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This research investigates how the uncertainty caused by the COVID-19 pandemic has affected digital banking usage in India. The study is made by utilizing a panel of data consisting of 108 firm-month observations during covid period from 2020 to 2022, with data mainly collected to analyze the impact of COVID-19 uncertainty. Most of the determinants were collected from the RBI data website. The main emphasis of this study is on the utilization of digital banking services in the context of the pandemic, and the research assesses the factors that have influenced this trend, including the number of physical bank branches, the utilization of debit and credit cards at automated teller machines (ATMs) and points of sale (PoS), as well as the level of economic policy uncertainty (EPU). The analysis was conducted using panel regression analysis, a suitable method for handling the error components in the model that are either fixed or random. The findings indicate that the uncertainty caused by the pandemic has had a negative impact on the use of digital banking services. Additionally, the study highlights that the usage of debit and credit cards at PoS has significantly contributed to promoting the progress of digital banking services during the pandemic. Overall, this study provides valuable insights into how digital banking services have evolved during a period of significant uncertainty and disruption. © 2023 by the authors.
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As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset. © 2023 IEEE.
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Introduction: Acute respiratory disease, Coronavirus Disease 2019 (COVID-19) is an infectious and potentially fatal respiratory disease. Increase in the inflammatory response, hypoxia, immobilisation are suggested mechanisms of procoagulant state. Deep Vein Thrombosis (DVT) and pulmonary emboli are common and often silent. Venous duplex ultrasound help in determination of the presence, extent, age of the thrombus and its attachment to venous wall. Aim: To evaluate the prevalence of DVT by colour doppler ultrasound in lower limbs of mild to severe clinical categories of COVID-19 patients. Materials and Methods: A time-bound, hospital-based prospective cohort study was conducted in the Department of Radiodiagnosis, MY Hospital, Indore, Madhya Pradesh, India, between March 2021 and February 2022. Study comprised 2200 cases of COVID-19 positive patients with elevated D-dimer levels i.e., >0.5 ng/mL and colour doppler imaging for lower limb. The clinical (co-morbidities, clinical severity) and radiological data (compressibility, colour flow) were studied and analysed using Statistical Package for the Social Sciences (SPSS) software version 25.0. Results: In the present study, there were 1144 (53%) males and 1056 (47%) females. Out of 2200 patients, 792 (36%) patients showed presence of DVT. The most prevalent age group was 36-55 years having 506 (63.9%) patients. Majority of DVT positive patients were suffering with hypertension and diabetes i.e., 261 (33%) and 372 (47%) patients, respectively. Most commonly affected vein in DVT was Common Femoral Vein (CFV) in 704 (88.9%) patients. Superficial veins thrombosis was also associated with DVT affecting Short Saphenous Vein (SSV) in 439 (55.4%) patients and Great Saphenous Vein (GSV) in 221 (27.9%) patients. Conclusion: There was a high prevalence of DVT among COVID-19 positive patients. Colour doppler ultrasound has provided an excellent aid in the diagnosis of DVT.
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Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007-2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.
