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Background: COVID-19 has caused pandemic during 2019-2020 and has presented with illnesses ranging from the usual mild flu to serious respiratory problems/complications, even leading to considerable mortality. Recent literatures have suggested that the health (especially psychological) impacts of quarantine are substantial and can be long lasting. Aim: The purpose of this study was to assess the mental health status (psychological distress) of experienced quarantine and compliance to quarantine during the outbreak of COVID-19 in Nuh district. Methods: The study included 543 subjects (adults aged 18 years or more) who were sent for quarantine at home or state-run facilities and included "Flu corner " screened patient and health-care staff working in COVID-19 outpatient and wards. The psychological impact was assessed using the Kessler Psychological Distress Scale (K10). Categorical data were presented as percentages (%), and bivariable logistic regression was applied to find out the association, and it was considered significant if the P < 0.05. Results: The doctors and nursing staff were among two-fifth of the subjects (217/543, 40.1%), and only 11.6% of quarantined subjects (63/543) were compliant with all protective measures. The mean score obtained on Kessler Psychological Distress Scale (K10) subjects was 18.69 +/- 4.88, whereas out of 543 subjects, 152 (27.9%) had a score of 20 or more, and it has a significant association with the elderly age group, female gender, and workplace as exposure setting (P < 0.05). Conclusion: Given the developing situation with coronavirus pandemic, policymakers urgently need evidence synthesis to produce guidance for the public. Thus, the outcomes of this study will positively help authorities, administrators, and policymakers to apply quarantine measures in a better way.
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To distinguish individuals wearing face masks in observation settings like banks and ATMs, this work will give a profound learning model to face mask recognition. Hoodlums and offenders perpetrate wrongdoings by disguising their elements behind face masks, which is contrary to the standard in checking environmental factors. To recognize and secure offenders and lawbreakers, the face mask locator model set forth in this study can be joined with observation cameras in independent reconnaissance frameworks. The COVID-19 pandemic has in short order disturbed worldwide exchange and transportation, influencing our everyday lives. The act of utilizing a defensive face mask has changed. Coming soon from now on, a few public specialist co-ops will expect that clients utilize the legitimate masks while utilizing their administrations. Face mask ID is turning into a significant obligation to help the worldwide civilization. This paper frames a dense strategy for accomplishing this objective using specific essential AI instruments, including Tensor Stream, Keras, OpenCV, and Scikit-Learn. The proposed procedure effectively perceives the face in the picture and afterward decides if it is covered by a mask. © 2022 IEEE.
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In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.
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Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.
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After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. This pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. This paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. This model is devised using several IoTs components and various ML (Machine Learning) techniques have been used to get comparative enhanced results. The hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. An Alert will be sent to Admin if someone has an abnormal temperature and oxygen level. The Firebase database is used to store information and it gets updated in real-time. People's health history can be further analyzed through graphs for visualization and monitored by the administrator. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it's not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It'll make it a lot easier to determine who's Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%. © 2023 The authors and IOS Press.
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To determine the capabilities of this technology, we refer to different research papers related to this topic. This literature-based research could assist practitioners in devising responses to relevant issues and combating the COVID-19 pandemic. This paper examines the position of IoT-based technology in COVID-19. It looks at state-of-the-art architectures, networks, implementations, and industrial IoT-based solutions for combating COVID-19 in three stages: early detection, quarantine, and recovery. Since 2020 was a challenging year for all of us, and during this pandemic, we all realized that social gatherings had to be avoided, and the serious issue was to handle it. So to tackle this and ease the handling of the Corona Virus, we developed an automatic door that monitors an individual's temperature and whether the person is wearing a mask. In the absence of a mask, it clicks a picture of the person and stores it in the database for future reference. © 2023 The authors and IOS Press.
