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

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 paper.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 parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,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)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.

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

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 life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Design/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 life.Nowadays,mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer.Though,due to the intricate formation of mammogram images,it is reasonably hard for practitioners to spot breast cancer features.Findings-The performance analysis was done for both segmentation and classification.From 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 classifiers.Additionally,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 classifier.Hence,it is concluded that the implemented breast cancer detection model was efficient in determining the normal,benign and malignant images.Originality/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.