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1,264 Research products, page 1 of 127

  • COVID-19
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  • 2018-2022
  • WHO Global literature on coronavirus disease

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  • Open Access
    Authors: 
    Victoria Rayskin;
    Publisher: AIP Publishing

    Utilization of multiple trajectories of a dynamical system model provides us with several benefits in approximation of time series. For short term predictions a high accuracy can be achieved via switches to new trajectory at any time. Different long term trends (tendency to different stationary points) of the phase portrait characterize various scenarios of the process realization influenced by externalities. The dynamical system's phase portrait analysis helps to see if the equations properly describe the reality. We also extend the dynamical systems approach (discussed in \cite{R5}) to the dynamical systems with external control. We illustrate these ideas with the help of new examples of the rental properties HOMES.mil platform data. We also compare the qualitative properties of HOMES.mil and Wikipedia.org platforms' phase portraits and the corresponding differences of the two platforms' users. In our last example with COVID-19 data we discuss the high accuracy of the short term prediction of confirmed infection cases, recovery cases and death cases in various countries. arXiv admin note: substantial text overlap with arXiv:1912.06939

  • Publication . Conference object . 2020
    Open Access
    Authors: 
    Maha A. Alanezi; Nabil M. Hewahi;
    Publisher: IEEE

    The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.

  • Open Access
    Authors: 
    Saksham Tewari; Utkarsh Agrawal; Siddharth Verma; Sunil Kumar; S. Jeevaraj;
    Publisher: IEEE

    Coronavirus is a virus of RNA-type that can infect both humans and animal and causes a wide variety of respiratory infections. In humans, it also causes pneumonia. Since coronavirus has been declared a pandemic, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been the standard method for detection but is a time consuming operation and due to sudden surge in demand it has a high cost. In this study, coronavirus was detected from X-ray scans of chest using a deep learning model consisting of fuzzy image enhancement, offline data augmentation, image segmentation and classification through Convolutional Neural Network. For training and classification, an ensembeled model consisting of the features of VGG-16, ResNet-50 and MobileNetV2 was built and optimized with bayesian optimization. The proposed model achieved an overall accuracy of 96.34%. The precision, recall and F1-Score for COVID-19 class was 100%, 96% and 98% respectively.

  • Open Access
    Authors: 
    Luxin Zhang; Wei Qi Yan;
    Publisher: IEEE

    The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.

  • Open Access
    Authors: 
    Hendra Adinanta; Edi Kurniawan; Suryadi; Jalu A. Prakosa;
    Publisher: IEEE

    World Health Organization (WHO) has confirmed that the spreading of coronavirus disease 2019 (COVID-19) could be avoided by keeping the physical distance at least 3 feet (1 meter). Then, we have a motivation to employ computer vision techniques to monitor social distancing violations. The principle of the works are to detect persons, then to assess the physical distancing violation from their distance. Most of the researchers have tried to utilize object detection methods such as faster RCNN, Yolo, and SSD to detect persons from the frame. Those methods rely on, the support of Graphics Processing Unit (GPU) to execute their heavy computation. In this works, we propose social distancing monitoring by applying background subtraction methods based on Gaussian Mixture Models (GMM) i.e. Geo-metric Multigrid (GMG), k-Nearest Neighbor (KNN), Mixture of Gaussian (MOG), and Mixture of Gaussian 2 (MOG2). These methods have been used to filter persons from the frame with computational process. Some parameters evaluation measures have been determined to check the best method suitable for this works. In terms of performance, better methods are ranked as KNN, MOG, MOG2, and GMG.

  • Publication . Article . 2020
    Open Access
    Authors: 
    Basu, Debarati; Gopalkrishna, Niveditha;
    Publisher: ACM
  • Open Access
    Authors: 
    Xinggan Peng; Huiping Zhuang; Guang-Bin Huang; Haizhou Li; Zhiping Lin;
    Publisher: IEEE

    Due to the outbreak of the novel coronavirus (or known as COVID-19), people are advised to wear masks when they stay outdoors in many countries. This could result in difficulty for some public safety surveillance systems involving face detection or tracking. Therefore, the development of face detection and tracking algorithms for people wearing face masks is particularly important. In this paper, a real-time tracking algorithm for people with or without face masks is proposed. This algorithm is trained on public face datasets with faces without masks. Although the training does not involve face images of people wearing face masks, we show that the proposed algorithm is robust as it is able to perform well in face tracking for people wearing face masks. We also discuss the possible scenarios where the algorithm could lose track of the target when experimenting in tracking masked faces. This can motivate future research in this area.