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In the era of demonetization, the banking sector has seen an exponential increase in the usage of digital payments. There has been a slew of digital payment networks proposed by both corporate and public entities. These platforms are being used by users to make payments, pay bills, and send money. The cost of Internet plans, the availability of low-cost mobile handsets, and technological savvy are just a few of the factors driving this digital revolution. Although private companies' platforms are preferred by the bulk of people using digital platforms, public players are continually bringing novel ideas to the table, such as UPI. Another new payment platform named e-Rupi has been created and released for users by the Indian government in a similar endeavor. This platform attempts to use a voucher-based system to deliver social programs, health benefits, and a variety of other services. Hence, this paper investigates the detailed functionality of the e-Rupi platform and performs an empirical evaluation and comparative analysis of e-Rupi with other digital payment platforms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose: The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future. Design/methodology/approach: The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach. Findings: The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being. Originality/value: The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings. © 2023, Emerald Publishing Limited.
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This research examined the effect of social media addiction on aggression and overall quality of life on undergraduate students at Lovely Professional University, Phagwara, Punjab. In the study, survey method was used to collect data. A sample of 50 students were selected for this study. Quality of Life Scale by John Flanagan, Internet Addiction Test by Kimberly S. Young, and The Aggression Scale by Pamela Orpinas and Ralph Frankowski (2001) were administered for data collection. SPSS tool has been used for data analysis. Results indicated that there is a positive and significant connection between social media addiction, aggression, and quality of life. It has also been found that Covid-19 has leaded to increased social media addiction, and therefore, more grave consequences. Copyright © 2022, Anka Publishers. All rights reserved.
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COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).
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—The current COVID-19 pandemic has elevated the importance of cleanliness and social distancing. These needs will continue to be important as the world moves to a new normal whilst navigating through a post-covid environment. This paper presents a use case application that focuses on enforcing safe distance measures inside a campus building where there is limited manpower resources. Amidst the social setting within the university, staff or students may at times accidentally congregate, which may lead to spread of diseases inconveniencing all affected parties. Our proposed integrated solution consists of a network of video cameras and sensors which allows one to monitor behavior within the building. The integrated smart devices communicate with (1) an analytics server that processes the data from the various sensors and (2) a platform that integrates the analytic results and optimizes the action items to be reflected to the environment. A pilot prototype has been deployed and evaluated within a living lab setting on campus. Results show that the system is useful in streamlining the operational process resulting in more efficient processes and procedures to help enforce safe management measures needed to maintain proper social distancing among occupants in campus. © 2022 by the authors.
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In the era of demonetization, the banking sector has seen an exponential increase in the usage of digital payments. There has been a slew of digital payment networks proposed by both corporate and public entities. These platforms are being used by users to make payments, pay bills, and send money. The cost of Internet plans, the availability of low-cost mobile handsets, and technological savvy are just a few of the factors driving this digital revolution. Although private companies’ platforms are preferred by the bulk of people using digital platforms, public players are continually bringing novel ideas to the table, such as UPI. Another new payment platform named e-Rupi has been created and released for users by the Indian government in a similar endeavor. This platform attempts to use a voucher-based system to deliver social programs, health benefits, and a variety of other services. Hence, this paper investigates the detailed functionality of the e-Rupi platform and performs an empirical evaluation and comparative analysis of e-Rupi with other digital payment platforms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Since the approach of the internet, many fake news and fabricated articles/contents