Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 13 de 13
Filter
1.
Review of Integrative Business and Economics Research ; 11(4):39-49, 2022.
Article in English | Scopus | ID: covidwho-2273660

ABSTRACT

Earlier work documented how COVID-19 affected the performance of the stock market indices around the world (Bieszk-Stolorz and Dmytrow, 2021;Lento and Gradojevic, 2021). Research has yet to investigate the longer-term recovery of these market indices. From a buy-and-hold perspective, this paper compares the recovery of indices in G7 countries and Hong Kong from the beginning of the pandemic in January 2020 to June 2021. The empirical results show that the null hypothesis of equal individual monthly returns in the indices of G7 countries and Hong Kong cannot be rejected. However, the null hypothesis of equal buy-and-hold returns in the indices of G7 countries and Hong Kong from January 2020 through June 2021 can be rejected, indicating that the market recovery status among the G7 countries and Hong Kong from the start of COVID-19 in January 2020 through June 2021 has been uneven and unequal. Copyright © 2022 GMP Press and Printing.

2.
IEEE Sensors Journal ; 23(2):914-921, 2023.
Article in English | Scopus | ID: covidwho-2243662

ABSTRACT

Considering the increasing growth of communicable diseases worldwide such as COVID-19, it is recommended to stay at home for patients with fewer chronic health problems. In recent times, the high chance of COVID-19 spread and the lack of an excellent remote monitoring system make the situation challenging for hospital administrators. Inspired by these challenges, in this paper, we develop a new edge-centric healthcare framework for remote health monitoring and disease prediction using Wearable Sensors (WSs) and advanced Machine Learning (ML) model, namely Bag-of-Neural Network (BoNN), respectively. The epidemic model collects the health symptoms of the patient using various a set of WSs and preprocesses the data in distributed edge devices for preparing a useful dataset. Finally, the proposed BoNN model is applied over the refined dataset for detecting COVID-19 disease at centralized cloud servers using a set of random neural networks. To demonstrate the efficiency of the proposed BoNN model over the standard ML models, the system is fine-tuned and trained over a synthetic COVID-19 dataset before being evaluated on a benchmark Brazil COVID-19 dataset using various performance metrics. The experimental results demonstrate that the proposed BoNN model achieves 99.8% accuracy while analyzing the Brazil dataset. © 2001-2012 IEEE.

3.
Lecture Notes in Networks and Systems ; 473:377-384, 2023.
Article in English | Scopus | ID: covidwho-2243546

ABSTRACT

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

4.
Pharmacology Online ; 2:277-285, 2021.
Article in English | GIM | ID: covidwho-2218762

ABSTRACT

The World Health Organization (WHO) stated the novel coronavirus (COVID-19) a global pandemic on 11th March 2020. The virus-infected patients suffered from a respiratory disease called Severe Acute Respiratory Syndrome Coronavirus 2 (SAR-CoV-2). A proteinaceous exudate, alveolar edema, and hyperplasia associated with monocytes and lymphocytes alveolar inflammatory infiltration was observed in the affected patient's lungs. Virus broadens a systemic inflammatory reaction with a cytokine release syndrome which is characterized with the aid of using unexpected growth in many pro-inflammatory cytokines especially IL-6, IL-1, and TNF-a through activated M1 macrophage phenotype. Virus block IL-6 with tocilizumab and the usage of respirator device appears to be very vital. Radioactivity is the process by which unstable atomic nucleus losses energy by radiation, mainly using alpha, beta, and gamma rays. SARS-CoV-2 affected lungs can be treated by a low dose of radiotherapy. It was found that minute dose chest radiation therapy can be able to wean patients off a ventilator as it can reduce inflammation inside the lungs of severely infected COVID-19 patients. Numerous such clinical trials are underway and researchers may work to cure the COVID-19 lung infections by radiotherapy.

5.
International Conference on Innovative Computing and Communications, Icicc 2022, Vol 1 ; 473:377-384, 2023.
Article in English | Web of Science | ID: covidwho-2094513

ABSTRACT

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed.

6.
Review of Integrative Business and Economics Research ; 11(1):51-62, 2022.
Article in English | Scopus | ID: covidwho-1871433

ABSTRACT

Past research using event-study methodology suggested that major political events such as parliamentary elections and Brexit affected the abnormal returns of equities. However, past research did not investigate the impact of the declaration of national emergency by President Trump during the Covid-19 pandemic on the abnormal returns of Fintech digital payment companies. Because of the pandemic and its subsequent social distancing measures, many governments, businesses, schools, and organizations have shut down their brick-and-mortar presence and moved their operations online. Fintech companies are expected to benefit from such migration to online operations. This study hypothesizes that Fintech companies enjoy higher abnormal returns in response to the national emergency declaration during the Covid-19 pandemic. However, this study’s findings cannot reject the null hypothesis that the abnormal returns of Fintech digital payment companies are zero in the week following the declaration of national emergency. This finding implies that the US equity markets are efficient in the dissemination of the news about the national emergency as there is no statistically significant difference between the actual returns of the digital-payment equities and their predicted returns based on the CAPM model. Copyright © 2022 GMP Press and Printing

