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1.
IEEE Internet of Things Journal ; JOUR: 1-1,
Article in English | Scopus | ID: covidwho-2097634

ABSTRACT

In the current Pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes GPS spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behaviour, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behaviour patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3D tracker movements of individuals, 3D contact analysis of COVID-19 and suspected individuals for 24 hours, forecasting and risk classification of COVID-19, suspected and safe individuals. IEEE

2.
Indian Journal of Public Health Research and Development ; 13(4):213-216, 2022.
Article in English | EMBASE | ID: covidwho-2081579

ABSTRACT

Background: In recent decades, the prevalence of fungal sinus infection has increased. It's plausible that this is related to increased awareness, antibiotic usage, and the use of immunosuppressive drugs. Furthermore, much has been written on the involvement of fungus as a causative organism. Objective(s): To identify fungal pathogens and correlate laboratory findings with clinical findings. Material(s) and Method(s): Patients with AIFR following recent COVID-19 infection were included. After performing potassium hydroxide (KOH) wet mounts, post-operative material was collected and cultured on two tubes of Sabouraud dextrose agar (SDA) and stored at 250 C and 370 C for isolation and identification. Result(s): Out of 329 diabetic individuals with AIFS following COVID-19 infection, 51% exhibited mucopurulent discharge and 75.6 % had unilateral involvement. Only 57.4% of KOH mount samples were positive for fungal components, however 76.3% of SDA samples exhibited positive growth, with 62 % Mucorales, 8% Aspergillus, and 6 % Candida species. Conclusion(s): Mucor mycosis can develop in COVID-19 patients, particularly those with diabetes, a high and imprudent use of corticosteroids, and invasive ventilation. KOH test resulted in a preliminary diagnosis, whereas Culture remains the gold standard for identification. Copyright © 2022, Institute of Medico-legal Publication. All rights reserved.

3.
Academy of Marketing Studies Journal ; 26(4), 2022.
Article in English | ProQuest Central | ID: covidwho-2046519

ABSTRACT

In this ailment situation, a necessity for people is health, which effectuates to escalate in the consumption of healthy organic products. The study analyzes the importance and consumption of quality-based organic products towards spending on purchasing and over purchasing, clues scarcity in products were influenced by the role of media including panic buying, consumer psychology, especially on children’s immunity power that might be for the third wave. By applying Statistical Package for Social Science (SPSS), correlation, KMO, and Bartlet’s with 112 respondents the present data were gathered with the assistance of purposive and snowball sampling, the results revealed that GHI and NEP have a positive association with PS and strongly influenced organic products buying process. It will assist in raise of sales and evolving advanced green strategies for the green marketers.

4.
7th International Conference on Communication and Electronics Systems, ICCES 2022 ; : 85-89, 2022.
Article in English | Scopus | ID: covidwho-2018796

ABSTRACT

In today's world, technology has drastically increased in all sectors of industries and businesses. Automated machines demands have increased rapidly. Most small and medium scale businesses are trying to use this technology to increase the speed and reliability. One of the booming technologies is robotics, which helps businesses in so many ways. This paper discusses the approach and experience in the design, simulation, modelling, testing, and deployment of a Low-Cost food delivery robot in hotels, restaurants etc. These food delivery robots give an enhanced experience for the customers and benefits the restaurant business financially by bringing attention to visitors and act as a publicity. The restaurant industry is also experiencing a downturn because of the COVID-19 breakout. With this method, food can be delivered directly from the kitchen to the customer's table while maintaining all norms and sanitary guidelines. The robot uses microcontroller mounted with DC motors. Ultrasonic sensors and IR sensors are used for mapping and localization of destination tables, motor drivers, obstacle detection, collision avoidance, path detection. The robot performed as per the test and achieved the desired result. © 2022 IEEE.