observed widely. With the growing utilization of advancement and social media, buyers are making and sharing more information than some other time in recent memory. However, some individuals distributed counterfeit news with no significance to reality just to build the readership. Gossip distinguishing on social media is an essential issue. This paper talks about the methodology of machine learning and natural language processing to solve this problem. Use of TF-IDF (TermFrequencyInverse Document Frequency) and trained the data on four classifiers to explore which amongst them works well for this Indian dataset (https://github.com/Aks121/Fake-News-Analysis-on-Indian-Dataset ).The recall, precision and F1 scores help us figure out which model works best. The accuracy achieved so far is 95 on the ratio of 70:30 split dataset. The reason for this work is to approach the mechanized arrangement of the news stories utilizing machine learning. This can be used by the users to identify through the locales containing fake news. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: With the emergence of highly infectious epidemics/pandemics, such as Ebola virus diseases, severe acute respiratory syndrome, and coronavirus disease-2019 (COVID-19), doctors are at much greater risk of infection due to the exposure to the highly infectious bodily fluids and droplet nuclei. Hence, treating and caring for patients need the use of personal protective equipment (PPE) to reduce the transmission risk. Objectives: The present study was conducted to estimate the prevalence of skin injury and its type due to PPE usage, to find the association of related factors with the skin injuries among doctors. Materials and Methods: This descriptive cross-sectional study was conducted after obtaining the institutional ethical approval in dedicated COVID-19 hospital for a period of 4 months among 132 doctors wearing grades 2 and 3 PPE kit using a questionnaire collecting the details regarding baseline, duty, skin injury characterization. During analysis of data, an association between variables was significant for P < 0.05. Results: It was observed that 17.1% of doctors were wearing the PPE kit for 5 or more hours/and 13.0% of subjects reported absenteeism from duty hours due to PPE-induced skin injuries. 76.4% of subjects have suffered from skin injury after PPE usage. The most common symptoms/signs for the skin injury occurred was indentation and pain on back of ears (61.0%). Conclusion: The skin injuries of PPE among the doctors may result in reduced morale for overloaded work and made them anxious. Hence, an appropriate monitoring of these adverse effects should be done and effective preventive measures should be adopted. © 2022 Journal of Medical Society ;Published by Wolters Kluwer - Medknow.
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Background: Hospitalised COVID-19 patients are known to exhibit varying degrees of immune dysfunction, few modifiable risk factors have been identified to improve this state of which one is the immune modulator effects of vitamin D. Vitamin D is being prescribed as a treatment of COVID-19 in a few guidelines as there is generalised assumption that vitamin D enhances immunity during this illness. So this is an attempt to find out whether a deficiency of vitamin D is associated with the severity of COVID-19. Aim: To study the relationship of serum 25 hydroxy vitamin D [25(OH)D] deficiency with disease severity in hospitalised COVID-19 patients. Materials and methods: The present case-control study compared serum 25(OH)D levels among Mild to moderate and severe COVID- 19 patients. Around 39 diagnosed and Hospitalised Severe COVID- 19 disease are compared with 39 Hospitalised Mild and Moderate COVID-19 disease in Care Hospital, Bhubaneswar, Odisha, India between April 1, 2021, ad August 31, 2021. Patients were divided into 2 groups. The Group 1-Mild to Moderate infection with CT Severity index < 10/25 and Group 2-Severe Infection with HRCT Chest of CTSI >10/25. As per hospital policy, severe infection patients were kept in Critical Care Area and Mild infection patients were kept in Ward/Cabin areas. Any patients becoming sick and being transferred to critical areas are shifted from Group 1 to Group 2 after HRCT chest. Vitamin D levels (25 D Cholecalciferol) are done on the day of admission by chemiluminescence immunoassay test after taking due consent from the patients/attenders. The level of cut-off used in our study is 20 ng/mL. The association was analysed using regression analysis and other statistical methods. Results: The status of 25(OH)D deficiency (present/absent with cut-off being 20 ng/mL) showed no significant difference among cases and control at p < 0.05. Chi-square statistics with Yates correction is 1.8909. The p value is 0.169099. So there were no significant differences in vitamin D3 levels between Mild to moderate and Severe COVID- 19 patients. Conclusion: 25(OH)D levels appear to have no strong association with disease severity amongst hospitalised COVID-19 patients. Hence, its prescription for COVID-19 treatment as well as prevention needs to be reconsidered.
