Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN)

COVID-19 endemic has made the entire world face an extraordinary challenging situation which has made life in this world a fearsome halt and demanding numerous lives. As it has spread across 212 nations and territories and the infected cases and deaths are increased to 5,212,172 and 334,915 (as of May 22 2020). Still, it is a real hazard to human health. Severe Acute Respiratory Syndrome cause vast negative impacts economy and health populations. Professionals involved in COVID test can commit mistakes when testing for identifying the disease. Evaluating and diagnosing the disease by medical experts are the significant key factor. Technologies like machine learning and data mining helps substantially to increase the accuracy of identifying COVID. Artificial Neural Networks (ANN) has been extensively used for diagnosis. Proposed Single Hidden Layer Feedforward Neural Networks (SLFN)-COVID approach is used to detect COVID-19 for disease detection on creating the social impacts and its used for treatment. The experimental results of the proposed method outperforms well when compared to existing methods which achieves 83% of accuracy, 73% of precision, 68% of Recall, 82% of F1-Score. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Comparison of COVID-19 Detection using Deep Learning Methods

The recognition of covid-19 is major confront in today's world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure. © 2023 IEEE.

Weather and population based forecasting of novel COVID-19 using deep learning approaches

The spread of novel corona virus across the globe has a significant impact on various stake holders and posting a major challenge to the research community. Government has taken several measures for maintaining social distance and containment of disease, but still it is not a sufficient for the developing countries like India where the level of understanding the issue is deprived and hence it is a major challenge to the Health Care professionals. Therefore, it is mandatory that a prediction of the number of possible cases enables the preparedness of the Government and the Hospitals in resolving the issues and to take measures in controlling the spread of the disease Series. Deep learning model has been built by considering the features of weather and COVID-19 data (recovered, infected and deceased) for predicting the number of cases expected in India. The model is built on Concurrent Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional RNN (BRNN), Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) based on the daily weather and COVID-19 data collected from Indian subcontinent. The results revealed that the algorithm BRNN yields a better prediction model when compared with the other models.

Probiotics mediated control and immunomodulation of SARS-CoV-2 associated pathological conditions

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a fast-evolving respiratory infection-causing virus. The simple structure of the virus enables it to spread through the air and easy lodging into the respiratory tract of the host. The virus-specific spike protein rendering antigenicity and pathogenicity in the host. The spike protein is highly evolving as an immunogenic form through mutation. Variants of the virus are much more pathogenic that induce inflammation during the early stages of infection and cytokine storm in later stages of infection, which cause cytolysis of own host cells. Drugs used are targeted towards spike protein and virus-specific components and several drugs are under trial. A drug that needs to control the host immune response and maintain homeostasis in the host is required. Anti-inflammatory drugs and steroids recommended for immunostimulatory and as immunosuppressors according to the prevailing condition of infection. Several phytomedicines evaluated for their efficiency to control the pandemic. Another natural system to control viruses is the probiotic organisms, proven for their ability to competitively inhibit entry and establishment of viral pathogens in the host. Administration of probiotics during the primary stage of viral infection improves the host immunity and prevents the increase in viral load and in later stages, regulates the host immunity when host inununoregulation disrupted by viral antigen. This helps in the immunomodulation of the host immune system on the whole. The present review evaluates the immunomodulatory effect of probiotics to control SARS-CoV-2.