5.
NeuroQuantology ; 20(6):7971-7985, 2022.
Article in English | EMBASE | ID: covidwho-2006535

ABSTRACT

Lung cancer patients have a greater frequency of COVID-19 infection, pulmonary issues, and worse survival rates as compared to the general population. The world's major professional organisations released new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients as a guide for prioritising cancer care issues during the epidemic. In the modern world, we are battling COVID-2019, a coronavirus-driven pandemic that is among the worst in human history. If the infection is discovered early, the patient can receive treatment right away (before it enters the lower respiratory tract).once the infection has reached the lungs, to look for ground-glass opacity on the chest X-ray caused by fibrosis. Based on the significant differences in the X-ray images of an infected and non-infected person, artificial intelligence systems can be used to determine the presence and severity of illness. In order to extract the features I needed for this study, I used feature extraction from transfer learning, which involves importing a pre-trained CNN model such as the Distributed Deep Convolutional Inception model, Distributed Deep Convolutional VGGNet model, or Distributed Deep Convolutional with ResNet Model and altering the final layer to suit my needs.The model can achieve an F1-score of 0.88 using Distributed Deep Convolutinal VGGNet, the highest of all pretrained models. Additionally, the COVID-19- related X-rays are broken down into three severity levels: moderate, medium, and severe. The data are analysed using the F1-Score because precision and recall are both crucial elements in this investigation. The confusion matrix and the results for the F1-Score, Precision, Recall, and overall Accuracy are also supplied to give a full analysis of the Model performance. The proposed strategies have had a significant impact on the nation as a warning to society.

6.
Indian Journal of Critical Care Medicine ; 26:S39-S40, 2022.
Article in English | EMBASE | ID: covidwho-2006338

ABSTRACT

Aim and background: Mechanical Power in ARDS has predictive value for both VILI and mortality. Driving pressure and mechanical power are two new targets in the mechanical ventilation of ARDS patients. COVID-19 pneumonia has two different phenotypes H type and L type which have different lung compliance, elasticity, and recrutability with different ventilatory strategies. We want to observe how Mechanical Power behaves in H type COVID-19 ARDS and its correlation with compliance and driving pressure. Objective: To study the correlation of Mechanical Power with Driving Pressure and Compliance in H type of COVID-19 pneumonia. Materials and methods: It is a prospective observational study conducted in COVID-19 patients admitted to the Medical Intensive Care unit. We included 65 adult COVID-19 patients aged between 18 and 70 years requiring invasive mechanical ventilation for at least 24 hours. Patients who developed spontaneous pneumothorax and pneumomediastinum before initiation of mechanical ventilation were excluded. Patients were categorised to H type based on lung compliance (<40 mL/cmH2O), recrutability, and lung weight. The Mechanical Power was calculated using the following equation, MP = 0.098 ∼ TV ∼ RR (Paw-1/2 for). Paw-peak airway pressure, for-driving pressure, TV-tidal volume, RR-respiratory rate. The variables are taken at 3 different time intervals in the first 24 hours of invasive mechanical ventilation. All patients are ventilated according to ARDSNET protocol. The Driving pressure and compliance were recorded. The correlation of Mechanical Power with Driving pressure and Compliance were analysed using Pearson Correlation. Results: The mean age of the patients was 57.04 ) 13.96 years (mean ) SD), gender distribution 75% were males and 25% were females. A positive correlation was observed between Mechanical power and Driving pressure (Pearson correlation 0.245) which is statistically significant p = 0.049. A negative correlation was observed between Mechanical power and Compliance (Pearson correlation 0.183) which is not statistically significant. Conclusion: The Mechanical Power and Driving pressure the new targets of Ventilator-Induced Lung Injury (VILI) and also predictors of mortality in ARDS patients. The positive correlation between Mechanical Power and Driving pressure was observed in H type of COVID-19 patients which behaves similar to other ARDS and independent risk factors of mortality in H type of COVID-19 ARDS too.

7.
Indian Journal of Critical Care Medicine ; 26:S1-S2, 2022.
Article in English | EMBASE | ID: covidwho-2006313

ABSTRACT

Background: The neutrophil-to-lymphocyte ratio (NLR) has been used as a circulating biomarker to determine the prognosis of inflammatory reaction in many conditions. Inflammation has an important role in the progression of COVID-19. NLR has proved to be potentially useful in diagnosing and prognosis of COVID-19 patients. Aim: To determine whether NLR is useful for the prognostication of outcomes in patients on mechanical ventilation due to severe ARDS in COVID-19. Materials and methods: This is a prospective study in which patients of both genders between age 18 and 78 years with severe ARDS due to COVID-19 who required mechanical ventilation within 1st 3 days of admission were included. CBC was done on 1st day of admission for calculation of NLR. The outcome was defined as in-hospital mortality. Pregnant patients, patients on steroids, and immunosuppression therapy and immune-compromised patients were excluded from the study. Results: Statistical analysis was done using Mann-Whitney, Pearson, and Chi-Square tests. 135 patients who required mechanical ventilation within the first 3 days of admission were included. 34 (25.1%) patients were females and 101 (74.81%) patients were males. 49 (36.29%) patients survived and 88 (63.70%) patients died. The median of NLR in alive 11.10 (1.18-48) and dead 11.05 (1.55-48) was statistically insignificant (P value 0.71). The comparison of NLR in males (14.37 ± 10.51) and females (10.76 ± 5.74) was statistically significant (p = 0.05). The mortality in females was 70.58% whereas in males it was 61.38%. As per the Person correlation test, there is a negative correlation of NLR with age and gender to determine mortality. Conclusion: NLR is not a good biomarker to predict the outcomes of patients on mechanical ventilation due to severe ARDS in COVID-19 disease. Though the NLR values were lower in females, the mortality was higher in the female group.

