## 3 Artificial Intelligence  
AI technologies such as machine learning are attracting attention to effectively analyze the big data produced, collected, and shared in real time in the energy and mineral industries. [PERSON] et al. [3] proposed models of artificial neural networks (ANN) and their ensembles to predict the flight distance of rocks caused by blasting in open-pit mines. The study collected data from 210 fly rock events in open-pit mines and used 80% of the entire dataset to develop the models and the remaining 20% to test the developed models. The performance of each machine learning model was evaluated by calculating the mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), correlation coefficient (R\({}^{2}\)), and variance accounted for (VAF). The results showed that the ensemble of ANN models yielded better performance with an MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R\({}^{2}\) = 0.986, and VAF = 98.446% compared to that of the ANN models with the same structure.  
[PERSON] et al. [4] compared seven machine learning techniques, including random forest (RF), ANN, k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART) to predict ilmenite content in beach placers. Of the 405 beach placer borehole samples, 325 samples were used to build the machine learning models, and 80 remaining samples were used in the models. Based on the evaluation criteria calculated by RMSE, R\({}^{2}\), a simple ranking method, and the residual analysis technique, the study indicated that the SGB model was the most appropriate for determining ilmenite content.  
[PERSON] and [PERSON] [5] presented a deep neural network (DNN)-based method for predicting ore production by truck haulage systems in open-pit mines. DNN models were trained using data obtained from an open-pit limestone mine in South Korea over a two-month period and optimized by varying the number of hidden layers and their corresponding nodes. The results showed that the MAPE predictions for morning and afternoon ore production equaled 11.40% and 8.87%, respectively, and the error between the actual and predicted ore productions on a given day was 4.17%. The DNN-based method could predict ore production by using the data without the help of conventional truck haulage system simulation methods based on complex algorithms.  
[PERSON] et al. [6] compared supervised machine learning models including RF, gradient boosting tree (GBM), and SVM to predict the productivity of shale gas wells using data from 150 shale gas wells in the Eagle Ford shale gas field, USA. The data contain various parameters, such as hydraulic fracturing and well completion. The 129 shale gas wells for data preprocessing were used as training datasets for supervised learning models. Optimal input variables were selected by using the variable importance method (VIM), and cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare training and test results. The RF with the IncNodePurity-based VIM showed the best prediction performance when compared to other supervised learning models.