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COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making.

In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score.

The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset.

Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

The first case of coronavirus disease 2019 (COVID-19) was documented in China, and the virus was soon to be introduced to its neighboring country – South Korea. South Korea, one of the earliest countries to initiate a national pandemic response to COVID-19 with fairly substantial measures at the individual, societal and governmental level, is an interesting example of a rapid response by the Global South. The current study examines contact tracing mobile applications (hereafter, contact tracing apps) for those who were subject to self-quarantine through the lenses of dataveillance and datafication. This paper analyzes online/digital data from those who were mandatorily self-quarantined by the Korean government largely due to returning from overseas travel.

This study uses an Internet ethnography approach to collect and analyze data. To extract data for this study, self-quarantined Korean individuals' blog entries were collected and verified with a combination of crawling and manual checking. Content analysis was performed with the codes and themes that emerged. In the COVID-19 pandemic era, this method is particularly useful to gain access to those who are affected by the situation. This approach advances the author’s understandings of COVID-19 contact tracing mobile apps and the experiences of self-quarantined people who use them.

The paper shows Korean citizens' understandings and views of using the COVID-19 self-tracing application in South Korea through examining their experiences. The research argues that the application functions as a datafication tool that collects the self-quarantined people's information and performs dataveillance on the self-quarantined people. This research further offers insights for various agreements/disagreements at different actors (i.e. the self-quarantined, their families, contact tracers/government officials) in the process of contact tracing for COVID-19.

This study also provides insights into the implications of information and technology as they affect datafication and dataveillance conducted on the public. This study investigates an ongoing debate of COVID-19's contact tracing method concerning privacy and builds upon an emerging body of literature on datafication, dataveillance, social control and digital sociology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0377

This study aims to identify the dietary patterns of two groups of subjects (with and without COVID-19), and to assess the relationship of findings with the prognosis of COVID-19 and metabolic risk parameters.

This study included 100 individuals in the age range of 19–65 years. The medical history, and data on biochemical, hematological and inflammatory indicators were retrieved from the files. A questionnaire for the 24-h food record and the food intake frequency was administered in face-to-face interviews, and dietary patterns of subjects were assessed.

In individuals with COVID-19, the hip circumference, the waist-hip ratio and the body fat percentage were significantly higher (p < 0.05), and the muscle mass percentage was significantly lower (p < 0.05). Mediterranean diet adherence screener (MEDAS), dietary approaches to stop hypertension (DASH) and healthy eating ındex-2015 (HEI-2015) scores were low in the two groups. A linear correlation of DASH scores was found with the muscle mass percentage (p = 0.046) and a significant inverse correlation of with the body fat percentage (p = 0.006). HEI-2015 scores were significantly and negatively correlated with body weight, body mass index, waist circumference, hip circumference and neck circumference (p < 0.05). Every one-unit increase in MEDAS, DASH and HEI-2015 scores caused reductions in C-reactive protein levels at different magnitudes. Troponin-I was significantly and negatively correlated with fruit intake (p = 0.044), a component of a Mediterranean diet and with HEI-2015 total scores (p = 0.032).

The limitation of this study includes the small sample size and the lack of dietary interventions. Another limitation is the use of the food recall method for the assessment of dietary patterns. This way assessments were performed based on participants’ memory and statements.

Following a healthy diet pattern can help reduce the metabolic risks of COVİD-19 disease.

Despite these limitations, this study is valuable because, to the best of the authors’ knowledge, it is the first study demonstrating the association of dietary patterns with disease prognosis and metabolic risks concerning COVID-19. This study suggests that dietary patterns during the COVID-19 process may be associated with several metabolic risks and inflammatory biomarkers.

This chapter explores theory and local context of socially constructed pandemic fears during COVID-19; how material and non-material fear objects are construed, interpreted and understood by communities, and how fears disrupt social norms and influence pandemic behavioural responses. We aimed to understand the lived experiences of pandemic-induced fears in socioculturally diverse communities in eastern Indonesia in the context of onto-epistemological disjunctures between biomedically derived public health interventions, local world views and causal-remedial explanations for the crisis. Ethnographic research conducted among several communities in East Nusa Tenggara province in Indonesia provided the data and analyses presented in this chapter, delineating the extent to which fear played a decisive role in both internal, felt experience and social relations. Results illustrate how fear emotions are constructed and acted upon during times of crisis, arising from misinformation, rumour, socioreligious influence, long-standing tradition and community understandings of modernity, power and biomedicine. The chapter outlines several sociological theories on fear and emotion and interrogates a post-pandemic future.

This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm.

In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach.

The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC).

The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.

Technologies are ubiquitous in modern Britain, gradually infiltrating many areas of our working and personal lives. But what role can technology play in the current COVID-19 pandemic? At a time when our usual face to face social interactions are temporarily suspended, many of us have reached out to technology (e.g. Skype, WhatsApp, Facebook, Zoom) to help maintain a sense of closeness and connection to friends, family and vital services.

One largely unsung technology is the virtual assistant (VA), a cost-efficient technology enabling users to access the Internet of Things using little more than voice. Deploying an ecological framework, in the context of smart age-friendly cities, this paper explores how VA technology can function as an emergency response system, providing citizens with systems to connect with friends, family, vital services and offering assistance in the diagnosis of COVID-19.

We provide an illustration of the potentials and challenges VAs present, concluding stricter regulation and controls should be implemented before VAs can be safely integrated into smart age-friendly cities across the globe.

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.