Heart disease data based privacy preservation using enhanced ElGamal and ResNet classifier
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.
About: This article is published in Biomedical Signal Processing and Control.The article was published on 2022-01-01. It has received 5 citations till now. The article focuses on the topics: Computer science & ElGamal encryption.
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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
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
1 citations
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
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
References
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TL;DR: A comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented in this paper, where the challenges and potential of these techniques are also highlighted.
Abstract: The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
570 citations
06 Apr 2017
TL;DR: This work has done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning and has reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same.
Abstract: The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process But Convolutional Neural Networks (CNN) have provided an alternative for automatically learning the domain specific features Now every problem in the broader domain of computer vision is re-examined from the perspective of this new methodology Therefore it is essential to figure-out the type of network specific to a problem In this work, we have done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning With AlexNet as the base CNN model, we have reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same We hope this piece of article will really serve as a guide for any neophyte in the area
364 citations
TL;DR: A new TCNN with the depth of 51 convolutional layers is proposed for fault diagnosis of ResNet-50 and achieves state-of-the-art results, which demonstrates that TCNN(ResNet- 50) outperforms other DL models and traditional methods.
Abstract: With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.
319 citations
TL;DR: The analysis of the performance of popular convolutional neural networks for identifying objects in real time video feeds shows that GoogLeNet and ResNet50 are able to recognize objects with better precision compared to Alex Net.
319 citations