8.
Pediatrics ; 149, 2022.
Article in English | EMBASE | ID: covidwho-2003377

ABSTRACT

Background: Due to the COVID-19 pandemic children were deprived of in-person attendance at school and experienced social isolation. The impact of these social-distancing measures on pediatric mental health is only now being unraveled. We conducted a descriptive review of psychiatric diagnoses at a pediatric outpatient practice in a Southern Illinois rural community. We compared the trends of pediatric psychiatric diagnosis before and following the COVID pandemic. Methods: Pediatric Group LLC has multiple office locations in Rural Southern Illinois catering to about 10,000 pediatric patients staffed by pediatric providers and a clinical psychologist. The pediatric population has remained stable during the period. The care providers and practices have remained unchanged over the past four years. We did a retrospective review of electronic health records from January 2019 through June 2021. Using ICD10 diagnostic codes, we analyzed the top 100 diagnoses made at the pediatric practice. Diagnoses were broadly classified into psychiatric and non-psychiatric categories. Psychiatric illnesses included anxiety, attention deficit hypersensitivity disorder (ADHD), conduct disorders, mood disorders, sleep disorders, and other psychiatric illnesses such as post-traumatic stress disorder (PTSD). Descriptive comparisons were made between pre-COVID (2019) and post-COVID (2021) periods. Results: Compared to a baseline of 5044 encounters in 2019 (pre-COVID), attendance was 9% lower (4680) in 2020. Attendance dropped by 14% (2206) in the first half of 2020, increasing by 11% (2474) to reach preCOVID levels in the second half of 2020. The attendances continued to increase in the first half of 2021, reaching 43% higher (3614) numbers compared to pre-COVID levels. Compared to 2019 and 2020, an increase in all psychiatric diagnoses was seen in our offices in the year 2021. Further analysis of the year 2021 showed significant increases in Anxiety and Depressive disorders, Oppositional Defiant Disorder (ODD), Disruptive Mood Dysregulation disorder (DMDD), and Major Depressive disorders (MDD) that almost doubled the statistics from the pre-COVID period. Sleep disorders and Post Traumatic Distress Disorder (PTSD) visits increased by far more than 150 percent. (Table 1) Conclusion: A steady increase in pediatric psychiatric illness has been noted in the second half of 2020 and first half of 2021 following COVID pandemic. We observed an increase by over two times with almost all the psychiatric disorders in 2021. The overall increase in the incidence of various pediatric psychiatric illnesses is concerning. We believe that the absence of in-school attendance may have played a significant role.

10.
Concurrency and Computation: Practice and Experience ; 2022.
Article in English | Scopus | ID: covidwho-1981630

ABSTRACT

In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID-19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two-stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC-CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC-CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC-CNN model is trained and tested with 3D-IRCADb-01 and Kaggle datasets. The tested results prove that the proposed FC-CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively. © 2022 John Wiley & Sons, Ltd.

11.
NeuroQuantology ; 20(6):8833-8847, 2022.
Article in English | EMBASE | ID: covidwho-1979735

ABSTRACT

Lung cancer patients have a greater frequency of COVID-19 infection, pulmonary issues, and worse survival rates as compared to the general population. The world's major professional organisations released new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients as a guide for prioritising cancer care issues during the epidemic. In the modern world, we are battling COVID-2019, a coronavirus-driven pandemic that is among the worst in human history. If the infection is discovered early, the patient can receive treatment right away (before it enters the lower respiratory tract).once the infection has reached the lungs, to look for ground-glass opacity on the chest X-ray caused by fibrosis. Based on the significant differences in the X-ray images of an infected and non-infected person, artificial intelligence systems can be used to determine the presence and severity of illness. In order to extract the features I needed for this study, I used feature extraction from transfer learning, which involves importing a pre-trained CNN model such as the Distributed Deep Convolutional Inception model, Distributed Deep Convolutional VGGNet model, or Distributed Deep Convolutional with ResNet Model and altering the final layer to suit my needs.The model can achieve an F1-score of 0.88 using Distributed Deep Convolutinal VGGNet, the highest of all pretrained models. Additionally, the COVID-19-related X-rays are broken down into three severity levels: moderate, medium, and severe. The data are analysed using the F1-Score because precision and recall are both crucial elements in this investigation. The confusion matrix and the results for the F1-Score, Precision, Recall, and overall Accuracy are also supplied to give a full analysis of the Model performance. The proposed strategies have had a significant impact on the nation as a warning to society.

