首页|New Machine Learning Findings from School of Resources & Safety Engineering Discussed (State-of-the-art Review of Machine Learning and Optimization Algorithms Applications In Environmental Effects of Blasting)
New Machine Learning Findings from School of Resources & Safety Engineering Discussed (State-of-the-art Review of Machine Learning and Optimization Algorithms Applications In Environmental Effects of Blasting)
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NETL
NSTL
Springer Nature
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "The technological difficulties related with blasting operations have become increasingly significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the threats posed by environmental effect of blasting (EEB)." Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Distinguished Youth Science Foundation of Hunan Province of China.
ChangshaPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningOptimization AlgorithmsSchool of Resources & Safety Engineering
NETL
NSTL
Springer Nature
