Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees

Ahmad, Mahmood; Al-Shayea, N.; Tang, X.; Jamal, Arshad; Al-Ahmadi, Hassan M.; Ahmad, Feezan

doi:10.3390/app10186486

Cited by 35 publications

(25 citation statements)

References 35 publications

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“…Decision trees were successfully used for prediction in medicine [ 39 , 40 , 41 ]. In [ 42 ], decision trees were used to predict the pillar stability of underground mines. In [ 43 ], Mahmood et al proposed a decision tree-based model to assist the allocation of new orders in manufacturing networks.…”

Section: Related Workmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

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Section: Related Workmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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“…To classify, from the root hub, the property estimation of the entered records is tested until a leaf hub is achieved for its respective class. The purpose of tree pruning is to decrease the background distortion or noise in the training data [10,52,53]. In this study, random forest (Fig.…”

Section: Decision Treementioning

confidence: 99%

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Mehak¹,

Ashraf²,

Zafar³

et al. 2022

Computers, Materials &Amp; Continua

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Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer histopathology has not received much consideration. Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast, non-invasive breast (usual ductal hyperplasia, atypical ductal hyperplasia) and pre-invasive breast (ductal carcinoma in situ) and invasive breast lesions. We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process. The classification results show the value of the multilayer autoencoder model in the evaluation of nuclear polymorphisms. The proposed method has indicated promising results, making them more fit in breast cancer grading.

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“…where Ac is the accuracy value obtained in Equation ( 5), and P e is the hypothetical probability of chance agreement, defined as Equation (7).…”

Section: Development Of Lmt Models For Pillar Stability Predictionmentioning

confidence: 99%

Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees

Naghadehi²,

et al. 2022

IJERPH

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Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of underground pillar stability is important. This paper presents a novel application of Logistic Model Trees (LMT) to predict underground pillar stability. Seven parameters—pillar width, pillar height, ratio of pillar width to height, uniaxial compressive strength of rock, average pillar stress, underground depth, and Bord width—are employed to construct LMTs for rock and coal pillars. The LogitBoost algorithm is applied to train on two data sets of rock and coal pillar case histories. The two models are validated with (i) 10-fold cross-validation and with (ii) another set of new case histories. Results suggest that the accuracy of the proposed LMT is the highest among other common machine learning methods previously employed in the literature. Moreover, a sensitivity analysis indicates that the average stress, p, and the ratio of pillar width to height, r, are the most influential parameters for the proposed models.

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Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees

Cited by 35 publications

References 35 publications

Assembly Assistance System with Decision Trees and Ensemble Learning

Assembly Assistance System with Decision Trees and Ensemble Learning

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees

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