12.
JOURNAL OF ALGEBRAIC STATISTICS ; 13(3):40-45, 2022.
Article in English | Web of Science | ID: covidwho-1965422

ABSTRACT

A data of four factors contributing to covid-19 infection and whether the person has been infected or not is taken as input to train a Random Forest Algorithm. Factors like wearing mask, regularity of exercise, consumption of pepper and area population density are taken as input factors and the status of covid-19 infection is taken as output parameter. A random forest model trained with at least 50 data instances will become a powerful predictive model for assessing the risk of covid19 infection.

13.
Journal of Pain and Symptom Management ; 63(5):780-781, 2022.
Article in English | Web of Science | ID: covidwho-1925466
14.
Indian Journal of Psychiatry ; 64(SUPPL 3):S558, 2022.
Article in English | EMBASE | ID: covidwho-1913268

ABSTRACT

Background: The Covid-19 pandemic is of an international scale with the number of people working on frontlines in healthcare positions has been of a scale never witnessed before. The wellbeing and emotional resilience of healthcare professionals are key components of continued delivery of healthcare services during the pandemic. Although, researchers have estimated burnout and resilience among health care workers, they didn't study in health care workers who were infected with covid. Nor was the group compared to uninfected health care workers. Aims: To compare these two groups to gain insights into how the infection will affect the already wounded morale of the health care workers in the light of continued onslaught in form of subsequent waves. Methods: This cross sectional study was conducted for 1 month, after obtaining the ethics clearance. Purposive sampling was used to include doctors, interns &nurses involved in Covid 19 duties in Andhra Pradesh during the second wave. A specially prepared anonymous, voluntary, online, valid, reliable self-administered google form was used. Brief resilience scale and Copenhagen Burnout inventory, used to asses resilience and burnout respectively, along with socio demographic data. The data was analysed using by Chi-square test, Fischer-exact test for significance of association. Results: The sample consisted of 249 respondents, of which, majority (n=134;45.4%) were between the ages 18-24 yrs, and most of them, interns (n=146;58.6%). As per the two groups, 27.7%(n=69) of subjects have been afflicted by Covid 19 during second wave. Conclusion: The mean resilience for those afflicted by COVID 19 and Non-afflicted was 2.9 and 3.2 respectively and mean work-related burnout was 60.7 and 54.6 respectively, indicating a difference in attitudes and outlook towards the work.

15.
Indian Journal of Psychiatry ; 64(SUPPL 3):S543, 2022.
Article in English | EMBASE | ID: covidwho-1913237

ABSTRACT

Background: A bidirectional relationship was seen between COVID 19 survivors and psychological issues. Global research shows 1 - 7% of patients infected with COVID had aggressive behaviour in acute as well as post illness stage. In general population, people with higher extroversion scores were associated with lesser social distancing, thereby high chances of infection. No clear data exists from India to comment on personality profile or aggressive intent among COVID survivors. Aim: The current study aimed to assess the personality profile and aggression intent among COVID-19 survivors in patients from Telangana. Methodology: This study is conducted in hospital out-patient or in-patient setting of KIMS, Narketpally, Nalgonda, Telangana in patients of COVID-19. A total of 157 COVID-19 patients were recruited based on convenient sampling. The data from patients was collected using standardized objective semi-structured proforma which contains clinical and sociodemographic profile, Big Five Inventory, Assessment of Aggressive Intent (which includes six components). Results: Among the study population, 18.5% of them had positive scores in one or more of the six components of aggression. On personality assessment, patients with aggression had significantly lower mean scores on agreeableness (p value 0.003) and significantly higher mean scores on neuroticism (p value 0.049). Conclusion: COVID survivors, along with medical complications, have a risk of having psychological disturbances like aggression. Clinicians should be aware and screen for these psychological issues when the patient comes for follow-up. Early intervention and management of aggression can help in improving the patient's quality of life.

16.
The Open Public Health Journal ; 15(e187494452204070), 2022.
Article in English | CAB Abstracts | ID: covidwho-1902779

ABSTRACT

Background: During the emergence of the COVID-19 pandemic in South Africa in March 2020, there was an urgent mobilization of healthcare workers (HCWs) who had to adapt quickly to a challenging health system. Therefore, this paper examines factors associated with HCWs' perceptions of the South African health system's capability for managing COVID-19 during the early stages of the pandemic.

