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2nd IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security, iSSSC 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2277569


In this article we have investigated a detection-accuracy enhancement technique of COVID-19 from multi-class lung diseases by instrumenting the CLAHE integrated deep learning technique. The image population is distributed upon disjoint sets of the patients suffering from diseases like (a) Viral-Pneumonia, (b) Lung Opacity, (c) COVID-19 and (d) Normal persons. We have used the CLAHE algorithm to pre-process those images and incorporated the ConvNet algorithm with the help of the transfer learning strategy to perform the analysis. The study reveals that the processing of the X-ray images by CLAHE technique followed by ConvNet algorithm can enhance the performance of the above mentioned four exclusive classes of lung images. Keeping the X-ray image processing using the CLAHE technique intact, we have further explored the diagnosis methods by using several CNN models such as, InceptionResNetV2, InceptionV3 and DenseNet121. In our study, out of 2400 chest X-ray images we have used 80% for the training and 20% for the validation purpose. The comparative study of the performance matrices explicitly shows the enhancement in multi-class detection accuracy. The study also shows that the CLAHE integrated DenseNet121 model provides the best performance exhibiting a maximum accuracy of 98.33% for detecting COVID-19 from multiple diseases. Also we have compared the performance of the present technique with the earlier reported approaches. © 2022 IEEE.

Studies in Economics and Finance ; 2021.
Article in English | Scopus | ID: covidwho-1311003


Purpose: The purpose of this paper is to investigate the effects of exchange rate volatility, oil price return and COVID-19 cases on the stock market returns and volatility for selected emerging market economies. Additionally, this study compares the market performance in the emerging economies during the COVID-19 pandemic with the pre-COVID and global financial crisis (GFC) period. Design/methodology/approach: The authors apply the arbitrage pricing theory to model the risk-return relationship between the risk-based factors (exchange rate volatility and COVID-19 cases) and stock market returns. By applying the exponential generalized autoregressive conditional heteroskedasticity model, the study captures the asymmetric volatility spillover from the stock markets to foreign exchange markets and vice versa. Findings: Findings reveal that exchange rate volatility exerts a negative and significant effect on the market returns in Brazil (BOVESPA), Chile (S&P CLX IPSA), India (SENSEX), Mexico (S&P BMV IPC) and Russia (MOEX) during the coronavirus pandemic. Regarding the effect of oil price returns, the authors find a positive relationship between oil price and stock market returns across all the economies in the study. The market returns of Russia, India, Brazil and Peru appeared more volatile during the pandemic than the GFC period. Practical implications: As the exchange rate volatility is causing higher risk and uncertainty in the stock market’s performance, the central bank’s effort to maintain a stabilizing effect on the exchange rate sale can be proven crucial for the economies under consideration. Emphasized should also be given to boost investors’ confidence in the stock market, and for this, the government policy actions in reducing the transmission of the disease are the need of the hour. Originality/value: While a large volume of literature on stock market performance in times of COVID-19 has emerged from developed economies, this study adds to the literature by exploring the emerging economies’ stock market performance during the COVID-19 pandemic. Unlike previous literature, this study examines the volatility spillover between stock and exchange rate markets in the worst affected emerging economies during the crisis. © 2021, Emerald Publishing Limited.