首页|Findings from Federation University Australia in the Area of Support Vector Mach ines Reported (Impact of Velocity of Detonation and Charge Per Bank Cubic Meters On Flyrock Throw Prediction Using Support Vector Machine)

Findings from Federation University Australia in the Area of Support Vector Mach ines Reported (Impact of Velocity of Detonation and Charge Per Bank Cubic Meters On Flyrock Throw Prediction Using Support Vector Machine)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Support Vecto r Machines have been published. According to news originating from Ballarat, Aus tralia, by NewsRx correspondents, research stated, "One of the ambient effects o f production blasting is flyrock. To effectively manage flyrock throw distance i n mining, there is the necessity to successfully envisage blasting output withou t sacrificing the hazardous impact of flyrock which may result in fatality and o perational shutdown." Financial supporters for this research include Federation University Australia, University of Mines and Technology (UMaT), Ghana National Petroleum Corporation, GNPC Professorial Chair of the Department of Mining at the University of Mines and Technology (UMaT), Gold Fields Ghana Limited. Our news journalists obtained a quote from the research from Federation Universi ty Australia, "For flyrock throw distance prediction, velocity of detonation (VO D) and charge per bank cubic meter (CPBCM) are not usually included. This paper focuses on the use of support vector machine (SVM) regression to ascertain the i mpact of VOD and CPBCM on flyrock throw predictions. The machine learning models were linear support vector machine (LSVM), quadratic Gaussian support vector ma chine (QGSVM), fine Gaussian support vector machine (FGSVM), medium Gaussian sup port vector machine (MGSVM), and cubic Gaussian support vector machine (CGSVM). The outcome indicates that FGSVM was the most sensitive with a 4% improvement when VOD and CPBCM were included."

BallaratAustraliaAustralia and New Z ealandEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesFederation University Australia

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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年,卷(期):2024.(Mar.8)