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Noise level policy advising system for mine workers

机译：矿山工人噪声水平政策咨询系统

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摘要

A noise level policy advising system to be used by mine administrators in assigning tasks to new employees at the mine is proposed. The presented novel system uses machine learning techniques which includes clustering and classification of new employee. The mine workers are clustered using K-means to determine their properties. By comparatively using Logistic regression, support vector machines, decision trees and random forests classification techniques, the mine workers are classified. Depending on the classification, which is based on the mine workers baseline and future threshold shift, recommendations to suitable mining tasks are made. The decision tree is the best performing model with the highest accuracy. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. However, logistic regression provides the best generalisation results on the testing set. Future work would include development of a friendly Graphical User Interface to facilitate easy use of the system.
机译：提出了一种噪音水平政策建议系统，该系统将由矿山管理员在将任务分配给矿山的新员工时使用。提出的新颖系统使用机器学习技术，其中包括新员工的聚类和分类。矿工使用K均值进行聚类以确定其属性。通过比较使用Logistic回归，支持向量机，决策树和随机森林分类技术，对矿山工人进行分类。根据基于矿工基线和未来阈值变化的分类，针对合适的采矿任务提出了建议。决策树是性能最高，精度最高的模型。它的平均测试准确度为91.25％，平均训练准确度为99.79％。但是，逻辑回归可以在测试集上提供最佳的泛化结果。未来的工作将包括开发友好的图形用户界面，以方便系统的使用。

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