Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Elmisery, Ahmed M.; Sertovic, Mirela

doi:10.4018/ijghpc.2017010107

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The merit of applying this model is its effectiveness and it can be accessed easily by every individual. Numerous applications of AI (Elmisery et al 2015;Elmisery and Sertovic 2017) have been deployed earlier to control COVID-19 infection.…”

Section: Introductionmentioning

confidence: 99%

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Shankar

Mohanty²,

Yadav

et al. 2021

Cogn Neurodyn

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was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

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Section: Introductionmentioning

confidence: 99%

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Shankar

Mohanty²,

Yadav

et al. 2021

Cogn Neurodyn

Self Cite

View full text Add to dashboard Cite

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Implicit authentication protocol and self-healing key management for WBANs

Shen

Chang

Liu

et al. 2018

Multimed Tools Appl

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Towards an Intelligent Framework for Cloud Service Discovery

Ali

Shamsuddin

Eassa

et al. 2021

International Journal of Cloud Applications and Computing

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The variety of cloud services (CSs) that are described, their non-uniform naming conventions, and their heterogeneous types and features make cloud service discovery a difficult problem. Therefore, an intelligent cloud service discovery framework (CSDF) is needed for discovering the appropriate services that meet the user's requirements. This study proposes a CSDF for extracting cloud service attributes (CSAs) based on classification, ontology, and agents. Multiple-phase classification with topic modeling has been implemented using different machine learning techniques to increase the efficiency of CSA extraction. CSAs that are represented in different formats have been extracted and represented in a comprehensive ontology to enhance the efficiency and effectiveness of the framework. The experimental results showed that the multiple-phase classification methods with topic modeling for CSs using a support vector machine (SVM) obtained a high accuracy (87.90%) compared to other methods. In addition, the results of extracting CSAs showed high values for precision, recall, and f-measure of 99.24%, 99.24%, and 99.24%, respectively, for Java script object notation(JSON) format, followed by 99.05%, 97.20%, and 98.11% for table formats, and with lower accuracy for text format (90.63%, 86.57%, and 88.55%).

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Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Cited by 3 publications

References 35 publications

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Implicit authentication protocol and self-healing key management for WBANs

Towards an Intelligent Framework for Cloud Service Discovery

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