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The pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyze data that can signify a quantitative revolution in healthcare as devices become more convenient and smaller data become larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process [15, 16]. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources [17]. Finally, necessity of edge computing in era of Covid-19 pandemic is addressed. © The Institution of Engineering and Technology 2022.
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Worldwide, 1.75 crores of cases are related to corona virus and deaths are approx. 6.7 lakhs. The outbreak of Coronavirus was declared in March 2020 in India and up to July total number of cases crossed the figure of sixteen lakhs with almost thirty-five thousands of deaths. People are facing shortage of money, health issues, social issues, employment issue. This study aims to analyse the impact of Coronavirus disease on stock price of FMCG companies of India. Alike other countries of world Indian government also used lockdown to stop transmission of infection in the country. To analyse the impact of Coronavirus shares prices from March 2020 to July 2020 of top companies listed in Indian stock exchange and data regarding daily increase in confirm cases and number of deceased have been collected. Various statistical tools are applied with the help of Eviews. Finding of study indicated that there is significant impact of Corona Virus over stock price of FMCG sector. © 2023, Indian Institute of Finance. All rights reserved.
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The purpose of this study is to understand and identify sustainability measures applicable in the aviation industry. Further, the study also explores the enablers of sustainability in the aviation sector. The study uses a systematic literature review, published in Scopus-indexed journals. The study addresses the complexity of sustainability in the sector and identifies key indicators based on comprehensive and valid datafrom different stakeholders. The study focuses separately on the three pillars of sustainability-social economic, and environmental sustainability-and identifies sustainability indicators for each pillar from the views of the triple bottom line. The study finds that there is a lack of sustainability knowledge and awareness in the aviation sector despite its resistant growth and expansion. The results show that the aviation industry highlights the importance of sustainability indicators that value equitable development in the pursuit of business goals and environmental and economic efficiency. © 2023, IGI Global. All rights reserved.
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Skin cancer is the most dangerous and lethal cancer that affects millions of people each year. The accurate identification of skin cancers can not be accomplished without expert dermatologists. However, specific research studies of WHO in Canada, US and Australia, show that in the year 1960s to 1980s, the cases of skin cancer has noted more than two times increased in comparison with the previous years. The identification of skin cancer in its early stage is an expensive and difficult task because it doesn't cause too much bad in the initial phase. Whereas, the growth of skin cancer requires biopsy and many other treatments each time which is quite costly as per the statistics of India. This challenge makes it a necessary step to identify the existence of skin cancer in the early stages to increase immortality. With the evolution and progression in technology, there are various methods which have participated in and solved medical issues including covid19, pneumonia and many others. Similarly, machine learning(ML) and deep learning(DL) models are applicable to diagnosing skin cancer in its early stages. In this work, the support vector machine (SVM), naive bayes (NB), K-nearest neighbour (KNN) and neural networks(NN) have been used for classifying benign and malignant lesions. Furthermore, for the feature extraction from the dataset, a pre-trained SqueezeNet model has been used. The classification results of KNN, SVM, NB and NN have been shown in the accuracy, recall, F1-Measure, precision, AUC and ROC. The comparison of the models has resulted that the NN model outperforms all other models when applied with the SqueezeNet feature extractor with the highest accuracy, F1-Measure, recall, precision and AUC as 88.2%, 0.882, 0.882, 0.882 and 0.957, respectively. Lastly, the performance metrics analogies results of each model have been illustrated for the classification of benign and malignant lesions. © 2023 IEEE.
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During the COVID-19 pandemic, researchers started to develop technical approaches to solve the numerous challenges imposed by the new pandemic. One fundamental precondition for research is to make relevant data about the COVID-19 pandemic available in a machine-processable way. For this purpose, COVID-19 ontologies and knowledge graphs have been developed and proposed for many different subareas of COVID-19 applications and research. In this paper, we provide a short analysis of the impact of COVID-19 ontologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Following the publication of the original article [1], the authors identified that Supreet Singh Nayyar, Rahul Kurkure, Arun Yadav, Jyoti Mishra, Biswajit Das and Shubankar Tiwari were incorrectly assigned to affiliation 3. The authors are assigned to affiliation 2. The original article [1] has been corrected. © 2023 The Author(s).
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Increasing disposable income of society and the individual., time-saving attitude., health safety (during COVID-19)., and innovation have increased consumer inclination from offline to online food delivery (OFD). Before COVID-19., eating out the home was the fashion and hangout., but after COVID-19., consumers feel safe while sitting at home. This study aims to explore the pre and current publications on online food delivery., find out the most studied country in OFD., find out the top-cited research publications in OFD., and find out the most dominant research terms in OFD. The present study reviews previous research published during the last 11 years (2012-March 2022) extracted from the www.dimensions.ai free web app. A drastic increase in publications since 2020 explains researchers' inclination toward the OFD. Two hundred twenty research articles were published during the pandemic out of 253 published in the last 11 years. The maximum researched country in OFD is the United States., followed by India and the United Kingdom. The most cited research publication has 255 citations. The most visible keywords in the present study were 'Zomato.,' followed by 'SEM' and 'OFD.' The present study has some limitations., like the database used in the study (dimensions) may not be as good as Scopus or WoS., which may give a better result. More study is required to understand the OFD topic and its survival. It is recommended that the catering industry take OFD as an opportunity along with the regular business generated through steady footfall of the customer/guest. It can be improved through proper logistics., software support., and merging with artificial intelligence. © 2022 IEEE.
