Impact of quarantine on fractional order dynamical model of Covid-19.

In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease.

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Study on IoT for SARS-CoV-2 with healthcare: present and future perspective.

The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution-tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic.

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Factor analysis approach to classify COVID-19 datasets in several regions.

The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.