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Author

V. Benhar Charles

Bio: V. Benhar Charles is an academic researcher. The author has contributed to research in topics: Computer science & ElGamal encryption. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: This study focused on an enhanced ElGamal encryption-decryption method for the encryption of data with a generated private key and a public key for decryption to better access the data.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , an exhaustive analysis of numerical, categorical and combination of both types of features have been done in context of state-of-the-art machine learning algorithms.

10 citations

Book ChapterDOI
Minglu Chen1
06 Nov 2022
TL;DR: Wang et al. as discussed by the authors used lion optimization-based feature selection (LOFS) method and three ML-based classifiers, namely LOFS-ANN, SVM and DT, to predict heart disease.
Abstract: Heart disease refers to the condition when the heart is not capable to push required amount of blood to the entire body. Heart disease (HD) is the prevailing reason behind deaths among the world-wide population. Early prediction of heart diseases can save lives. Predicting cardiovascular or heart disease in advance, a person can be warned beforehand, and the death can be prevented in turn. Machine learning (ML) has made a huge contribution to classify the population with heart disease from the healthy population. This paper proposes three heart disease prediction (HDP) models namely LOFS-ANN, LOFS-SVM, and LOFS-DT utilizing lion optimization-based feature selection (LOFS) method and three ML-based classifiers. The datasets used are from UCI repository. The comparative analysis reflects that the model LOFS-ANN performs best among all three models, with the values of 97.1% and 90.5% for AUC measure and accuracy measure, respectively. It can be concluded that the LOFS-ANN has a significant potential to predict heart disease after drawing its statistical comparison with the competing models.

4 citations

Journal ArticleDOI
TL;DR: In this paper , two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyze data from a benchmark database obtained from the UCI repository.
Abstract: Abstract Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system.

1 citations

Journal ArticleDOI
TL;DR: In this article , an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via Magnetic Fluid Leakage (MFL) signals.
Abstract: Girth weld defects in long-distance oil and gas pipelines are one of the main causes of pipeline leakage failure and serious accidents. Magnetic flux leakage (MFL) is one of the most widely used inline inspection methods for long-distance pipelines. However, it is impossible to determine the type of girth weld defect via traditional manual analysis due to the complexity of the MFL signal. Therefore, an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via MFL signals. Firstly, the image data set of girth welds MFL signal was established with the radiographic testing results as labels. Then, the deep convolutional adversarial generative network (DCGAN) data enhancement algorithm was proposed to enhance the data set, and the residual network (ResNet-50) was proposed to address the challenge presented by the automatic classification of the image sets. The data set after data enhancement was randomly selected to train and test the improved residual network (ResNet-50), with the ten validation results exhibiting an accuracy of over 80%. The results indicated that the improved network model displayed a strong generalization ability and robustness and could achieve a more accurate MFL image classification of the pipeline girth welds.

1 citations