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Background: It is very important to increase awareness and understanding of oral manifestations of post COVID-19 disease among dentists which can happen by continuing education and training for dentists to recognize and manage oral manifestations of post COVID-19 disease. Aims & objectives: The aim of this is to aware dentists regarding oral manifestation of covid 19 disease. The objectives of study were to assess the level of awareness, understanding and factors influencing awareness among dentists regarding oral manifestations of post COVID-19 disease. Evaluate the of oral manifestations of post COVID-19 among dentists in central India. Methodology: A cross-sectional, questionnaire-based study was carried out among COVID-19 recovered patients. A sample of 100 subjects, diagnosed as mild and moderate cases of COVID-19 disease were selected based on inclusion and exclusion criteria. A well-structured questionnaire composed of total 21 Closed ended questions was send to sunjects. Result(s): The study comprised an almost equal number of male (54%) and female (48%) participants and among them, 47% belong to the health professional group. A total of 56% of subjects were aged above 35 years and 47% below 35 years. Xerostomia, frequent aphthous ulcers, swallowing difficulty, and burning mouth were the most frequently encountered symptoms in study subjects during the disease and post recovery. Conclusion(s): In the present study, dentists were found to have good knowledge toward novel corona virus disease with optimum preparedness level for dental practice modification. Dental fraternity is further advised to follow the standard guidelines overcoming this pandemic.Copyright © 2022 Authors. All rights reserved.
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Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.
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Purpose: Our study aims to compile data on the clinical presentation, pathological and radiological findings in cases of post-COVID mucormycosis, and present the management strategy used in our center. Methods: This is a retrospective cohort observational study based at a tertiary healthcare institution in Northern India. All COVID-positive patients presenting with clinical features of mucormycosis were included in the study. They underwent complete otorhinolaryngeal, medical, and ophthalmological examination after thorough history taking. Biochemical tests, biopsy and imaging studies were done for all the patients. The treatment strategy included a multidisciplinary team approach, that is, intravenous antifungals as well as surgical debridement of necrotic tissue via Modified Denker's approach or open maxillectomy, and orbital exenteration, if required. Patients were followed up for six months to look for recurrence. Results: Twenty-three patients were studied, out of which 14 were males and 9 were females. Pathological findings of 13 out of 15 patients, who underwent surgical debridement revealed mucormycosis as a causative agent, received Amphotericin. Aspergillus was found in two cases which received Voriconazole. Eleven out of 20 patients who were treated in our hospital survived. Three patients were lost to follow up. The average hospital stay of discharged patients was 14 days. Conclusion: Post-COVID mucormycosis was reported at an alarming rate after the second COVID wave in India especially after steroid therapies in diabetic patients. Thus a timely, aggressive, team approach using Modified Denkers or open maxillectomy along with proper intravenous antifungals is the key to survival in such patients. © 2023, The Author(s).
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Rhino-orbital mucormycosis is a rare life threatening invasive fungal infection that has recently shown a very high mortality rate in India during COVID-19 pandemic. We have designed the present study to find out associations between COVID-19 induced rhino-orbital mucormycosis and concentrations of inflammatory markers, i.e. D-dimer, Ferritin, IL-6, CRP and PCT, in blood serum of Indian population. There were four groups in the study, viz. control group with healthy subjects, treatment group-1 with patients suffering from SARS-COV-2 infection, treatment group-2 with patients suffering from both SARS-COV-2 infection and rhino-orbital mucormycosis, and treatment group-3 with patients suffering from rhino-orbital mucormycosis after SARS-COV-2 infection recovery. Inflammatory markers were quantified with standard protocols, and recorded data were subjected to statistical analyses. We found that patients suffering from SARS-COV-2 infection were more susceptible to rhino-orbital mucormycosis, as they had higher concentrations of inflammatory markers in their blood than the other subjects. Diabetes mellitus, hypertension, cardiovascular diseases and renal disorders were the associated comorbidities with the patients. We also found higher concentrations of inflammatory markers in males than the females, indicating towards their higher susceptibility in developing rhino-orbital mucormycosis than females. Present study therefore suggests that the frequent occurrence of rhino-orbital mucormycosis in India during second wave of COVID-19 was possibly due to indiscriminate use of corticosteroids by COVID-19 patients. Subjects with previous history of comorbidities like diabetes mellitus, hypertension, cardiovascular disorders and renal diseases are the most susceptible population groups for developing infection. Moreover, males are at higher risk of developing mucormycosis than the females. © 2022 Innovative Publication, All rights reserved.
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This study presents a fractional-order mathematical model of coronavirus. We select COVID-19 model and convert the model into fractional order. Discuss its theoretical and numerical analysis. Firstly, we investigate the existence and uniqueness results using some fixed point theorems for the proposed fractional-order COVID-19 model. Further, we provide the stability analysis with the help of the Hyers-Ulam stability. The fractional operator is used in the Caputo sense. We obtain numerical solutions using famous numerical methods and provide a graphical interpretation using adopted numerical methods. Finally, we compare the above techniques and provide observations according to the obtained solutions. © 2023, The Author(s), under exclusive licence to The National Academy of Sciences, India.
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COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists). IEEE
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The Internet of Things (IoT) consists of three major components: perception or idea generation, secure transmission, and intelligent data analysis. These core components can be applied in different formats for the prevention and control of infectious diseases. The procedure relies on the combination of sensors, artificial intelligence (AI), information technology, and available dynamic networking devices. The IoT networks can establish long-distance communication among hospitals, patients, and medical equipment, which could ultimately improve current medical conditions. The IoT has found many applications in infectious disease management that include early prediction, accurate diagnosis, suggestions on therapeutic intervention, and sharing of research data and policy making. All the components of IoT network gets integrated into skeletal framework and can help in control and prevention of infectious diseases. © 2021 Elsevier Inc. All rights reserved.
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Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
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Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.