ABSTRACT
The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.
ABSTRACT
In this research article, we have introduced a knowledge-based approach to regional/national security measures. Proposed Knowledge-based Normative Safety Measure algorithm for safety measures helps to take practical actions to conquer COVID-19. We analyzed based on five dimensions: the correlation between detected cases and confirmed cases, social distance, the speed of detected cases, the correlation between imported cases and inbound cases, and the proportion of masks worn. It prompts actions based on the security level of the region. Through the use of our proposed algorithm, the government has accelerated the implementation of social distancing, accelerated test cases, and policies, etc., to prevent people from contracting COVID-19. This idea can be a very effective way to realize the impending danger and take action in advance. Help speed up the process of controlling the COVID-19. In pandemic times, it can be helpful to understand better. Holding the normative safety measure at a high level leads nations to perform excellently on triple T's (testing, tracking, and treatment) policy and other safety acts. The proposed NSM approach facilitates for improve the governance of cities and communities.
ABSTRACT
The Internet of Things (IoT) plays an important role in various healthcare applications. Radio Frequency IDentification (RFID) is widely used as a leading identification technology in a variety of IoT-health applications. In 2020, the number of cases of novel Corona Virus Disease 2019 ( n COVID-19) was increased rapidly throughout the world. Herein, IoT-Health enables the more convenient ways to access remotely and efficiently the medical services for the patients, also provides health monitoring by the doctors, physicians, and nurses over the Internet. However, security and privacy are considered key concerns in RFID-based IoT-health systems due to wireless communication over the channel. There could be huge risks of leakage of the patient's sensitive information, medical data, privacy of the patients, and so forth. To overcome these shortcomings, we have put forward a secure and reliable RFID authentication protocol using Digital Schnorr Cryptosystem for IoT-Health in COVID-19 patients care named S R2 AP-DSC. Compared with the similar existing protocols, the security analysis followed by the performance evaluation of our proposed protocol demonstrates the minimal computation overheads and also provides resistance to various well-known security attacks. The AVISPA and Scyther simulation results confirm that the proposed protocol is safe under active and passive attacks. The overall analysis shows that the S R2 AP-DSC is relatively superior to the other similar existing protocols.
ABSTRACT
The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.
ABSTRACT
In 2019, a novel type of coronavirus emerged in China called SARS-COV-2, known COVID-19, threatens global health and possesses negative impact on people's quality of life, leading to an urgent need for its diagnosis and remedy. On the other hand, the presence of hazardous infectious waste led to the increase of the risk of transmitting the virus by individuals and by hospitals during the COVID-19 pandemic. Hence, in this review, we survey previous researches on nanomaterials that can be effective for guiding strategies to deal with the current COVID-19 pandemic and also decrease the hazardous infectious waste in the environment. We highlight the contribution of nanomaterials that possess potential to therapy, prevention, detect targeted virus proteins and also can be useful for large population screening, for the development of environmental sensors and filters. Besides, we investigate the possibilities of employing the nanomaterials in antiviral research and treatment development, examining the role of nanomaterials in antiviral- drug design, including the importance of nanomaterials in drug delivery and vaccination, and for the production of medical equipment. Nanomaterials-based technologies not only contribute to the ongoing SARS- CoV-2 research efforts but can also provide platforms and tools for the understanding, protection, detection and treatment of future viral diseases.
ABSTRACT
The outburst of the pandemic Coronavirus disease since December 2019, has severely impacted the health and economy worldwide. The epidemic is spreading fast through various means, as the virus is very infectious. Medical science is exploring a vaccine, only symptomatic treatment is possible at the moment. To contain the virus, it is required to categorize the risk factors and rank those in terms of contagion. This study aims to evaluate risk factors involved in the spread of COVID-19 and to rank them. In this work, we applied the methodology namely, Fuzzy Analytic Hierarchy Process (FAHP) to find out the weights and finally Hesitant Fuzzy Sets (HFS) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to identify the major risk factor. The results showed that "long duration of contact with the infected person" the most significant risk factor, followed by "spread through hospitals and clinic" and "verbal spread". We showed the appliance of the Multi Criteria Decision Making (MCDM) tools in evaluation of the most significant risk factor. Moreover, we conducted sensitivity analysis.
ABSTRACT
This article focus the elimination and control of the infection caused by COVID-19. Mathematical model of the disease is formulated. With help of sensitivity analysis of the reproduction number the most sensitive parameters regarding transmission of infection are found. Consequently strategies for the control of infection are proposed. Threshold condition for global stability of the disease free state is investigated. Finally, using Matlab numerical simulations are produced for validation of theocratical results.