openaire   +2 more sources