Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. The deadly Covid 19 viruses suddenly stopped the fast-moving world and all the commercial and noncommercial activities were stalled for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification (PPC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper classification (SAMC) and Spectral information divergence classification (SIDC) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that PPChas obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through MDC, MaDC, and MLCclassification algorithms. © 2022 The Author(s)

Satellite Data Investigation for Change Estimation During COVID Era by Fusing Pixel and Object-Based Technique

In this investigation, an innovative combination of pixel-based change detection technique and object-based change detection technique is explored with the satellite images of Holy Masjid al-Haram, Saudi Arabia. The gray-level co-occurrence matrix (GLCM) method is used to quantify the texture of the remote sensing data through the texture classification approach on the satellite data in this work. GLCM produces results of the texture quantification in normalized form. Thus, applying a texture classification scheme on the satellite data is impressive to observe. Later maximum likelihood image classification approach is used for classification purposes. The classified information is categorized into four different classes. The kappa coefficient’s value and the overall accuracy for the pre- COVID classified study area are 0.6532 and 76.38%, respectively. During COVID, the classified study area presents the kappa coefficient and the overall accuracy of 0.7631 and 82.18%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diurnal Variation in the Mortality of Patients with COVID-19 Pneumonia: A Retrospective Study

Introduction: Coronavirus Disease 2019 (COVID-19), the new contagious novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), pandemic in 2020-21 has had a devastating impact on human race. The most common cause of death among hospitalised patient was COVID-19 pneumonia or lung injury. Various studies have shown diurnal variation in human mortality due to all causes with or without intervention. Aim: To identify existence of diurnal variations for mortality among the hospitalised patients with COVID-19 pneumonia. Materials and Methods: This hospital-record based, retrospective study was conducted in a tertiary referral centre of north east India (Assam Medical College, Dibrugarh, Assam, India) which was a dedicated COVID-19 hospital during the pandemic. The study was conducted from September 2021 to December 2021 and the data was collected and recorded from the Cadaver slips issued to families of patient dying of COVID-19 pneumonia during the period January 2021 to August 2021. The data were generated by plotting the number of deaths of COVID-19 cases for each two hour interval as a percent of the mean number of deaths per two-hour interval and as a percentage of cumulative deaths per two-hour interval on a 24 hour scale. The deaths were sub grouped according to gender, age, and reported co-morbid causes of death along with pneumonia. Comparisons of data i.e., mean deaths/2 hour interval (mean±SD) were performed by one-way Analysis of Variance (ANOVA), followed by Bartlett's test for equal variances. The p-value <0.05 was considered as statistically significant. Results: Total 743 deaths, with 537 males and 206 females were included in the study. Mean age of the deaths was 56.39 years. There was rise of deaths during 4 PM to 6 PM (16:00 to 18:00) interval for all deaths due to COVID-19 pneumonia. The increase in deaths during this period was mainly due to deaths among males equal or above 65 years and females below age 65 years. However, the deaths of females equal or above the age of 65 years did not show significant diurnal variation. Only 26.51% (n=197) of pneumonia deaths were without co-morbidity. Conclusion: There exists a diurnal variation in mortality among COVID-19 pneumonia patients with evening rise of deaths. Diurnal variation is significantly more among males rather than females above 65 years.

Investigating Novel Coronavirus Through Statistical Methods for Biomedical Applications: An Engineering Student Perspective

The World of today is suffering from novel coronavirus (nCOV2). This is a respiratory infectious disease that has affected the entire globe. This respiratory infection is first originated in Wuhan, China. Today, it has many variants like the “United Kingdom (UK) variant called B.1.1.7,” “South African variant is called B.1.351,” “Brazilian variant is known as P.1,” etc. In this research work, we will discuss the Indian scenario to tackle nCOV2. We will also present an engineering student’s perspective to detect changes developed in the patient’s chest suffering from nCOV2 employing statistical methods. Among all the statistical techniques, GLCM-based texture analysis-based technique has gained popularity due to its diverse applications. It is used in many applications like remote sensing, image processing, biomedical applications, seismic data analysis. Thus in this research work, this methodology is used various changes in the before and after images of the patient suffering from the novel coronavirus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact and Investigation of nCOV 2 in the Indian State of Uttarakhand

In this research work, we have presented a brief study of the impact of the Novel Coronavirus in my hometown Dehradun, in the State of Uttarakhand, India. Here we discussed the impact of Coronovirus on various sectors and districts of the State. Here we have also discussed the State government’s steps and precautions to fight this global epidemic. We have also presented a change detection methodology to identify coronavirus’s impact on the patient’s chest using image processing techniques. Pre and post-DICOM images of Covid infected person are analyzed based on the statistical image parameters. Texture classification of the pre and post DICOM images is performed based on the visual statistical features, i.e., contrast, correlation, energy, and homogeneity. Finally, for both the images histogram signature plotting is performed, and based on this, changes developed in the DICOM images are monitored. © 2021 IEEE