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Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced., Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected., Objective: COVID-19 attacks people with less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So, they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images., Method: This paper introduces a new denoising technique for low-dose Covid-19 CT images using a convolution neural network (CNN) and noise-based thresholding method. The major concern of the methodology for reducing the risk associated with radiation while diagnosing., Results: The results are evaluated visually and using standard performance metrics. From comparative analysis, it was observed that proposed works give better outcomes., Conclusion: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in noise suppression and clinical edge preservation., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.

Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients., (© 2022. The Author(s).)

The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare system. Globally, over 314.019.135 confirmed cases and 5.507.370 deaths have been recorded at 12 January 2022, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic, and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper proposes different machine-learning algorithms to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major populations, economics and affected by disease countries in Latin America. This model uses only the COVID-19 patient's geographical, social conditions, economics conditions, clinical risk factors, symptom reports, and demographic data to predict recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Web Services (AWS) with the vision to improve the clinical diagnosis, even infectious disease with mathematical approaches. [ABSTRACT FROM AUTHOR]

The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient's triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients' symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions. [ABSTRACT FROM AUTHOR]

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID‐19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID‐19 patients, which are not effective. The above complex circumstances need to detect suspected COVID‐19 patients based on routine techniques like chest X‐Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID‐19 or Non‐COVID‐19 patients with a full‐resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID‐19 patient dataset. The evaluation results are generated using a fourfold cross‐validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1‐score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset. [ABSTRACT FROM AUTHOR]

COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations’ governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system. This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates. The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data. An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only, such as age groups affected, other diseases, and stringency index. The data of Spain, Portugal, and Germany have been taken for a case study of BCG impact analysis. [ABSTRACT FROM AUTHOR]

• Prophet Model provides understanding of the number of people affected daily by this disease. • The imposed interventions are reflected by five significant changepoints detected by the model in the growth curve of India. • The model forecasts90 days future growth trend and finds the peak time for all the six countries and six states of India. • The model has achieved around 85% MAPE for all the six countries and the six states of India. Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure. [Display omitted] [ABSTRACT FROM AUTHOR]