Results 61 to 70 of about 101,601 (232)

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

برآورد محاسباتی واحدهای رنگی L*a*b* از RGB با کمک پردازش تصاویر دیجیتالی [PDF]

مجله پژوهش‌های علوم و صنایع غذایی ایران, 2017
رنگ اولین ویژگی کیفیت مواد غذایی است که توسط مصرف کنندگان مورد بررسی قرار می گیرد. اندازه گیری رنگ مواد غذایی به‌عنوان یک شاخص غیرمستقیم در اندازه گیری دیگر ویژگی های کیفیتی مانند عطر و طعم و محتویات رنگدانه به دلیل سرعت و سادگی در اندازه گیری، و همچنین ...
سامان آبدانان مهدی زاده, سمیه امرایی   +1 more
doaj   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Forecasting of Rice Harvest Results Using SVR Modeling Techniques

Jambura Journal of Mathematics
Forecasting is an activity that predicts future values {}{}by utilizing existing track record data. The object of this study is rice plants because they are the primary food source for the Indonesian people.
Devie Rosa Anamisa, Bain Khusnul Khotimah, Mohammad Yanuar Hariyawan, Firli Irhamni, Achmad Jauhari, Fifin Ayu Mufarroha, Dina Violina, Dinah Nuraini   +7 more
doaj   +1 more source

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Axioms, 2019
Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors.
Chao Shi, Xiaosheng Zhuang
doaj   +1 more source

Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data. [PDF]

, 2018
PurposeThe accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and ...
Landers, Angelia, Neph, Ryan, Ruan, Dan, Scalzo, Fabien, Sheng, Ke   +4 more
core  

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

Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization

Angewandte Chemie, EarlyView.
Staged diversity‐constrained modeling enables efficient navigation of high‐dimensional reaction spaces, validated on cross‐coupling HTE data and applied to ruthenium‐catalyzed meta‐C─H functionalization. ABSTRACT Optimizing reaction conditions in high‐dimensional chemical spaces remains a central challenge in modern synthesis.
Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shuo‐Qing Zhang, Lutz Ackermann, Xin Hong   +5 more
wiley   +2 more sources

A Universal Gauge for Thermal Conductivity of Silicon Nanowires With Different Cross Sectional Geometries

, 2011
By using molecular dynamics simulations, we study thermal conductivity of silicon nanowires (SiNWs) with different cross sectional geometries. It is found that thermal conductivity decreases monotonically with the increase of surface-to-volume ratio (SVR)
Chen, Jie, Li, Baowen, Zhang, Gang
core   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source