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Online learning has gained a tremendous popularity in the last decade due to the facility to learn anytime, anything, anywhere from the ocean of web resources available. Especially the lockdown all over the world due to the Covid-19 pandemic has brought an enormous attention towards the online learning for value addition and skills development not only for the school/college students, but also to the working professionals. This massive growth in online learning has made the task of assessment very tedious and demands training, experience and resources. Automatic Question generation (AQG) techniques have been introduced to resolve this problem by deriving a question bank from the text documents. However, the performance of conventional AQG techniques is subject to the availability of large labelled training dataset. The requirement of deep linguistic knowledge for the generation of heuristic and hand-crafted rules to transform declarative sentence into interrogative sentence makes the problem further complicated. This paper presents a transfer learning-based text to text transformation model to generate the subjective and objective questions automatically from the text document. The proposed AQG model utilizes the Text-to-Text-Transfer-Transformer (T5) which reframes natural language processing tasks into a unified text-to-text-format and augments it with word sense disambiguation (WSD), ConceptNet and domain adaptation framework to improve the meaningfulness of the questions. Fast T5 library with beam-search decoding algorithm has been used here to reduce the model size and increase the speed of the model through quantization of the whole model by Open Neural Network Exchange (ONNX) framework. The keywords extraction in the proposed framework is performed using the Multipartite graphs to enhance the context awareness. The qualitative and quantitative performance of the proposed AQG model is evaluated through a comprehensive experimental analysis over the publicly available Squad dataset. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
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BACKGROUND: Persons with Coronavirus Disease 2019 (COVID-19) infection have an increased risk of pregnancy-related complications. However, data on acute cardiovascular complications during delivery admissions remain limited. OBJECTIVE(S): To determine whether birthing individuals with COVID-19 have an increased risk of acute peripartum cardiovascular complications during their delivery admission. METHOD(S): This population-based retrospective cohort study used the National Inpatient Sample (2020) by utilizing ICD-10 codes to identify delivery admissions with a diagnosis of COVID-19. A multivariable logistic regression model was developed to report an adjusted odds ratio for the association between COVID-19 and acute peripartum cardiovascular complications. RESULT(S): A total of 3,458,691 weighted delivery admissions were identified, of which 1.3% were among persons with COVID-19 (n=46,375). Persons with COVID-19 were younger (median 28 vs. 29 years, p<0.01) and had a higher prevalence of gestational diabetes mellitus (GDM), preterm births and Cesarean delivery (p<0.01). After adjustment for age, race/ethnicity, comorbidities, insurance, and income, COVID-19 remained an independent predictor of peripartum cardiovascular complications including preeclampsia (aOR 1.33 [1.29-1.37]), peripartum cardiomyopathy (aOR 2.09 [1.54-2,84]), acute coronary syndrome (ACS) (aOR 12.94 [8.85-18.90]), and cardiac arrhythmias (aOR 1.55 [1.45-1.67]) compared with no COVID-19. Likewise, the risk of in-hospital mortality, AKI, stroke, pulmonary edema, and VTE was higher with COVID-19. For resource utilization, cost of hospitalization ($5,374 vs. $4,837, p<0.01) was higher for deliveries among persons with COVID-19. CONCLUSION(S): Persons with COVID-19 had a higher risk of preeclampsia, peripartum cardiomyopathy, ACS, arrhythmias, in-hospital mortality, pulmonary edema, AKI, stroke, and VTE during delivery hospitalizations. This was associated with an increased cost of hospitalization. Keywords: COVID-19, Pregnancy, GDM, PCOS, Preeclampsia, CVD, Cardiovascular Disease Abbreviations: COVID-19: Coronavirus disease-2019, GDM: Gestational Diabetes Mellitus, PCOS: Polycystic Ovary Syndrome, National Inpatient Sample: NIS, AHRQ: Agency for Healthcare Research and Quality, HCUP: the Healthcare Cost and Utilization Project Funding and Conflicts of Interest Dr. Michos reports Advisory Board participation for Amgen, AstraZeneca, Amarin, Bayer, Boehringer Ingelheim, Esperion, Novartis, Novo Nordisk, and Pfizer. The remaining authors have nothing to disclose.Copyright © 2023
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Background: Surgical resection remains the mainstay for early breast cancer. However, older patients with multiple co-morbidities may be deemed unsafe for general anaesthesia (GA). The Covid-19 pandemic necessitated some such surgery under local anaesthesia (LA) especially those who lacked anti-hormonal bridging therapy option. We present a retrospective study comparing outcomes following breast conserving surgery (BCS) under LA and GA. Method(s): 31 patients under LA (April 2018-March 2022) were compared with 31 age-matched patients under GA during the same period. Main outcomes were length of hospital stay and rates of margin positivity, re-operation, and post-operative complications within 1 month (including wound infections, seromas needing >=3 aspirations). Statistical analysis (with R-4.2.2) used two-tailed test with significant p-value (<0.05). Result(s): Only 5 LA cases were performed in the 2 years prior to first UK Covid-19 lockdown (March 2020), whilst 26 cases were performed in the 2 years after. [Formula presented] Conclusion(s): The number of BCS cases under LA increased five-fold following Covid-19 pandemic. Outcomes under LA were no worse than under GA. BCS under LA can allow BCS in patients unfit for or unwilling to have GA, especially older patients. Dedicated lists for BCS under LA may reduce need for resources such as hospital beds and overnight stays in the current resource and financially constrained health-care system.Copyright © 2023
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Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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According to WHO, Skin Infection is very common but sometimes very serious and affects a large no population all over the world. Monkeypox, Chickenpox, and Measles are the major infectious disease that causes skin infection all over the world. It has been obverse that the cases of Monkeypox have drastically increased as an effect of Covid 19. This infection has spread easily and exponentially that cause serious health issues in many underdeveloped and developing countries. Some time it has been observed that people are not able to properly classify the type of skin infection well in time, which can be a main reason of serious health issues. So, it became important to propose an effective classification of Skin Disease. In this paper the authors have proposed an effective classification of Skin Disease using Deep Learning Techniques. This approach will help in classification of chicken pox, measles, and monkeypox through skin images. The authors have utilized Monkeypox Skin Images Dataset (MSID) dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The best algorithms with maximum accuracy and other parameters are Xception, EfficientNetV2L, and EfficientNetV2M, and CNN, VGG16, and VGG19 are the least favored algorithms for this research. © 2023 IEEE.
