Covid-19 Detection and Classification using Transfer learning with XGboost

The 2019 new corona virus (COVID-19), with a genesis phase in China, has dispersed apace amid individuals subsisting in distinct nations and is rising toward about twelve lakh cases in the balance as per the intuition of the European center for Health Security and Communicable diseases and ECDC. There is a foreordained figure of COVID-19 trial caskets attainable in medical centers because of the escalating cases in day-to-day life. In this way, it is important to execute a programmed location framework as a snappy elective conclusion alternative to forestall COVID-19 transmitting between peoples. In this examination, three disparate Convolutional neural system- based models (XGBOOST/LIGHTGBM, Inception-ResNetV2 and InceptionV3) have been put forward for the whereabouts of coronavirus and pneumonia contaminated convalescent by harnessing thoracic radiographic screening. Receiver Operating Characteristics (ROC) investigations and disordered networks by those tripartite models are bestowed and deteriorated by exploiting 5-superimpose traverse accredit. Contemplating the demonstration outcome obtained, it is perceived that the pre- prepared XGBOOST/LIGHTGBM model accouters the most upraised characterization execution with 98.6% exactness amongst the other two propounded models (96% correctness for InceptionV3 and 85% exactness for Inception-ResNetV2). © 2023 IEEE.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predicting Happiness Score during Covid-19 Using Machine Learning

The COVID-19 pandemic has been a bad dream for many people. People suffered from job losses, leading to a low level of happiness. Happiness is the key to a healthy life, and predicting the happiness score of 156 countries will give the idea of a happiness index around the world during the COVID-19 pandemic. An open dataset of the happiness index has been picked from the World Happiness Report, which is manifested already in a United Nations conference. The available dataset splits into training data and testing data, respectively. The training data have fitted into different machine learning algorithms. After that, the prediction score has observed based on testing data. After applying a large number of algorithms, the highest accuracy of the resulting regression model is 97 percent. © 2022 IEEE.

Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Role of Dietary Flavonoids in Preventing COVID-19 Infection and Other Infectious Diseases: A Mini Review

Flavonoids are a large group of naturally occurring polyphenolic compounds that are almost universally present in various plant parts such as fruits, berries, leaves, and tubers. These compounds are synthesized in plants in reaction to environmental stressors such as microbial infections. The antioxidant properties in these flavonoids provide us with numerous health benefits. They can be extracted from said natural sources via methods such as maceration and boiling all the way to advanced methods such as microwaves and ultrasounds.Numerous studies have been conducted to research the protective role that flavonoids can play in preventing infectious diseases in humans. The present modalities of treating such infectious diseases rely solely on chemotherapeutic agents and adjunctive therapies such as palliative and supportive care. These chemotherapeutic agents, primarily antibiotics, cause a degeneration of our immunity and an increased susceptibly to several other diseases. Thus, it is crucial that our methods in dealing with infections focus on prevention. This can be achieved by strengthening our immune system, which is the primary line of defense against such diseases. Flavonoids can help boost our immunity, fight infections, and decrease the incidence of antibiotic resistance.Hence, these natural compounds are being largely studied and used as nutraceuticals to supplement our daily diet and successfully reduce the occurrence of major infectious diseases in our body. © 2022. The Author(s).

Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Social Distance Surveillance Using Deep Learning

COVID-19 disease discovered from the novel corona virus can spread through close contact with a COVID-19 infected person. One of the measures advised to contain the spread of the virus is to maintain social distancing by minimizing contact between potentially infected individuals and healthy individuals or between population groups with high rates of transmission and population groups with no or low-levels of transmission. Motivated by this practice, we propose a deep learning framework for social distance detection and monitoring using surveillance video that can aid in reducing the impact of COVID-19 pandemic. This work utilizes YOLO, Detectron2 and DETR pre-trained models for detecting humans in a video frame to obtain bounding boxes and their coordinates. Bottom-centre points of the boxes were determined and were then transformed to top-down view for accurate measurement of distances between the detected humans. Based on the depth of each bottom-centre point estimated using monodepth2, dynamic distance between pairs of bounding boxes and corresponding distance threshold (safe distance) to prevent violation of social distancing norm were computed. Bounding boxes which violate the distance threshold were categorized as unsafe. All the experiments were conducted on publicly available Oxford Town Center, PETS2009 and VIRAT dataset. Results showed that Detectron2 with top-down view transformation and distance thresholding using pixel depth estimation outperformed other state-of-the-art models. The major contribution of this work is the estimation and integration of variable depth information in obtaining the distance threshold for evaluating social distances between humans in videos. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Correlation based Threshold Networks of Dow Jones stocks of USA: An Econophysics Approach

