Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.
Cloud computing is the concept of using maximum remote services through a network using various minimum resources, it provides these resources to users via internet. There are many critical problems appeared with cloud computing such as data privacy, security, and reliability etc. But we find that security is the most important between these problems. In this research paper, the proposed approach is to eliminate the concerns regarding data security using bio hash function for biometrics template security to enhance the security performance in cloud as per different perspective of cloud customers. Experiments using well know benchmark CASIA fingerprint-V5 data sets show that the obtained results proved that using Bio-hash function approach is more efficient in protecting the biometric template compared to Crypto-Biometric Authentication approach and the error rate is minimized by 25%.
Sharing images via internet is very important and widely used nowadays especially medical images. Medical Image Sharing is a term for the electronic exchange of medical images between hospitals, physicians and patients. Rather than using traditional media, such as a CD or DVD, and either shipping it out or having patients carry it with them, technology now allows sharing of these images using the internet. In this research paper, the proposed approach is to secure medical images stored in the cloud by using Iris recognition as a human biometrics to check identity of the user who has the authorization to access the medical images. Experiments using well know benchmark CASIA-Iris V4 data sets show that the obtained results is more efficient in protecting the data on the cloud compared to the approach presented in [24] by 20%.
During the ongoing worldwide crisis, researchers, clinicians, and medical care specialists around the world continue looking for another innovation to help in handling the COVID-19 pandemic. The proof of Machine Learning (ML) and Artificial Intelligence (AI) application on the past pestilence empower scientists by giving another point to battle against the novel Coronavirus episode. This paper intends to thoroughly audit the part of AI and ML as one critical technique in anticipating SARS-CoV-2 and its related epidemic. Coronavirus is an irresistible illness, and it does serious harm to the lungs. Coronavirus causes disease in people and has executed numerous individuals in the whole world. Nonetheless, this infection is accounted for as a pandemic by the World Health Organization. (WHO) and all nations are attempting to control and lockdown all spots. This work's main principle goal is to predicting the spread of COVID-19 across Egypt and analyzing the development rates. For this aim, we access real datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). And European Union open dataset. We have implemented the results by using R Language.
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