Results 101 to 110 of about 150,441 (331)

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm

PLoS ONE
Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable ...
Wenguang Li, Yan Peng, Ke Peng
semanticscholar   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

Applied Sciences
The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions.
Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay   +2 more
doaj   +1 more source

A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

Plants
The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato
O. M'hamdi, S. Takács, G. Palotás, R. Ilahy, L. Helyes, Z. Pék   +5 more
semanticscholar   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 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

Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource

Psychology Research and Behavior Management
Jingsong Luo,1,2 Yuxin Chen,2 Yanmin Tao,1 Yaxin Xu,3 Kexin Yu,2 Ranran Liu,2 Yuchen Jiang,2 Cichong Cai,2 Yiyang Mao,2 Jingyi Li,2 Ziyi Yang,2 Tingting Deng1 1School of Nursing, The Chengdu University of Traditional Chinese Medicine, Sichuan, 610000 ...
Luo J, Chen Y, Tao Y, Xu Y, Yu K, Liu R, Jiang Y, Cai C, Mao Y, Li J, Yang Z, Deng T   +11 more
doaj  

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

Scientific Reports
In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic.
Zhen Chen, Zhenwan Li, Jia Huang, Shengzheng Liu, Haixia Long   +4 more
semanticscholar   +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

XGBoost: Scalable GPU Accelerated Learning

, 2018
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GPU systems with all of the features of the XGBoost library. We employ data compression techniques to minimise the usage of scarce GPU memory while still allowing highly ...
Mitchell, Rory, Adinets, Andrey, Rao, Thejaswi, Frank, Eibe   +3 more
openaire   +2 more sources