Results 101 to 110 of about 6,308,360 (314)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Review on Applications and Prospects of Federated Learning in New Power Systems

工程科学与技术
SignificanceAgainst the background of new power system development, massive amounts of multi-source heterogeneous data are distributed across multiple data owners.
HAN Fujia, QIAO Ji, LI Jiateng, TANG Zhuang, ZHAO Zixuan   +4 more
doaj  

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Applied Sciences, 2018
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions.
Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, Elisabeth Ilie-Zudor   +5 more
doaj   +1 more source

Bayesian Federated Learning with Stochastic Variational Inference

Federated Learning (FL) faces significant challenges, such as handling non-IID (Non-Independent and Identically Distributed) data and efficiently aggregating distributed models, which can lead to slower convergence and reduced model accuracy.
Ali, Muhammand Intizar, Ali, Muhammand Intizar,, Awaysheh, Feras, Tahir, Mehreen,, Alawadi, Sadi,, Tahir, Mehreen, Alawadi, Sadi, Awaysheh, Feras,   +7 more
core   +1 more source

Current Standards of Monitoring Models in Healthcare Settings

Advanced Intelligent Discovery, EarlyView.
AI/ML‐enabled medical devices are entering clinical practice faster than monitoring standards mature. This review highlights gaps in postmarket surveillance, limited use of predetermined change‐control plans, and the need for ongoing performance tracking, drift detection, explainability, and workflow‐aware governance to support safer, more reliable ...
Alan Kay, Daljit Takher, Wenting Liu, Emily Wittrup, Gilbert S. Omenn, Kayvan Najarian   +5 more
wiley   +1 more source

nicolasfara/experiments-2024-ACSOS-opportunistic-federated-learning: 1.4.0

<h2><a href="https://github.com/nicolasfara/experiments-2024-ACSOS-opportunistic-federated-learning/compare/1.3.0...1.4.0">1.4.0</a> (2024-04-23)</h2> <h3>Features</h3> <ul> <li>add baseline sim alchemist ...
Semantic Release Bot, Gianluca Aguzzi, Davide Domini, Nicolas Farabegoli   +3 more
core   +1 more source

Resource Allocation through Auction-based Incentive Scheme for Federated Learning in Mobile Edge Computing [PDF]

, 2023
openMobile Edge Computing (MEC) combinedly with Federated Learning is con- sidered as most capable solutions to AI-driven services. Most of the studies focus on Federated Learning on security aspects and performance, but the re- search is lacking to ...
ASIF, JAWAD
core  

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Mathematics
Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data.
Jianping Wu, Jiahe Jin, Chunming Wu
doaj   +1 more source