ST-based Deep Learning Analysis of COVID-19 Patients

Hounaida, Frikha; Fokapu, Odette; Chrifi‐Alaoui, Larbi; Amel, Meddeb Makhlouf; Zarai, Faouzi

doi:10.46300/91011.2022.16.39

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluate our models (DT and classification) using ML techniques. The evaluation of the prediction models involves calculating true positive (TP), false positive (FP), true negative (TN), false negative (FN), precision, and accuracy [33], [34] where: TP: represents the accurate identification of data. It indicates that the predicted values match the actual values.…”

Section: Resultsmentioning

confidence: 99%

Automating cloud virtual machines allocation via machine learning

Kamoun-Abid,

Frikha,

Meddeb-Makhoulf

et al. 2024

IJEECS

View full text Add to dashboard Cite

In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.

show abstract

Section: Resultsmentioning

confidence: 99%

Automating cloud virtual machines allocation via machine learning

Kamoun-Abid,

Frikha,

Meddeb-Makhoulf

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…They used four artificial intelligence algorithms to train and classify the data, which are Random Forest, CNN-LSTM, ANN, and Xgboost. The testing and evaluation process showed that better accuracy is getting by applying CNN-LSTM and Xgboost with a classification accuracy of 87% and 88.7% respectively [15].…”

Section: Related Workmentioning

confidence: 98%

Employing Transfer Learning for Diagnosing COVID-19 Disease

et al. 2022

View full text Add to dashboard Cite

Corona virus’s correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models; the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model.

show abstract