Results 81 to 90 of about 129,870 (304)

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

Advanced Intelligent Discovery, EarlyView.
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

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

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

FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction

Advanced Intelligent Discovery, EarlyView.
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker, Enzo Raffaele Moretti, Jin Hyun Chang, Tejs Vegge   +3 more
wiley   +1 more source

Federated Learning-Based Framework: A New Paradigm Proposed for Supply Chain Risk Management

Engineering Proceedings
This paper proposes federated learning-based frameworks for supply chain risk management to address data-sharing constraints. To validate, centralized federated learning with horizontal data was applied for delivery delay prediction using datasets from ...
Thanh Tuan Nguyen, Abdelghani Bekrar, Thi Muoi Le, Abdelhakim Artiba, Tarik Chargui, Thi Thu Huong Trinh, Ahmed Snoun   +6 more
doaj   +1 more source

Federated Reconstruction: Partially Local Federated Learning

, 2021
Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients to communicate all model parameters can be undesirable due to privacy and communication constraints.
Singhal, Karan, Sidahmed, Hakim, Garrett, Zachary, Wu, Shanshan, Rush, Keith, Prakash, Sushant   +5 more
openaire   +2 more sources

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

FedADC: Federated Average Knowledge Distilled Mutual Conditional Learning (FedADC) for Waste Classification

IEEE Access
Federated learning presents a potent avenue for addressing challenges in waste classification, where diverse datasets are distributed across sources.
Ananya Ghosh, Parthiban Krishnamoorthy
doaj   +1 more source

AI Powered Biobanks From Static Archives to Dynamic Discovery Engines

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) provide a potential framework for transforming biobanks from static data repositories into intelligent discovery engines. By enabling unified representation and analysis of multimodal biomedical data, LLM‐based systems facilitate dynamic risk prediction, biomarker identification, and mechanistic interpretation, thereby ...
Wenzhen Yin, Yutian Feng, Wenrui Ma, Yuxin Su, Li Min, Juan Liu   +5 more
wiley   +1 more source

A DQN-based Multi-Objective Participant Selection for Efficient Federated Learning

, 2023
Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang, Peng Liu   +4 more
openalex   +2 more sources