Results 101 to 110 of about 33,486 (254)

Real-Time Jaundice Detection in Neonates Based on Machine Learning Models

BioMedInformatics
Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models ...
Ahmad Yaseen Abdulrazzak, Saleem Latif Mohammed, Ali Al-Naji, Javaan Chahl   +3 more
doaj   +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

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

APPICATION OF XGBOOST ALGORITHM IN HOUSING ASSET VALUATION

World Journal of Information Technology
Machine learning models supported by big data have been practiced and applied in many ways in recent years, and as a representative technology of artificial intelligence, machine learning models have been proved to be able to perform well in many predictive problems such as economics and management.
BoHong Wang, YiXuan Guo, ChaoLin Hou, ZhiLing Zhang   +3 more
openaire   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients

Scientific Reports
Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses.
Yuan Liu, Yuankun Liu, Yu Zhang, Pengpeng Zhang, Jiaheng Xie, Ning Zhao, Yi Xie, Chao Cheng, Songyun Zhao   +8 more
doaj   +1 more source

Survival Prediction Model for Patients with Hepatocellular Carcinoma and Extrahepatic Metastasis Based on XGBoost Algorithm. [PDF]

J Hepatocell Carcinoma, 2023
Lim J, Jeon HG, Seo Y, Kim M, Moon JU, Cho SH.   +5 more
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Retracted: Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital. [PDF]

J Healthc Eng, 2023
Healthcare Engineering JO.
europepmc   +1 more source