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A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

Acta Geologica Sinica(English Edition) 2024年第2期98卷 518-530页

作者： DENG Song PAN Haoyu LI Chaowei YAN Xiaopeng WANG Jiangshuai SHI Lin PEI Chunyu CAI Meng College of Petroleum Engineering Changzhou UniversityChangzhouJiangsu 213000China

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this *** method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling *** the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five ***,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)*** addition,this study combines the recognition advantages of the four *** accuracy of lithology identification by the weighted average probability model reaches *** study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

关键词： mud logging data real-time lithological identification improved crow search algorithm petroleum geological exploration SMOTE-Tomek

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improved region growing segmentation for breast cancer detection:progression of optimized fuzzy classifier

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International Journal of Intelligent Computing and Cybernetics 2020年第2期13卷 181-205页

作者： Rajeshwari S.Patil Nagashettappa Biradar Department of Electronics and Communication BLDEA’s V P Dr PG Halakatti College of Engineering and TechnologyBijapurIndia Department of Electronics and Communication Bheemanna Khandre Institute of TechnologyBidarIndia

Purpose-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to ***,mammography is considered as a fundamental criterion for medical practitioners to recognize breast ***,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer ***/methodology/approach-Breast cancer is one of the most common malignant tumors in women,which badly have an effect on women’s physical and psychological health and even danger to ***,mammography is considered as a fundamental criterion for medical practitioners to recognize breast ***,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer ***-The performance analysis was done for both segmentation and *** the analysis,the accuracy of the proposed IAP-CSA-based fuzzy was 41.9%improved than the fuzzy classifier,2.80%improved than PSO,WOA,and CSA,and 2.32%improved than GWO-based fuzzy ***,the accuracy of the developed IAP-CSA-fuzzy was 9.54%better than NN,35.8%better than SVM,and 41.9%better than the existing fuzzy ***,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant ***/value-This paper adopts the latest improved Awareness Probability-based crow search algorithm(IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images,and this is the first work that utilizes this method.

关键词： Mammogram Breast cancer detection Optimized region growing Membership optimized-fuzzy classifier improved crow search algorithm

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