This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer ...

This project is designed to predict the GT Turbine decay state coefficient based on operational parameters of a gas turbine. The machine learning model is built using regression techniques and ...

Predictive analytics is a branch of advanced analytics that combines historical data with statistical modeling, data mining techniques, and machine learning. Important ...

Predictive Modeling of Concrete Compressive Strength using Machine Learning Algorithms Abstract: The culture and modern-day society reflect their attributes with their style of architecture. A ...

Our aim from the project is to make use of pandas, matplotlib, & seaborn libraries from python to extract insights from the data and xgboost, & scikit-learn libraries for machine learning. Secondly, ...

Objective Aiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset. Design Retrospective study based on a large sample and a ...

Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China; Objective: To evaluate the efficacy of a machine learning model for predicting prostate-specific antigen (PSA) ...

Machine learning models could analyze live data streams—such as real-time search trends, user behavior on a website and competitor SEO strategies—to make immediate predictions and recommendations.

Keywords: sarcopenia, chronic kidney disease, predictive model, nomogram, machine learning, CHARLS. Citation: Lu R, Wang S, Chen P, Li F, Li P, Chen Q, Li X, Li F, Guo S, Zhang J, Liu D and Hu Z (2025 ...

We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression.