Results 121 to 130 of about 983,800 (290)

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

SciBERT Optimisation for Named Entity Recognition on NCBI Disease Corpus with Hyperparameter Tuning

Journal of Applied Informatics and Computing
Named Entity Recognition (NER) in the biomedical domain faces complex challenges due to the variety of medical terms and their context of use. Transformer-based models, such as SciBERT, have proven to be effective in natural language processing (NLP ...
Abu Salam, Syaiful Rizal Sidiq
doaj   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Hyperparameter Optimization and Feature Selection Analysis on the XGBoost Model for Hepatitis C Infection Prediction

Journal of Applied Informatics and Computing
Hepatitis C is a liver disease that can progress to chronic conditions such as cirrhosis and liver cancer. Early detection is essential and can be supported through machine learning approaches.
Nadia Martha Lefi, Majid Rahardi
doaj   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Hate speech detection using pretrained DeBERTaV3 optimized with Grey Wolf Optimizer: a hybrid approach for social media content moderation

Scientific Reports
The proliferation of hate speech on social media poses a significant challenge to maintaining safe and inclusive online environments, necessitating accurate and scalable automated detection systems.
Manas Kamal Das, Basil Skaria, Argha Auddy, M. Vidhyalakshmi   +3 more
doaj   +1 more source

Data driven state of charge estimation for lithium ion batteries: Evaluating the influence of averaged input features using machine learning

Next Materials
For electric car batteries to operate safely and dependably, a highly accurate State of Charge (SOC) is essential. While machine learning (ML) techniques have demonstrated superior performance over traditional methods, their effectiveness heavily depends
Mohamed Abdul Basith Mydeen Pitchai
doaj   +1 more source

A Versatile‐Designable Framework for Active and Programmable Shape‐Morphing Soft Matter Systems: From Inverse Design to Closed‐Loop Control

Advanced Science, EarlyView.
A versatile framework integrates addressable electrothermal actuation and strain‐constraint mechanisms to construct programmable shape‐morphing soft matter systems. By combining an analytical inverse design strategy for high‐fidelity 3D surface reconstruction with deep learning‐based closed‐loop control, this approach enables zero‐energy shape locking,
Kai Liu, Peiling Xie, Ruitong Song, Banghan Liu, Rui Guo, Jiu‐an Lv   +5 more
wiley   +1 more source

Congruent Learning for Self-Regulated Federated Learning in 6G

IEEE Transactions on Machine Learning in Communications and Networking
Future 6G networks are expected to be AI-native with distributed machine learning functionalities responsible for improving and automating a variety of network- and service-management tasks. To enable a privacy-preserving approach to distributed learning,
Jalil Taghia, Farnaz Moradi, Hannes Larsson, Xiaoyu Lan, Adam Orucu, Masoumeh Ebrahimi, Andreas Johnsson   +6 more
doaj   +1 more source

DDSurfer: A Weakly‐Supervised Dual‐Stream Deep Learning Framework for Cortical Surface Reconstruction From Diffusion MRI

Advanced Science, EarlyView.
DDSurfer reconstructs cortical surfaces directly from diffusion MRI without requiring T1‐weighted scans. By fusing complementary microstructural features and learning diffeomorphic deformations, it efficiently generates accurate white matter and pial surfaces, improving geometric fidelity and morphometric reliability across datasets for robust surface ...
Chengjin Li, Wei Zhang, Xi Zhu, Yuqian Chen, Nir A. Sochen, Jarrett Rushmore, Carl‐Fredrik Westin, Yogesh Rathi, Lauren J. O'Donnell, Ofer Pasternak, Fan Zhang   +10 more
wiley   +1 more source