Results 231 to 240 of about 87,026 (282)

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

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

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
wiley   +1 more source

A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

Advanced Intelligent Systems, EarlyView.
This study introduces the first inverse machine learning model to predict laser powder bed fusion process parameters for targeted surface roughness of Inconel 718 parts. Unlike prior approaches, it incorporates spatial surface characteristics for improved accuracy.
Samsul Mahmood, Bart Raeymaekers
wiley   +1 more source

Neonatal Respiration Monitoring System with Synchronized Oxygen Supply and Machine Learning‐Based Breathing Classification

Advanced Intelligent Systems, EarlyView.
The study presents a low‐cost, noninvasive system for real‐time neonatal respiratory monitoring. A flexible, screen‐printed sensor patch captures chest movements with high sensitivity and minimal drift. Combined with machine learning, the system accurately detects breathing patterns and offers a practical solution for neonatal care in low‐resource ...
Gitansh Verma, Eugen Koch, Andreas Dietzel, Shrutidhara Sarma   +3 more
wiley   +1 more source

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Advanced Intelligent Systems, EarlyView.
Hydrothermal synthesis records for rare‐earth compounds are repurposed to learn mineralization rules. An extreme gradient boosting model ingests precursors, additives, and engineered descriptors to predict product phases, crystallization temperature, and pH. Feature importance indicates dominant thermodynamic control with kinetic modulation, suggesting
Juejing Liu, Xiaoxu Li, Yifu Feng, Zheming Wang, Kevin M. Rosso, Xiaofeng Guo, Xin Zhang   +6 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Extreme Gradient Boosting for Cyberpropaganda Detection

2021
Propaganda, defamation, abuse, insults, disinformation and fake news are not new phenomena and have been around for several decades. However, with the advent of the Internet and social networks, their magnitude has increased and the damage caused to individuals and corporate entities is becoming increasingly greater, even irreparable. In this paper, we
Fattahi, Jaouhar, Mejri, Mohamed, Ziadia, Marwa   +2 more
openaire   +1 more source

Self-trained eXtreme Gradient Boosting Trees

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019
Semi-Supervised Learning (SSL) is an ever-growing research area offering a powerful set of methods, either single or multi-view, for exploiting both labeled and unlabeled instances in the most effective manner. Self-training is a representative SSL algorithm which has been efficiently implemented for solving several classification problems in a wide ...
Nikos Fazakis, Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos Sgarbas   +4 more
openaire   +1 more source

Stock Selection Based on Extreme Gradient Boosting

2019 Chinese Control Conference (CCC), 2019
In this paper, we established a multi-factor stock selection model based on Extreme Gradient Boosting (XGBoost) to beat the benchmark. We used accounting indicators, valuation indicators, emotions and technical indicators, 62 in total, as the feature space of the XGBoost classifier, and attempted to identify stocks from CSI 300 Index that are likely to
Xiaoyun Zhang, Wanyi Chen
openaire   +1 more source