Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
1.
18th International Conference on Frontiers of Information Technology (FIT) ; : 37-42, 2021.
Article in English | Web of Science | ID: covidwho-1868539

ABSTRACT

Corona virus has spread the Covid-19 pandemic to the whole world resulting in the loss of about 3.8 million people. Nearly 156.5 million people have recovered from this disease by timely diagnostic using primary symptoms, which include lethargy caused by muscular weakness. Post Covid-19 patients also face myalgia, which is caused by the abnormal neural action potential. Electromyography (EMG) has been used for years to detect the neural communication and the action potential caused by it. Biomedical experts prefer EMG over other methods due to its ability to capture and conserve the data which helps in detecting major muscular disorders. This paper depicts multiple approaches to diagnose current Covid-19 patients or post Covid-19 patients using the EMG data of lower limb using Machine Learning. These approaches vary from each other in the form of the information conserved in the training data. The proposed method achieves the highest accuracy of 93.8% along with increasing the computational efficiency, as compared to the conventional methods. The dataset used is a publically available dataset, provided by University of California, by the name of Irvine (UCI) EMG lower limb dataset.

2.
Pakistan Journal of Medical and Health Sciences ; 16(2):1, 2022.
Article in English | EMBASE | ID: covidwho-1798531
3.
Asean Journal of Psychiatry ; 22(6):9, 2021.
Article in English | Web of Science | ID: covidwho-1431614

ABSTRACT

As of August 9, 2021, there have been around 203 million confirmed cases of coronavirus disease (2019) COVID-19, including 4.3 million deaths. Adverse psychological effects are expected to be long-lasting in vulnerable groups, especially among frontline healthcare workers, given the magnitude of the crisis. Observing strict quarantine and social distancing measures, while being an important strategy to curb the spread, have also led to a significant negative impact on mental health indicators;the long-term consequences are yet to be assessed on a global scale. A medical crisis may become a mental health crisis and the updated findings are reviewed in this paper to provide an updated brief for immunological, occupational, socioeconomic, racial/ethnic, psychological predictors while commenting on care recommendations to prevent psychological trauma from progressing to PTSD.

4.
International Conference on Sustainable Expert Systems, ICSES 2020 ; 176 LNNS:11-22, 2021.
Article in English | Scopus | ID: covidwho-1265474

ABSTRACT

Depression is a medical illness that affects the way you think and how you react. It is a serious medical issue that impacts the stability of the mind. Depression occurs at many stages and situations. With the help of classification, the stage of depression the person is in can be tried to categorize. Nowadays, many users are sharing their views on social media, and it became a platform for knowing people around us. From the data that is shared on social media, the depressing posts are being classified using machine learning techniques. With these reports collected, the depressed person might be helped from making any sudden decisions. So, in our research study, the large datasets of the people in depression during the COVID-19 pandemic situations is analyzed and not in pandemic situations. Here to analyze the data, the neural networks have been trained with the current pandemic analysis report, and it has given a prediction that the people are less likely to get depressed when they are not in a pandemic situation like COVID-19. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
Urban Climate ; 34:100729, 2020.
Article | ScienceDirect | ID: covidwho-907194

ABSTRACT

A drastic decline in the sources of emissions of pollutants under COVID-19 induced lockdown resulted in an unprecedented trends in most hazardous pollutants PM2.5, PM10 and NO2 in India. To realize the impact of lockdown in the concentrations of PM2.5, PM10 and NO2, we compared the trend of lockdown period (20nd March to 15th April) with several (3–7) years of past data in four Indian mega cities (Delhi, Pune, Mumbai, and Ahmedabad) of different micro-climate and geography. The significant reduction in the concentrations of NO2 in the ranges of ~60–65% is noticed in four megacities within the lockdown period when compared with the averaged data of past years. However, relatively low reduction in PM2.5 (~25–50%) and PM10 (~36–50%) is observed and city to city variation is found to be significant. The prevailing secondary aerosol formation and enhancement of any natural source of emissions could be some factors preventing PM2.5 levels to go down significantly. Under near negligible fossil fuel emission, contrary to the expectation, an increase in the ratio as compared to normal scenario is observed in Delhi on some days whereas on some selected days, PM2.5/PM10 ratio is found to decline significantly.

6.
Environ Res ; 191: 110121, 2020 12.
Article in English | MEDLINE | ID: covidwho-726518

ABSTRACT

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe due to its contagion nature. We hereby report the baseline permanent levels of two most toxic air pollutants in top ranked mega cities of India. This could be made possible for the first time due to the unprecedented COVID-19 lockdown emission scenario. The study also unfolds the association of COVID-19 with different environmental and weather markers. Although there are numerous confounding factors for the pandemic, we find a strong association of COVID-19 mortality with baseline PM2.5 levels (80% correlation) to which the population is chronically exposed and may be considered as one of the critical factors. The COVID-19 morbidity is found to be moderately anti-correlated with maximum temperature during the pandemic period (-56%). Findings although preliminary but provide a first line of information for epidemiologists and may be useful for the development of effective health risk management policies.


Subject(s)
Air Pollution , Coronavirus Infections , Pandemics , Pneumonia, Viral , Air Pollution/analysis , Betacoronavirus , COVID-19 , Cities , Humans , India , SARS-CoV-2 , Weather
SELECTION OF CITATIONS
SEARCH DETAIL