Optimizing CNN-LSTM hybrid classifier using HCA for biomedical image classification

In medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID-19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID-19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID-19 belongs to Chest X-Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID-19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long-short term memory (LSTM) is used for final classification. Moreover, the Hill-Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data-set is pre-processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty-five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID-19 were used for testing the proposed model ‘CNN-LSTM-HCA'. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as;Accuracy, Sensitivity, Specificity, F-measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN-LSTM-PSO, CNN-LSTM-Jaya, and CNN-LSTM-GWO and also with state-of-art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID-19, Haemorrhage, and Alzheimer's data sets, respectively. © 2023 John Wiley & Sons Ltd.

The effect of COVID-19 on Indian stock market volatility: can economic package control the uncertainty?

Purpose The stock market has shown fluctuating degrees of volatility because of the recent COVID-19 pandemic in India. The present research aims to investigate the effect of the COVID-19 on the stock market volatility, and whether the economic package can control the market volatility or not, measured by a set of certain sector-level economic features and factors such as resilience variables. Design/methodology/approach We examine the correlation matrix, basic volatility model and robustness tests to determine the sector-level economic features and macroeconomic factors helpful in diminishing the volatility rising because of the COVID-19. Findings The outcomes of this study are significant as policymakers and financial analysts can apply these economic factors to set policy replies to handle the unexpected fluctuation in the stock market in sequence to circumvent any thinkable future financial crisis. Originality/value The originality of the paper is to measure the variables affecting the stock market volatility due to COVID-19, and understand the impact of capital market macroeconomic variables and dummy variables to theoretically explain the COVID-19 impact on stock market volatility.

Global Level Smart Vaccination Tracking System using Blockchain and IoT

The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment. Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19. Most of these vaccination supply chain models are cloud-centric and depend on humans. Due to this, the transparency in the supply chain and vaccination process is questionable. Moreover, we con't trace where the vaccination programs are facing issues in real-time. Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities. This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things. Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity. We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities. The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases. From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure. Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation. © 2021 IEEE.All rights reserved.