Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

IoT-Based Smart Mask Protection against the Waves of COVID-19

In the year 2020, the word “pandemic” has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a “Pandemic”. The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytics. The manuscript and associated research work integrate the IoT and Internet of Everything (IoE) based analytics with sensor technologies with real-time data so that the overall predictions will be more accurate and integrated with the health sector. Supplementary Information The online version contains supplementary material available at 10.1007/s12652-022-04395-7.

The Chinese plug-in electric vehicles industry in post-COVID-19 era towards 2035: Where is the path to revival?

The sudden Coronavirus Disease reported at the end of 2019 (COVID-19) has brought huge pressure to Chinese Plug-in Electric Vehicles (PEVs) industry which is bearing heavy burden under the decreasing fiscal subsidy. If the epidemic continues to rage as the worst case, analysis based on System Dynamics Model (SDM) indicates that the whole PEVs industry in China may shrink by half compared with its originally expected level in 2035. To emerge from the recession, feasible industrial policies include (1) accelerating the construction of charging infrastructures, (2) mitigating the downtrend of financial assistance and (3) providing more traffic privilege for drivers. Extending the deadline of fiscal subsidy by only 2 years, which has been adopted by the Chinese central government, is demonstrated to achieve remarkable effect for the revival of PEVs market. By contrast, the time when providing best charging service or most traffic privilege to get the PEVs industry back to normal needs to be advanced by 10 years or earlier. For industrial policy makers, actively implementing the other two promoting measures on the basis of existing monetary support may be a more efficient strategy for Chinese PEVs market to revive from the shadow in post-COVID-19 era.