We have investigated the time series of constituents of the Dow Jones Industrial Average (DJIA) for a period of 18 years (2000-2018). DJIA is a dominant stock market index comprising of thirty US based companies. We have applied the Random Matrix Theory (RMT), complex network analysis and hierarchical clustering techniques to extract out the information from the time series of DJIA stocks. The impact of sub-prime crisis of 2008(FC08) on structure and dynamics of network of DJIA stocks is studied by diving the periods under consideration into three distinct periods;pre crisis (PRC), during crisis (DUC) and post crisis (POC) on the basis of volatility. The RMT analysis shows that data analyzed contain important information. Network analysis reveals high correlation among the stocks in the DUC period. The MST and hierarchical clustering techniques support the results of RMT analysis. Degree centralities, closeness centralities and clustering coefficients of DJIA networks increases in DUC period. High correlation and closeness among stocks in DUC period is depicted in various analyses. The dynamic analysis is also carried out which detect various extreme events such as Covid-19. In conclusion, investigation shows that during the period of crisis, there are significant changes in the structure and dynamics of DJIA network. The findings of investigation can be utilized as risk indicator and detection of such crises in future. © 2022. School of Science, IHU. All rights reserved.

Emergency Pain Management of Untreated Pulpitis during COVID-19 Lockdown by Telephonic Communication

To evaluate the beneficial effects of orally administered corticosteroids in alleviating the pain of symptomatic pulpitis. Materials and Methods: Out of the 87 patients who contacted the expert panel telephonically during the period of COVID-19 lockdown, 55 patients complaining of moderate to severe dental pain were included in the study and thus advised to take oral tablets of paracetamol 650 mg postmeal thrice a day for 3–5 days along with a single oral dose of 4 mg of dexamethasone. The patients were asked to report their pain status after every 24 hours for at least 72 hours. In case of severe pain not controlled by these medicines even after 3 days, two tablets of dispersible ketorolac tromethamine (10 mg) were advised once on the fourth day, followed by a single tablet three times a day for another 3 days. In case the pain did not subside within 3 days of taking the second line of treatment, or there was a development of swelling/lymphadenopathy, the patient was advised to get the tooth extracted. Results: Out of 55 patients taking a single dose of dexamethasone, 47 (85.45%) patients reported a “significant” reduction of pain within 24–72 hours. The remaining eight patients (14.55%) in which severe pain was not controlled by paracetamol and dexamethasone even after 3 days, two tablets of ketorolac tromethamine (10 mg) were advised. Six patients (75%) reported a “significant” reduction in the pain, while two (25%) patients still in pain were advised tooth extraction and were referred to the emergency department of the tertiary care center. Conclusion: For the pain felt by patients diagnosed with symptomatic irreversible pulpitis, systemic corticosteroids administration is an adequate strategy in controlling pain for up to 48–72 hours, the time during which the pain is felt most. © The Author(s). 2022.

Kloman Metre: An EMD-Based Tool for Triaging Diseases Leading to Lung Infections Including COVID-19

The digital revolution can help developing countries to overcome the problem of limited healthcare infrastructure in developing nations such as India. The COVID-19 pandemic has shown the urgency of integration of digital technologies into healthcare infrastructure. In order to solve the issue of lack of trained healthcare professionals at public health centres (PHCs), researchers are trying to build tools which can help to tag pulmonary ailment within a fraction of second. Such tagging will help the medical community to utilize their time more efficiently. In this work, we have tried to assess the “lung health” of patients suffering from a variety of pulmonary diseases including COVID-19, tuberculosis and pneumonia by applying Earth Mover’s Distance algorithm to the X-ray images of the patients. The lung X-ray images of patients suffering from pneumonia, TB and COVID-19 and healthy persons are pooled together from various datasets. Our preliminary data based upon 100 random images depicting each type of lung disease such as COVID-19, tuberculosis and pneumonia revealed that patients suffering from tuberculosis have the highest severity as per the values obtained from the EMD scale. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Headwinds across the Balkans: economic growth slows down

