The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...
Scientific Research Publishing

Nguyen, N.T.T., Kenyon, G.T. and Yoon, B. (2020) A Regression Algorithm for Accelerated Lattice QCD That Exploits Sparse Inference on the D-Wave Quantum Annealer. Scientific ...

ABSTRACT: The Matrix Element Method (MEM) is a widely used algorithm in experimental and theoretical high-energy physics (HEP) analyses. The MEM is based on the Lagrangian method to assess the ...
C&EN

Simulation of Core Flooding with Predicted Oil and Water Relative Permeabilities Using Bagging, Boosting, and Stacking Machine Learning Techniques

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Despite global efforts to transition from fossil fuels to ...
Frontiers

Blending physics with data using an efficient Gaussian process regression with soft inequality and monotonicity constraints

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, United States In this work, we propose a new Gaussian process (GP) regression framework that enforces the physical ...
Visual Studio Magazine

Gradient Boosting Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the gradient boosting regression technique, where the goal is to predict a single numeric value. Compared to ...
Scientific Research Publishing

Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 267-288.

ABSTRACT: Continuous groundwater quality monitoring poses significant challenges affecting the environment and public health. Groundwater in Abidjan, specifically from the Continental Terminal (CT), ...
IEEE

Comparison of Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy

Abstract: Comparison of the innovative Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy is the goal of this study, which was designed to ...
IEEE

Classification of Fraud Websites using Linear regression Algorithm and Recurrent Neural Network Algorithm with Improved Accuracy

Abstract: The purpose of this work is to improve the detection of fraud websites using Novel Linear Regression Algorithm and Recurrent Neural Network Algorithm. Materials and Methods: Novel Linear ...