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This is a paper on disease prediction using machine learning through a python graphical user interface application. The motivation behind this application is the pandemic (Covid- Situation) faced by the whole world and also the idea to robotize the current manual framework of initial diagnosis by the assistance of mechanized supplies and undeniable PC programming so that their important information/data can be put away for a more drawn out period and also for a more useful purpose. This paper introduces the field of diseases prediction, the treatment for the disease, and consulting with the doctors nearby through efficient programming using machine learning. It describes the need for a system of an online artificial doctor, which will not only help them in predicting and understanding the diseases, but it will also advise them of certain medicines that are necessary for controlling or curing those diseases. © 2022 IEEE.
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The COVID-19 pandemic has caused economic, physiological, and psychological harm to the world. A crucial step, hence, in the fight against covid is the highly efficient screening of patient cases. Conventional RT-PCR testing, even though more reliable, cannot be done on every patient as the virus has spread way faster than the world’s resources could afford. One very important screening approach that is being used across the globe is chest X-ray imaging. Since X-ray facilities are readily obtainable in healthcare systems of most countries across the globe, and with more and more X-ray systems being digitized, the cost and time of transportation are cut as well. Hence, if the detection of the virus in a CXR image can be automated using AI techniques, it will save a lot of time and effort of radiologists to have to go through hundreds of such images, and in some cases will also spare the need of doing RT-PCR testing, and since saving resources in this time is vital, automated detection can be very effective. In this work, we will explore, analytically discuss, and do a comparative study of many ML and deep learning techniques that have been taken for automated COVID-19 detection through chest X-rays (CXR). We carefully analyze the papers and derive a set of key factors for discriminating the methodologies, classification techniques, approaches, and the results that yielded. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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COVID-19 has taught us the need of practicing social distancing. In the year 2020 because of sudden lockdown across the globe, E-commerce websites and e-shoppingwere the only escape to fulfill our basic needs and with the advancement of technology putting your websites online has become a necessity. Be it food, groceries, or our favorite outfit, all these things are now available online. It was noticed during the lockdown period that the businesses that had no social presence suffered heavy losses. On the other hand, people who had established their presence on the internet saw a sudden boom in their overall sales. This project discusses how the recent advancement in the field of Machine Learning and Artificial Intelligence has led to an increase in the sales of various businesses. The machine learning model analyses the pattern of customer's behavior which affects the sales builds a dataset after many observations and finally helps generate an algorithm which is an efficient recommendation system. This project also discusses how cyber security helps us have secured and authenticated transactions which have aided e-commerce business growth by building customer's trust. © 2022 IEEE.
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Background: During the second wave of COVID-19, India suffered from a catastrophic outburst of cases and rapid transmission of disease due to the highly infectious delta strain (B.1.617.2). Patients infected with this strain underwent prolonged hospitalisations, suffered from severe symptoms. A sudden surge of fungal infections, primarily Mucormycosis was observed. Methods: We conducted a case-control study to study various risk factors and form of intracranial involvement in cases of Mucormycosis. Results: Study included 121 patients in total;out of which 61 were Mucormycosis patients with prior COVID-19 infection. 30 out of 61 Mucormycosis patients had intracranial involvement with majority having stroke in the form of the either infarct (10 patients, 34%);or haemorrhage (3 patients, 10%) and thrombosis of artery involvement (8 patients, 29%). Other intracranial form of involvement was abscess (6 patients, 20%) and meningitis (2 patients, 7%). The most frequent type of infarcts were lacunar infarcts and the most common location of infarcts were middle cerebral artery (MCA) or anterior cerebral artery (ACA). Patients were treated with administration of Amphotericin B and Posaconazole. Telephonic follow-up was conducted after a time period of about 90 days and their health condition was recorded on basis of modified ranking scale (mRS). Out of the 30 Mucormycosis infection patients displaying the occurrence of stroke, 10 patients could not survive. q Conclusion: Intracranial Mucormycosis in COVID19 patients presenting with stroke were observed frequently and had mortality in about one-third cases.