Results 161 to 170 of about 150,441 (331)

Engineering Biochar‐Derived Functional Materials for High‐Performance Supercapacitors: Design Principles, Mechanisms, and Scalable Strategies

Carbon Energy, EarlyView.
ABSTRACT Biochar has emerged as a useful and adaptable source of carbon for supercapacitor electrodes. Its value comes from the way biomass chemistry, thermal conversion, and activation conditions shape the resulting pore network, surface groups, and degree of carbon ordering.
Soumen Mandal, Avinash C. Mendhe, Mayna Roy, Neha S. Barse, Taejoon Park, Han‐Seung Lee, Haigun Lee   +6 more
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models

Journal of Asthma and Allergy
Joon Young Choi,1 Chin Kook Rhee2 1Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; 2Department of Internal Medicine, Seoul St.
Choi JY, Rhee CK
doaj  

Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?

, 2016
Tree boosting has empirically proven to be a highly effective approach to predictive modeling. It has shown remarkable results for a vast array of problems. For many years, MART has been the tree boosting method of choice. More recently, a tree boosting method known as XGBoost has gained popularity by winning numerous machine learning competitions.
openaire   +1 more source

Phishing URL Detection Using XGBoost

International Journal for Research in Applied Science and Engineering Technology
Abstract: Phishing attacks are a major threat to cybersecurity, affecting individuals and organizations around the world. In this project we are developing a phishing site detection system using XGBoost, a widely used machine learning algorithm that is well-known for its effectiveness and precision in classification tasks.
openaire   +1 more source

Clinical Model‐Informed Precision Dosing Consult Service for Accelerating Personalized Medication in Pediatric Patients

Clinical Pharmacology &Therapeutics, EarlyView.
Traditional dosing strategies often rely on a “one‐size‐fits‐all” paradigm, assuming an “average” patient with typical demographic and pharmacological characteristics. In reality, this often overlooks existing between‐patient variability and can lead to suboptimal drug exposure or toxicity. This issue is especially pronounced in pediatric patients, who
Zachary L. Taylor, Takanobu Nadai, Kei Irie, Wen Rui Tan, Sanjana Parikh, Anhar Hosawi, Matthijs W. van Hoogdalem, Ronaldo Morales Junior, Sonya Tang Girdwood, Abbie D. Leino, Kana Mizuno, Alexander A. Vinks, Tomoyuki Mizuno   +12 more
wiley   +1 more source

Drug Response Prediction Using XGBOOST

International Journal for Research in Applied Science and Engineering Technology
Abstract: An essential issue in computational personalised medicine is the prediction of drug responses. There have been several proposals for approaches to this problem that rely on machine learning, particularly deep learning. Nevertheless, these approaches often portray the medications as strings, an implausible representation of molecules ...
openaire   +1 more source

Bridging Theory and Prediction: A Hybrid SEM and Machine Learning Approach to Optimize Lean Construction for Megaproject Sustainability in China

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Construction megaprojects, large‐scale, complex, and capital‐intensive, are particularly prone to inefficiencies, cost overruns, delays, and environmental degradation due to fragmented workflows, stakeholder misalignment, and resource intensity.
Abdelazim Ibrahim, Tarek Zayed, Zoubeir Lafhaj, Ahmed Maged, Ahmed Farouk Kineber, Jingchao Yang   +5 more
wiley   +1 more source

Development and validation of a machine learning-based early warning model for carbapenem-resistant <i>Klebsiella pneumoniae</i> bloodstream infections using non-carbapenem susceptibility profiles. [PDF]

Front Microbiol
Song J, Bai Y, He S, Ji J, Chen Y, Xiao Y.   +5 more
europepmc   +1 more source