Pawalai Kraipeerapun

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Qnantification of unscrtalnty in mineral prospee tivity predictinn is an important prncess tn support decisinfi making In mineral exploration, D e p oF uncertainty can identify lcvel of quality in thc prcdiction. This papcr proposes an approach to predict d w e s of hvourability for gold deposits together with quantification o f uncertainty in the(More)
— This paper describes the integration of neural network ensembles and interval neutrosophic sets using bagging technique for predicting regional-scale potential for mineral deposits as well as quantifying uncertainty in the predictions. Uncertainty in the types of error and vagueness are considered in this paper. Each component in the ensemble consists of(More)
This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique. Each component in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and(More)
— This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of(More)