A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).

A Model based on Deep Learning for COVID-19 X-rays Classification

Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy. © 2023, Modern Education and Computer Science Press.

Detection of Coronavirus (COVID-19) Associated Pneumonia Based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model Using Chest X-ray Dataset

The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs’ air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Chest X-rays Classification Through the Fusion of Deep Transfer Learning and Machine Learning Methods

One of the most challenging issues that humans face in the last decade is in the health sector, and it is threatening his existence. The COVID-19 is one of those health threats as declared by the World Health Organization (WHO). This spread of COVID-19 forced WHO to declare this virus as a pandemic in 2019. In this paper, COVID-19 chest X-rays classification through the fusion of deep transfer learning and machine learning methods will be presented. The dataset “DLAI3 Hackathon Phase3 COVID-19 CXR Challenge” is used in this research for investigation. The dataset consists of three classes of X-rays images. The classes are COVID-19, Thorax Disease, and No Finding. The proposed model is made up of two main parts. The first part for feature extraction, which is accomplished using three deep transfer learning algorithms: AlexNet, VGG19, and InceptionV3. The second part is the classification using three machine learning methods: K-nearest neighbor, support vector machine, and decision trees. The results of the experiments show that the proposed model using VGG19 as a feature extractor and support vector machine. It reached the highest conceivable testing accuracy with 97.4%. Moreover, the proposed model achieves a superior testing accuracy than VGG19, InceptionV3, and other related works. The obtained results are supported by performance criteria such as precision, recall, and F1 score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Within the Protection of COVID-19 Spreading: A Face Mask Detection Model Based on the Neutrosophic RGB with Deep Transfer Learning

COVID-19’s fast spread in 2020 compelled the World Health Organization (WHO) to declare COVID-19 a worldwide pandemic. According to the WHO, one of the preventative countermeasures against this type of virus is to use face masks in public places. This paper proposes a face mask detection model by extracting features based on the neutrosophic RGB with deep transfer learning. The suggested model is divided into three steps, the first step is the conversion to the neutrosophic RGB domain. This work is considered one of the first trails of applying neutrosophic RGB conversion to image domain, as it was commonly used in the conversion of grayscale images. The second step is the feature extraction using Alexnet, which has been small number of layers. The detection model is created in the third step using two traditional machine learning algorithms: decision trees classifier and Support Vector Machine (SVM). The Simulated Masked Face dataset (SMF) and the Real-World Mask Face dataset (RMF) are merged to a single dataset with two categories (a face with a mask, and a face without a mask). According to the experimental results, the SVM classifier with the True (T) neutrosophic domain achieved the highest testing accuracy with 98.37%. © 2022

The classification of possible Coronavirus treatments on a single human cell using deep learning and machine learning approaches

Every major healthcare system is now under the throes of the Coronavirus disease outbreak as it is operating at its maximum capacity. There is an absolute need to establish an appropriate cure for this virus as quickly and efficiently as possible. Advances in deep learning models may play a critical role in SARS-2 discovery by locating a possible treatment. This article's objective is to demonstrate the machine learning and deep learning models approaches for classifying prospective coronavirus treatment on a single human cell. A partial dataset of RXRX.ai which is a publicly available dataset is used in this research. This work targeted to implement a strategy for automatically identifying a single human cell depending on the type of treatment and its concentration level. Throughout this study, we present a DCNN model along with an image processing approach. The systematic approach comprises translating the original dataset's numerical attributes to the image domain, and then incorporating them into DCNN model. In comparison to standard machine learning techniques including such Ensemble, Decision Tree and Support Vector Machine, the experimental findings indicate that the suggested DCNN model for treatment classification (32 categories) obtained a testing accuracy of 98.05 percent. The (Ensemble) algorithm achieves 98.5 percent for the accuracy test in treatment concentration level prognosis, whereas the suggested DCNN model reached 98.2 percent. The classification of treatments and assessing their concentration levels are considerably accurate due to the performance indicators obtained from the experiments. © 2021 Little Lion Scientific.

Exploring the impacts of covid-19 on oil and electricity industry

The COVID-19 Pandemic has dramatically influenced the global market of energy production and consumption. This influence could be noticed obviously by the latest drop in crude oil prices. Furthermore, coronavirus has affected the supply chains and delayed the development of sustainable energy worldwide. Due to its relevance, academics have begun to study the associations regarding this crisis. The COVID-19 Pandemic offers a new chance to investigate the impacts of prolonged landscape-scale confusion on sustainability change paths in real-time. How the global renewable energy flexibility sector will respond to the COVID-19 Pandemic is a critical question. This crisis could inspire governance structures to plan adequately for other varieties of crises in the future. These improvements can drive research by spouting the generation of new disciplines stemming from the COVID-19 outbreak to expedite sustainability transitions and improve the recognition of governance's main role in changes. Smart policies could transform COVID-19 threats into great opportunities for the world's sustainable energy scenario towards green energy generation and use in the coming years. In this paper, the impacts of COVID-19 in terms of the energy sector, especially the electricity and oil sectors, will be explained. The major objective of this research is to shed light on future research on renewable energy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Tracking of COVID-19 geographical infections on real-time tweets

Coronavirus COVID-19 is a global pandemic stated by the World Health Organization (WHO) in 2020. The COVID-19 devastating impact was not only affect human life but also many aspects of it such as social interaction, transportation options, personal saving and expenses, and more. The power of social media data in such world pandemic outbreaks provides an efficient source of tracking, raising awareness, and alerts with potentials infection location. Social networks can fight the pandemic by sharing helpful content and statistics based on demographics features of users around the world. There is an urgent need for such frameworks for tracking helpful content, detecting misleading content, ranking the trusted user content, presenting accurate demographics statistics of the outbreak. In this paper, the real-time tweets of Coronavirus pandemic (COVID-19) analysis will be presented. The proposed framework will be used to track the geographical infections, trends of the content, and the user’s categorization. The framework will include analysis, demographics features, statistical charts, classifying the content of tweets related to its usefulness. The performance of the proposed framework is evaluated based on different measures such as classification accuracy, sensitivity, and specificity. Finally, a set of recommendations will be presented to benefit from the proposed framework with its full potentials as a tool to stand against the COVID-19 spreading. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The role of emerging technologies for combating COVID-19 pandemic

The outbreak of the new coronavirus disease (COVID-19) in 2019 resulted in more than 100,000 infections and thousands of deaths. The number of deaths and infections continues to rise rapidly since the virus date of appearance. COVID-19 threatens not only human health but also many aspects of life such as manufacturing, social performance, and international relations. Emerging technologies can help in the fight against COVID-19. Emerging technologies include blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data technologies, and they proved its efficiency in practical fields. These fields include the fast aggregation of multi-source big data, fast visualization of epidemic information, diagnosing, remote treatment, and spatial tracking of confirmed cases. Every country in the world is still seeking realistic and cost-effective solutions to stand against COVID-19 under current epidemiological conditions. This chapter discusses the concepts of emerging technologies, applications, and contributions to combating COVID-19. Moreover, the challenges and future research directions are reviewed in detail. Also, a list of publicly available open-source COVID-19 datasets will be presented. Finally, this chapter concludes that cooperation among government, medical institutions, and the scientific community is significant and critical. Also, there is an urgent demand for improvement in the analytical algorithms and electronic devices to combat the COVID-19 pandemic. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.