7.
IEEE Sensors Journal ; 2022.
Article in English | Scopus | ID: covidwho-1831850

ABSTRACT

Considering the increasing growth of communicable diseases worldwide such as COVID-19, it is recommended to stay at home for patients with fewer chronic health problems. In recent times, the high chance of COVID-19 spread and the lack of an excellent remote monitoring system make the situation challenging for hospital administrators. Inspired by these challenges, in this paper, we develop a new edge-centric healthcare framework for remote health monitoring and disease prediction using Wearable Sensors (WSs) and advanced Machine Learning (ML) model, namely Bag-of-Neural Network (BoNN), respectively. The epidemic model collects the health symptoms of the patient using various a set of WSs and preprocesses the data in distributed edge devices for preparing a useful dataset. Finally, the proposed BoNN model is applied over the refined dataset for detecting COVID-19 disease at centralized cloud servers using a set of random neural networks. To demonstrate the efficiency of the proposed BoNN model over the standard ML models, the system is fine-tuned and trained over a synthetic COVID-19 dataset before being evaluated on a benchmark Brazil COVID-19 dataset using various performance metrics. The experimental results demonstrate that the proposed BoNN model achieves 99.8% accuracy while analyzing the Brazil dataset. IEEE

8.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4396-4400, 2021.
Article in English | Scopus | ID: covidwho-1730863

ABSTRACT

The COVID-19 pandemic has significantly altered our way of life. Physical, social interactions are being steadily replaced with virtual connections and remote interactions. Social media platforms such as Facebook, Twitter, and Instagram have become the primary medium of communication. However, being relegated to a solely online presence has had a major impact on the mental health of users since the onset of the pandemic. The present study aims to identify depressed Twitter users by analyzing their tweets. We propose a deep learning model which stacks a bidirectional LSTM layer along with a CatBoost Algorithm layer to classify tweets and detect depression. The results show that the proposed model outperforms standard machine learning approaches to classification and that there was a definite rise in depression since the beginning of the pandemic. The study's primary contribution is the novel deep learning model and its ability to detect depression. © 2021 IEEE.

9.
Pharmacologyonline ; 2:277-285, 2021.
Article in English | Scopus | ID: covidwho-1602500

ABSTRACT

The World Health Organization (WHO) stated the novel coronavirus (COVID-19) a global pandemic on 11th March 2020. The virus-infected patients suffered from a respiratory disease called Severe Acute Respiratory Syndrome Coronavirus 2 (SAR-CoV-2). A proteinaceous exudate, alveolar edema, and hyperplasia associated with monocytes and lymphocytes alveolar inflammatory infiltration was observed in the affected patient’s lungs. Virus broadens a systemic inflammatory reaction with a cytokine release syndrome which is characterized with the aid of using unexpected growth in many pro-inflammatory cytokines especially IL-6, IL-1, and TNF-α through activated M1 macrophage phenotype. Virus block IL-6 with tocilizumab and the usage of respirator device appears to be very vital. Radioactivity is the process by which unstable atomic nucleus losses energy by radiation, mainly using alpha, beta, and gamma rays. SARS-CoV-2 affected lungs can be treated by a low dose of radiotherapy. It was found that minute dose chest radiation therapy can be able to wean patients off a ventilator as it can reduce inflammation inside the lungs of severely infected COVID-19 patients. Numerous such clinical trials are underway and researchers may work to cure the COVID-19 lung infections by radiotherapy. © 2021, SILAE (Italo-Latin American Society of Ethnomedicine). All rights reserved.

10.
Ieee Sensors Journal ; 21(18):20504-20511, 2021.
Article in English | Web of Science | ID: covidwho-1472295

ABSTRACT

The outbreak of the coronavirus is in its growing stage due to the lack of standard diagnosis for the patients. The situation of any populous area in a geographic location is very critical due to the quick virus spread from an infected individual to the rest. Currently, medical administration is at a crisis point due to the rapidly increasing number of cases and limited medical facilities. Thus, it is time to explore and design an intelligent model to monitor patient health symptoms remotely and predict and detect the abnormality of the patient's health status in quick succession. Thus, the health status of a coronavirus-affected patient can be identified via a well-adjusted predictive model by analyzing the observed parameters of the health. In the proposed model, an Auto-regressive Integrated Moving Average is incorporated to design a predictive model to find the kth forecast of the observed health symptoms of a patient, and Akaike Information Criteria based selection is introduced to find the current best-fit prediction model. Further, the features are extracted from the forecast over each symptom to find a pattern of each patient, and the patterns are learned by the K-Means algorithm to detect the symptomatic and asymptomatic patient intelligently. To demonstrate the efficiency of the proposed model, we evaluate the model using a synthetic dataset, generated from the health symptoms of 400 patients and compare the performance of the model with the standard methods.