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Mental Health Issues are a hidden pandemic which will emerge in the upcoming years. As the world witnessed COVID-19 pandemic and went into lockdown, the cases of Depression, Anxiety and Stress skyrocketed than ever before. This has given rise to the need for exploring the interdisciplinary field of Artficial Intelligence and Psychometry. In this paper, we propose compare various machine learning and ensemble learning methods, on the survey dataset comprising of the DASS-42 Psychometric Test Results and Demographic information. Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost) are used to classify the level of Depression, Anxiety and Stress into normal, mild, moderate, severe and extremely severe categories. In our experiments on the dataset, Support Vector Machine outperformed and reached a final F1-measure of 94%, 95% and 91% in the prediction of Depression, Anxiety and Stress, respectively. © 2022 IEEE.
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Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins' explosive behavior during 2013-2022 including the most volatile COVID-19 pandemic and Russia-Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins' opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day's opening price. © 2023 World Scientific Publishing Company.
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Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Background: Due to its physiologic immune suppression, pregnancy is a vulnerable time for severe respiratory infections including COVID-19. However, information regarding the effect of COVID-19 during pregnancy is limited. Objectives: To study the clinical profile of patients suffering from coronavirus disease-2019 (COVID-19) during pregnancy and to evaluate the effect of COVID-19 on maternal, perinatal, and neonatal outcomes. Methodology: This is a cross-sectional observational study over a period of one year from June 2020 to May 2021, in Level-3 Covid facility in Ghaziabad. All pregnant females with confirmed positive for Corona virus infection admitted to the covid ward under the department of Obstetrics & Gynecology were included in the study. Results: A total of 233 pregnant women were included in the study. Maximum patients were from age group 21-30 years (53.2), multigravida (62.7%), and presented in the third trimester (80.7%). On admission, 198 patients (85%) had no covid related symptoms and only three patients had severe symptoms requiring ICU care. Total 102 patients delivered (43.77%), out of whom 40 had a normal vaginal delivery and 62 had a cesarean section. The incidence of preterm birth was 22.5% and maternal death was in three patients (1.3%). Conclusion: The common presentation of COVID-19 during pregnancy is either a mild disease or even asymptomatic. The maternal outcomes observed in late pregnancy and fetal and neonatal outcomes appear good in most cases. Further studies are required to know long-term outcomes and potential intrauterine vertical transmission. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.
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With vaccines and treatments now available and yet more on the horizon, the first major manufacturing hurdles have been crossed. However, the finish line is still in the distance. As organizations such as drug manufacturers around the world continuously evaluate how to effectively operate, the pandemic has provided a hard reality check. Companies have seen the necessity of strong sourcing/procurement functions to enable business operations. For at least the next few years, we anticipate constraints on aseptic fill/finish and potentially API manufacturing capacity. These constraints can be mitigated by having staffing flexibility when needs arise, enabling rapid technology transfers and adding surge capacity utilization. In addition, truly partnered approaches of pharmaceutical companies and their CDMO suppliers need to be the standard for managing and operating with speed, quality, and safety rigor to meet the needs of the global impact of the pandemic and to ensure focus on the end goal. Finally, maintaining strong relationships with regulatory bodies around the world enables a strong public-private partnership both during and post pandemic, with patients as the shared motivational force to execute and deliver. Copyright © 2021, Drug Delivery Technology. All rights reserved.