This paper provides comparison of economic growth across the Balkans in terms of gross domestic product (GDP) growth. The economies of Western Balkans are suffering, supply chains are collapsing, and remittances may be cut off. The situation is no better among EU Members of the Balkans. In The consequences of the health crisis will be felt much more. Unemployment will rise and domestic demand has also suffered severely. Pandemic-related restrictions have impaired the functioning of the transport and supply chains. Over the first half of 2020, the impact of COVID-19 left its mark on the region's palm oil imports. With the exception of Greece, all major importers recorded a drop in demand. However, economic experts foresee strong recovery from 2021. With the upturn, opportunities for palm oil will return. The Malaysian Ministry of Plantation Industries and Commodities projects additional exports of 300,000 tonnes of processed palm oil to the Balkans.

Face Mask Detection Using LabView1

Rapid worldwide spread of Corona virus Disease 2019 (COVID 19) has resulted in a global pandemic. In present scenario due to covid-19, the mask has been an important part of our live for our safety as well as for the others safety so there is a need for efficient face mask detection applications in crowded areas like shopping malls, public transportation etc. To ensure safety of the people in the surroundings. Face Mask Detection using NI LabVIEW. In this project a real-time system is developed to detect whether the person is wearing a mask or not by acquiring a real-time image of him through a Camera. The main challenges in detecting the mask are there are masks with various colours and patterns and secondly the background, light intensity are also the factors that affect the result. So, all these factors should be taken into consideration while developing the system in real-time. This system used for this application consists of vision development module. Vision development module helps to develop applications for machine vision and image processing applications we can use it with LabVIEW for real- time systems. A camera with good pixel quality is used for image acquisition. The captured image is of RGB format, it is difficult to analyse the image in this format, so it undergoes colour plane extraction in this only a single plane of the image is considered which separates the mask from surroundings and results in a grey scale image for further processing. The image later is compared to a custom-made template dataset using pattern matching algorithm from vision assistant which helps to detect the mask region. overlaying techniques are used to highlight the mask region which shows that the person is wearing the mask. © 2021. All Rights Reserved.

Impact of Pandemic on Women Health Indicators in a Tertiary Health Centre of Bihar

BACKGROUND This study was conducted to analyse the impact of pandemic on healthcare, evaluate the negative psychological behaviour towards health professionals and study the effect of Covid-19 infection on hospital avoiding attitude of female patients. METHODS This was a case-control study conducted in Department of Obstetrics and Gynaecology at Patna Medical College and Hospital, Patna, Bihar. Study period was from 01st April 2020 to 30th September 2020. All the patients except Covid-19 positive cases, coming to Gynaecology Outpatient Department (GOPD), antenatal care (ANC) and labour room emergency (LRE) were included in the study. Patients who were seen from April 2019 to September 2019, total of 20,961 were in "pre Covid-19" control group, while patients seen from April 2020 to September 2020, a total of 8,859 were in "during Covid-19" case group. Records of all health parameters for patients were reviewed, and then divided into two groups as patient input indicators and healthcare efficiency indicators. Number of patients visiting GOPD, ANC and admitted in LRE comprised patient input indicators (implying hospital avoiding attitude) while delivery rate, dilation and evacuation (D & E) rate, stillbirth rate and mortality rate comprised healthcare efficiency indicators. RESULTS Overall patients visiting the hospital dropped down from 21,361 to 8859 (by 58.5 %);GOPD patients reduced by 74 % while total ANC patients reduced by 44 %;and total LRE admissions reduced by 35.3 %. CONCLUSIONS Despite increased health professionals (workdays) per patient in LRE, mortality rate and still birth rate increased by 60.2 % and 23 % respectively indicating worsening of efficiency which is direct hidden negative psychological impact of pandemic immediately calling for the need of positive counselling and proper psychiatric care of both the health professionals and patients.

