Scientific Research Publishing

Proposal for a Machine Learning Model to Improve Hauling Productivity in an Open-Pit Mine ()

This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within open-pit mining. Since hauling accounts for up to 60% of total operational costs, ...
Analytics Insight

10 Must-Know Machine Learning Algorithms for Data Science in 2026

Machine learning algorithms help computers analyse large datasets and make accurate predictions automatically.Classic models like regression, dec ...
Frontiers

Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features

Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of ...
Scientific Research Publishing

Brereton, R.G. and Lloyd, G.R. (2010) Support Vector Machines for Classification and Regression. The Analyst, 135, 230-267.

ABSTRACT: Support vector regression (SVR) and computational fluid dynamics (CFD) techniques are applied to predict the performance of an automotive torque converter in the design process of turbine ...
IEEE

Support Vector Machine Regression Optimised by Genetic Algorithms for Calculating the Ageing Population

Abstract: The aging problem has an increasing impact on China's economy, employment, and medical care, so it is important to make accurate predictions of the aging population. In this paper, we use ...
Frontiers

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f.