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Background: With the ongoing pandemic of COVID-19, there has been a rapid upsurge in cases of rhino-orbital-cerebral mucormycosis (ROCM). It is an opportunistic fungal infection associated with high morbidity and mortality. Rapid and appropriate application of clinical and radiological methods is crucial for early diagnosis, to limit the associated morbidity and improve post-treatment outcomes. In our study, we analyzed imaging features, common sites, and the extent of infection in patients suffering from ROCM. Results: The majority of the patients were either diabetics or developed uncontrolled blood glucose levels during COVID-19 infection. 79.17% of patients had a history of treatment with steroid therapy. Headache and facial pain were the most common clinical features seen in 76.67% and 60% of patients, respectively. Maxillary and ethmoid sinuses were commonly involved. The most common extra-sinus site of involvement was periantral fat and orbit, seen in 91 (75.83%) and 84 (70%) patients, respectively. Bone erosion or marrow edema was seen in 72 (60%) patients. Intracranial extension in the form of meningitis, cavernous sinus thrombophlebitis/thrombosis, and brain abscess were seen in 20%, 10%, and 3.3% of patients, respectively. MRI-based staging showed that 24.7% of patients had stage I, 5.83% had stage II, 50% had stage III, and 20% had stage IV disease. Conclusion: The spread of COVID-19-associated rhinomucormycosis to extra-sinus sites is common, which can be detected adequately on MRI. The radiological signs of invasion and devitalization of tissues are crucial for the early diagnosis of ROCM. © 2022, The Author(s).
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Introduction: Patients on The Cancer pathway should be investigated on The 2 weeks wait pathway, but COVID-19 pandemic had universal impact on The Healthcare systems. one of The main worries was The impact on Cancer patients due to delayed diagnosis and management. Our study looks at The timeframe of investigations for Colorectal Cancer during The second wave of The pandemic compared to pre COVID time. Method(s): Retrospective study looking at The waiting time to investigate patients with +ve qFIT test during The second wave of pandemic (from November 2020 till March 2021). Result(s): During this period 150 patients had +ve qFIT test, The main presenting symptom was Change in bowel habits. 90 patients were investigated with colonoscopy, only 16 (17%) patients had The colonoscopy done within 2 weeks from The qFIT result. 23 patients had colonoscopy 2-3 weeks from The result. 30 patients (33%) had The colonoscopy between 3-4 weeks, and 21 patients had to wait between 1-6 months to have The colonoscopy. Out of The 150 patients, 60 patients were investigated primarily with CT scan or CT colon. Conclusion(s): During The COVID-19 pandemic, majority of patients in our trust were investigated within one month of +ve qFIT test but yet there was some delay in carrying out The investigations compared to The normal pathway and more patients had CT scans as primary investigations before being referred for colonoscopy.
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Introduction: When FIT is used for symptomatic patients presenting to primary care a positive result is considered >=10 mcgHb/gStool. When qFIT is used for The asymptomatic screening population (i.e NBCSp) a positive result is >=120mcgHb/gStool. During COVID-19 Pandemic The 120 cut of f was used in some of The hospitals to triage patients who require further investigations for either colonoscopy or CT scan. Method(s): Retrospective cohort study done in General district hospital comparing The pathology identified in patients with Qfit results>120 and patients with result <120. Result(s): In The period between July 2020 and November 2021, 448 patients had +ve qFIT result (>=10 micrograms).In The first group, 340 patients had qFIT result <120. 191 patients had colonoscopy with 8 confirmed Colorectal cancer, and 137 patients had CT/CT colon with only 1 patient was found to have Colorectal cancer. Overall 2.6% of The patients had cancer. While in The second group, 108 patients had qFIT>120. 69 patients had colonoscopy with 9 confirmed cancer. The rest of The patients had CT/CT colon with 2 patients showing features of malignancy. Overall 10.1% of The patients had cancer. Conclusion(s): The incidence of Colorectal Cancer in patients with qFIT result >120 is much higher than The other group, but The incidence of Colorectal Cancer in patients with qFIT<120 is still significant and The patients shouldn't be discharged without investigations.