Post-COVID-19 sequelae

COVID-19 is a new disease and the acute clinical presentation is mostly clear now. It is also known now that the disease may have sequelae affecting various systems. The respiratory sequelae include pulmonary fibrosis due to the immune-mediated mechanisms that follow a cytokine storm, diffuse alveolar damage, and microvascular thrombosis. A decline in lung function may be seen in patients who still have residual symptoms and hypoxia. COVID-19-associated pulmonary aspergillosis, a well-recognized complication, especially in patients with acute respiratory distress syndrome, has emerged as a significant risk factor for increased mortality. Fatigue is a common symptom that patients come back with, in the post-COVID period. Dyspnea without hypoxia has been attributed to respiratory muscle dysfunction and deconditioning resulting in decreased exercise tolerance. Palpitation is another common persisting symptom. Thromboembolic disease, a common association during the acute phase of illness, is not an uncommon entity that is seen even after "recovery" from COVID-19. Thromboembolic events causing stroke have been identified as an immediate complication of COVID-19, but can occur during the recovery phase as well, in high-risk patients. The return of smell and taste sensations could take a few weeks to months even after complete recovery from the illness. Mood swings, anxiety, and sleep deprivation have all been reported by patients recovering from this viral illness. The last 14 months have been feverishly spent in trying to understand this particular disease, but the long-term complications of COVID-19 are still elusive.

Face Mask Detection System using CNN

Amid current pandemic, Covid-19 has made us realize the importance of Face Masks and we need to understand the crucial effects of not wearing one, now more than ever. Right now, there are no face mask detectors installed at the crowded places. But we believe that it is of utmost importance that at transportation junctions, densely populated residential area, markets, educational institutions and healthcare areas, it is now very important to set up face mask detectors to ensure the safety of the public. In this paper we have tried to build a two phased face mask detector which will be easy to deploy at the mentioned outlets. With the help of Computer Vision, it is now possible to detect and implement this on large scale. We have used CNN/ MobileNet V2 architecture for the implementation of our model. The implementation is done in Python, and the python script implementation will train our face mask detector on our selected dataset using TensorFlow and Keras. We have added more robust features and trained our model on various variations, we made sure to have large varied and augmented dataset so that the model is able to clearly identify and detection the face masks in real time videos. The trained model was tested on both real-time videos and static pictures and in both the cases the accuracy was more than the other designed models. © 2021 IEEE.

Pandemic corona virus disease (COVID-19) and non-pharmacological interventions: an impact assessment from India. (Special Issue: COVID-19.)

Introduction: World Health Organization (WHO) declared Corona Virus Disease (COVID-19) as a public health emergency of international concern on 30 January 2020 and later confirmed it as a pandemic on 11 March 2020. In India first case of COVID-19 was detected on 31 January 2020 and as of 14 April 2020;354 districts were affected and 10,363 confirmed cases were reported. India has adopted various non pharmacological interventions to control the spread of COVID-19 such as nationwide lockdown and social distancing. This study was undertaken to assess the impact of non-pharmacological interventions adopted by India to contain the spread of novel corona virus (SARS CoV-2) during the pandemic period. Material and Methods: We analyzed the data of number of cases reported to WHO in three weeks prior to announcement of lockdown and three weeks during the lockdown period. Doubling time and rate of spread of COVID-19 cases was calculated to assess the impact of non-pharmacological interventions. Data was entered in MS excel and was analyzed using the software GraphPad Prism version 8.4.2.

Design and development of face shield by 3D printing for the COVID-19 epidemics

The world is facing problem of pandemic crisis situation with Covid-19 virus. In this situation medical persons are seeking a piece of personal protective equipment such as face mask to avoid the infections. The face mask/shield help to protect from body fluids and droplets, where the patients are treated from cough, other infections. In this concern the present work is focused on the design and development of prototype face shield mask by 3D printing technology using fused deposition modelling (FDM).A preliminary economic design indicated that the presented approach offers a feasible alternative to the current practices.