May 17, 2020 Machine learning approach to handle data-driven model for simulation and forecasting of the cone crusher output in the stone crushing plant Khaled Ali Abuhasel , Corresponding Author
Nov 05, 2021 The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et
Sep 15, 2020 Fig. 4 presents the HHVMs generated through the application of the five advanced machine learning algorithms (ANN, SVM, REPTree, RF and GP) in selected four stone quarrying and crushing clusters of the study area. The HHVMs are classified into five vulnerability classes like very high, high, moderate, low and very low
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The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link
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The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link
Machine learning applications in minerals processing A . Machine learning applications in mineral processing from 2004 to 2018 are reviewed. As machine learning applications in mineral processing will only increase with time, there is a real need to increase the availability of these resources (data, process understanding and technique expertise)
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Nov 22, 2019 The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link
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Apr 19, 2021 A.A. Jimoh and S. S. Awe (2007), A study on the influence of aggregate Size and type on the compressive strength of concrete presented in his research that the effect of crushed stone sand as fine aggregates causes of flexural strength increase than the concrete with natural sand but the values decreases as percentage of crusher dust increases