11.
Journal of Public Health and Development ; 19(2):180-187, 2021.
Article in English | Scopus | ID: covidwho-1281158

ABSTRACT

The unprecedented outbreak of COVID-19 has significantly increased the volume of biomedical waste while the number of waste management workers halved in the state of lockdown in Bangladesh. The improper management of biomedical waste might facilitate the spreading of COVID-19 as SARS-COV-2 could survive on these wastes for variable durations. In this article, we presented the impact of COVID-19 on biomedical waste generation and management in Bangladesh and the waste disposal practices in laboratories. We also presented the practice of waste management in two COVID testing laboratories in Bangladesh. About 109 laboratories are working on the detection of COVID-19 through Real-time Reverse Transcriptase PCR. In April 2020, at least 14,500 tons of medical wastes were produced throughout the country which was almost double the amount produced previously (7756.3 tons per month earlier). COVID testing laboratories used biohazard bags for disposing of wastes and autoclaved these waste-filled bags before releasing them from the laboratory. These bagged wastes are collected by the City Corporation (local city authority) workers for final disposal. However, proper management of excess volumes of biomedical waste requires multidisciplinary collaboration of different stakeholders including the government, hospital administration, laboratory workers, researchers, and policymakers. © 2021, Mahidol University - ASEAN Institute for Health Development. All rights reserved.

12.
Journal of the American Geriatrics Society ; 69(SUPPL 1):S166-S167, 2021.
Article in English | EMBASE | ID: covidwho-1214855

ABSTRACT

CASE REPORT: A 79-year-old woman with a history of hypertension, anxiety, chronic pain, fibromyalgia, peripheral neuropathy, prior right AKA, and CVA was admitted with acute osteomyelitis and cellulitis of 3rd toe. Initially she was tachycardic at 140, and EKG showed sinus tachycardia. Diastolic BP was around 100. The HR slowed after rehydration, and BP normalized with analgesia. CBC showed mild leukocytosis with neutrophilia of 74%. ESR and CRP were elevated at 56 and 146, respectively. She tested negative for COVID-19, RSV and influenza. CXR showed low lung volumes. Blood cultures were drawn before initiation of antibiotics (Vancomycin and Cefepime). Orthopedics evaluated the patient. On day 1, the patient was found noted to be confused. CT of head showed extensive periventricular and subcortical white matter hypodensities. MRI of brain showed no acute abnormalities. MRA showed no abrupt vessel occlusion of the circle of Willis. Aspirin and statin were recommended by Neurology. On day 6, the Geriatrics team was consulted. The patient was minimally verbal, not following commands. She did have perseveration and answered with 'What' or 'Wait' to most questions. Blood cultures returned showing no growth, and the mild leukocytosis had resolved. Her acute delirium was thought to be multifactorial, due to infection, pain and also possibly some contributing medication (probably Cefepime). Patient's home dose of Gabapentin and Cymbalta were halved and scheduled Tylenol was recommended. Cefepime was discontinued and Rocephin was started. On day 7, the patient underwent left 3rd toe amputation. Her mental status improved and she became more attentive and was able to answer orientation questions correctly by day 8. On day 9, she was back to her baseline mental status. She experienced no worsening of her delirium post-op. CONCLUSION: This case focuses on the importance of recognizing the effects of antibiotics on cognition and mental status in geriatric patients. Cefepime induced encephalopathy can occur even with normal renal function and can lead to marked changes in mental status. For patients presenting with expressive aphasia and changes in mentation, prompt removal of the delirium-contributing drug may allow for rapid resolution of the delirium.

13.
Pacific Business Review International ; 13(4):46-51, 2020.
Article in English | Web of Science | ID: covidwho-1070138

ABSTRACT

The recent Covid 19 pandemic has affected the entire globe in the last few months. It has resulted into lockdown across the country w.e.f., 24th March, 2020.Due to lockdown, it has been anticipated that students might be massively suffered and as a result of that MHRD and UGC have issued notification asking teachers to take classes using different online teaching learning tools. Zoom has been a big success in this period. But due to the fact that it does not safeguard the privacy of the users, many teachers have shifted to Google classroom, hangouts or other tools. It has also been witnessed that teachers have started to use whatsApp for sharing of study materials or presentation slides. Therefore, the current study endeavors to know how teachers are using whatsApp as an e-learning application during All India lockdown period due to covid 19 pandemic. The study also focuses on to stimulate the relationship between different advantages arising out of whatsapp and its effectiveness as a tool of e-learning. A total of 202 college and university students from different parts of the country have participated in the study as sample respondents.

SELECTION OF CITATIONS
SEARCH DETAIL