A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting

Dai, Yong; Khandelwal, Manoj; Qiu, Yingui; Zhou, Jian; Monjezi, Masoud; Yang, Peixi

doi:10.1007/s00521-021-06776-z

Cited by 55 publications

(19 citation statements)

References 76 publications

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“…A powerful bagging-based ensemble algorithm is the distributed random forest (DRF), which improves learning by addressing the problem of local optima and covering the full search space [ 48 ]. It uses a combination of decision trees to maximize the model classification efficiency rather than using just one as a weak learner.…”

Section: Methodsmentioning

confidence: 99%

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

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Section: Methodsmentioning

confidence: 99%

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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“…Each tree is made up of column variables and row observations that are chosen at random. A single DT is difficult to predict accurately, but all DTs form a forest, allowing the aggregated findings to incorporate the outcomes of all DTs, resulting in a more accurate overall prediction [59].…”

Section: Random Forest 351 Definitionmentioning

confidence: 99%

Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

et al. 2022

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The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.

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“…Dai Yong et al (2022) modeled BB based on the parameters of hole length (L), spacing (B, S), stemming (T), specific drilling (SD), specific charge (PF), and an output parameter (BB) using random forest (RF) and particle optimization swarm (PSO) algorithms. The results showed that parameters L and T have the highest Gini index in the RF model, indicating their effect on the output of the model (Dai et al, 2022). In 2021 and 2022, Sohrabi et al used Whale algorithm and neural network to optimize crude oil and coal price estimation.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Sorabi

Ataei

Jazi

et al. 2023

Preprint

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One of the adverse consequences of the blasting in the mineral extraction process in mines is back-break (BB) so that development of many fractures and cracks at large distances behind the last row of blast pits reduces the safety of the benches and increases operating costs. Since various parameters affect the BB, various techniques have been developed to predict and optimize its values. In this study, 48 blasts were investigated in Gol Gohar Mine No. 1 in the tailings section of the mine to predict BB based on the Whale Optimization Algorithm (WOA), Multiverse Optimizer (MVO), Sine Cosine Algorithm (SCA), and Ant Lion Optimizer (ALO). The parameters of bench height, hole length, burden, spacing, specific charge, the number of blasting rows, hole diameter, stemming, uniaxial compressive strength, joint spacing, and geological strength index (GSI) were evaluated as inputs to the models to predict back-breaks in the blasts. The comparison of the results of four BB prediction models suggested that the MVO-based model with a coefficient of determination (R2) of 0.9802, root-mean-square error (RMSE) of 0.2161, and mean squared error (MSE) of 0.1127 had the highest accuracy and the lowest error. So, it was introduced as the most appropriate model for predicting BB.

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A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting

Cited by 55 publications

References 76 publications

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

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