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Outlier detection in neutrosophic sets by using rough entropy based weighted density method

CAAI Transactions on Intelligence Technology 2020年第2期5卷 121-127页

作者： Tamilarasu Sangeetha geetha mary amalanathan SCOPE Vellore Institute of TechnologyVelloreIndia

Neutrosophy is the study of neutralities,which is an extension of discussing the truth of *** logic can be applied to any field,to provide the solution for indeterminacy *** of the real-world data have a problem of inconsistency,indeterminacy and *** sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate *** handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy *** sets provide a solution for both incomplete and indeterminate *** has mainly three degrees of membership such as truth,indeterminacy and *** values are obtained from the three degrees of membership by cut relation *** items which contrast from other objects by their qualities are *** weighted density outlier detection method based on rough entropy calculates weights of each object and *** the obtained weighted values,the threshold value is fixed to determine *** analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.

关键词： method. entropy incomplete

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FG-SMOTE:Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 269-286页

作者： Putta Hemalatha geetha mary amalanathan SCOPE Vellore Institute of TechnologyVelloreIndia

Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding *** data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a *** issue is known as the imbalance problem,which is one of the most common issues occurring in real-time *** of imbalanced datasets is a ubiquitous challenge in the field of data *** data degrades the performance of the classifier by producing inaccurate ***/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced *** mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class *** ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure ***-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion *** limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed ***/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced ***-SMOTE has aided in balancing minority and majority class datasets.

关键词： Imbalanced data Gaussian SMOTE Levenshtein distance measure technique Skewed class distribution Fuzzy based Gaussian SMOTE Deep learning Deep belief network classifie

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Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model

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Computer Systems Science & Engineering 2022年第12期43卷 1041-1055页

作者： A.geetha N.B.Prakash National Engineering College Kovilpatti628502India

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and *** is an incurable and unavoidable eye disease that damages the vision ofoptic nerves and quality of life. Classification of Glaucoma has been an active fieldof research for the past ten years. Several approaches for Glaucoma classification areestablished, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). Incontrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasetsdetermines the performance of the CNN;the model trained with small datasets,overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current workcompares with other models, namely VGG16, InceptionV3, and Xception usingpublic datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, andACRIMA. The dataset has split into training, validation, and testing with the ratioof 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listedabove. The proposed method achieved 99.38% accuracy and also better results forother metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, andArea Under Curve (AUC) compared to other models.

关键词： Convolution neural network deep learning fundus image glaucoma image classification

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