Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

Wang, Zihao; Cai, Yidong; Liu, Dameng; Qiu, Feng; Sun, Fengrui; Zhou, Yingfang

doi:10.1016/j.coal.2023.104208

Cited by 15 publications

(5 citation statements)

References 63 publications

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“…Furthermore, Wang et al compared the performance of multiple linear regression (MLR), RF, and deep fully connected neural network (DNN) in predicting coal structures. 30 In tests conducted with 300 and 1200 sample groups, RF demonstrated the highest accuracy of 83% and 86%, respectively, highlighting its advantages in precision and noise resistance.…”

Section: Introductionmentioning

confidence: 93%

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Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

Hu,

Ding,

Dong

et al. 2024

RSC Adv.

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Section: Introductionmentioning

confidence: 93%

“…And 17 actual samples (no. [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] provided by Shandong Zhigu Carbon Research Institute Co., Ltd.…”

Section: Sample Collection and Preparationmentioning

confidence: 99%

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

Hu,

Ding,

Dong

et al. 2024

RSC Adv.

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show abstract

“…Compared with the decision tree, the RF employs a different strategy for feature selection during each split process. For the classification problem, RF predicts classes based on majority votes [54]. Additionally, RF can assess the importance of each feature and evaluate their role in the classification by providing an importance score [55].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.

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“…In [25], LR and a survival model were used in Russian exports. In [26], an intelligent categorization of coal structure was presented, using multinomial LR. In [27], LR was effectively used in nursing.…”

Section: Introductionmentioning

confidence: 99%

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

Alshutayri

2023

Eng. Technol. Appl. Sci. Res.

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This paper highlights how the proliferation of online transactions, especially those involving the use of credit cards, has resulted in the emergence of new security flaws that pose threats to customers and enterprises worldwide. E-commerce and other forms of online monetary transactions have become essential in the manufacturing and service sectors, propelling the global economy. The widespread and dependent connectivity of mobile payment systems using credit card transactions presents chances for fraud, risk, and security breaches. In light of the importance of accurately predicting fraud incidents through payment procedures, this study investigated the credit card payment methods used for movie tickets, using the machine learning logistic regression method to analyze and predict such incidents. This study used a dataset from cinema ticket credit card transactions made in two days of September 2013 by European cardholders, including 284,807 transactions out of which 492 were fraudulent purchases. The results of the proposed method showed a prediction accuracy of 99%, proving its high prediction performance.

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Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

Cited by 15 publications

References 63 publications

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

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