17.
Interactive Technology and Smart Education ; : 19, 2022.
Article in English | Web of Science | ID: covidwho-1886559

ABSTRACT

Purpose The purpose of this study is to evaluate student visual literacy skills using the newly designed visual literacy framework and visual literacy (VL) scale. Design/methodology/approach It includes a newly designed framework, a self-reporting questionnaire and a scale to evaluate an individual's VL skills and overall competency. The self-reporting questionnaire consists of 13 items with a five-point Likert scale. Findings The newly developed VL skill scale assessed the Fiji students' competency (i.e. identify, understand, evaluate and communicate using visuals). The mean for the 13 items on VL skills showed average results, but 46.33% recorded high visual literacy competencies. The multiple linear regression analysis outcomes showed all 13 skills demonstrated significant contributions to becoming visually literate. Research limitations/implications The limitation of this study is that the questionnaire is self-reporting, so the evaluation can be highly rated. The implications are that relevant stakeholders will be able to devise strategies and content to improve visual literacy in Fiji. Practical implications Images are playing an important role today, especially after COVID-19, which forced the education system to go online. Online learning involves a lot of visuals, and as such, visual literacy is important to students so that they can successfully learn online. This paper brings out the important aspects of visual literacy, which needs to be understood by the students. Social implications In society, everything involves visuals. This paper introduces a visual literacy scale and a visual literacy tool to measure the visual competencies of individuals. If people understand the components of visual literacy, then visual competencies of the people will also improve. Originality/value To the best of the authors' knowledge, this paper is the first one on evaluating visual literacy competencies in Fiji and also in the South Pacific. The visual literacy tool is also new to the world.

18.
Journal of Research ANGRAU ; 48(4):59-68, 2020.
Article in English | CAB Abstracts | ID: covidwho-1864077

ABSTRACT

The online survey was conducted between June, 2020 and August, 2020 to understand the perception of the undergraduate students of the S.V. Agricultural College. Tirupati regarding online classes. Out of 300 undergraduate students pursuing 1st, 2nd, and 3rd year of B.Sc (Hons.), 72 students belonging to all three years responded. The perception of the students was categorised under four aspects viz., perception of learning environment, social perception, academic self-perception and perception of students on faculty members. The majority of the students (97.1%) were using smartphone for attending virtual classes, ranked online lecture supported by a screen shared PowerPoint as the best virtual teaching method with a weightage of 278 and 3 hrs/day was the comfortable screen time mentioned by 50 percent of the students. About 95.7 percent of the students perceived that a real class environment helps in good understanding than an online learning environment. The majority (87.2%) stated that learning in isolation is not exciting. Less than half (44.10%) of respondents expressed satisfaction, while 55.90% of the respondents expressed dissatisfaction towards virtual classes.

19.
6th Annual International Conference on Information Communications Technology and Society, ICTAS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831822

ABSTRACT

This article deals with a case study of migration from a local in-house data centre (Hyper-V Windows Server 2012) at the Durban University of Technology to Microsoft Azure. The introduction provides the context leading to the migration and the details about why crisis management became the norm during the start of the Covid-19 pandemic in 2020. The background reprises the start of e-learning leading up to the present. Next, an account is given of the methodology used to migrate to Azure, which suggests that the lack of foresight or strategic planning resulted in knee-jerk responses, slow implementation and avoidable stress on project personnel. A simplified technology roadmap for e-learning at DUT is then provided, and is suggested as a starting point for a more comprehensive roadmap, using the system dynamics approach. © 2022 IEEE.

20.
2022 International Conference on Electronics and Renewable Systems, ICEARS 2022 ; : 1799-1804, 2022.
Article in English | Scopus | ID: covidwho-1831804

ABSTRACT

As of January 2019, there have been fears worldwide over COVID-19. In order to detect a person is affected by the virus is not, Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest X-Ray Images, Computerized Tomography (CT) scans are used. The patients who test positive for COVID-19 require early treatment and diagnosis. Manually analyzing the medical images of Chest radiographs and CT scans takes more time and are more susceptible to human error. So, to overcome this problem, Artificial Intelligence (AI) and Deep Learning-based tools are used to analyze medical images. This study focuses primarily on comparing deep learning models and finding the best one to detect COVID-19 in CT scans and X-rays of the chest. For X-Rays of the chest, COVID-19 Radiography Database is used, and SARS COV 2 Ct Scan Dataset is used for CT scans. © 2022 IEEE.

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