  • Open Access
    Authors: 
    Nayeeb Rashid; Adnan Faisal Hossain; Mohammad Ali; Mumtahina Islam Sukanya; Tanvir Mahmud; Shaikh Anowarul Fattah;
    Publisher: IEEE

    Radiology examination of chest radiography or chest X-ray (CXR), is currently performed manually by radiologists. With the onset of the COVID-19 pandemic, there is now a need to automate this process which is currently one of the key methods of primary detection of the SARS-Cov-2 virus. This will lead to shorter diagnosis time and less human error. In this study, we try to perform three-class image classification on a dataset of chest X-rays of confirmed COVID-19 patients(408 images), confirmed pneumonia patients(4273 images), and chest X-rays of healthy people(1590 images). In total the dataset consists of 6271 people. We aim to use a Convolutional Neural Network(CNN) and transfer learning to perform this image classification task. Our model is based on a pre-trained InceptionV3 network with weights trained on the ImageNet dataset. We fine-tune the layers of the Inception network to train it to our specific task. We try fine-tuning the network to different extents by freezing a different number of layers and then comparing accuracy for each variation of the network. To evaluate the performance of our network we use several metrics which include Classification accuracy, Precision, Sensitivity, and Specificity. Our proposed method achieves an accuracy of 96.33% on a 3-class classification task (Normal, COVID-19, Pneumonia) and an accuracy of 99.39% on a 2-class (COVID and Non-COVID) classification task.

  • Open Access
    Authors: 
    Gunawansyah; Riska Rahayu; Nurwathi; Bambang Sugiarto; Gunawan;
    Publisher: IEEE

    The use of technology really helps to maximized the effectiveness and efficiency of work expecially in the education field. Elearning is the concept of education that has begun to be widely implemented at this covid-19 pandemic to avoid the spread of transmission through social distancing. One of elearning types is essay but for large participants, it need much effort for evaluate by human rater. The inconsistency of assessment by the rater due to fatigue can also affect the quality of the assessment. Developing a system that can learn and understand on its own without having to be repeatedly programmed by humans used machine learning and computational linguistics to study the interaction between computers and human natural language used natural language processing proposed in this research. Natural language processing and text mining methods are able to provide a good assessment which is influenced by several processes, namely tokenization, stopword, stemming and support with the number of keywords, and the synonym of more complex keywords. The automated essay scoring system is proven to provide consistent and objective assessments and is able to approach human raters assessments.

  • Open Access
    Authors: 
    Ken Donaldson; A Brenton; P Haslam; N Turner; J Talbot; J Newsham; F Clarke; A Kinley; K Prior;
    Publisher: BMJ Publishing Group Ltd and British Thoracic Society

    Background University Hospitals of Morecambe Bay NHS Trust, witnessed an early peak of COVID-19 with related hospital admissions in early 2020, this created a need for a coordinated approach to post COVID-19 rehabilitation needs across the area Objectives A three-armed COVID-19 rehabilitation pathway was devised in March 2020 with Arm 1 aiming to assess and address the immediate rehabilitation needs of those leaving hospital following an admission for respiratory complications of COVID-19 Methods Existing Pulmonary Rehabilitation teams were repurposed by integrated care network (MBRN) to be a new 'Virtual' rehabilitation service A register of patients discharged from hospital sites was remotely screened for pathway suitability Then, using a multi-professional template a holistic assessment needs was conducted using telephone and/or home visit consultations Clinical assessment tools were built into the assessment process Weekly 'acute-community' virtual in-service training sessions and multi-disciplinary case discussions supported the clinicians Results To date 207 patients have entered the service for virtual triage, 138 patients were deemed suitable for further assessment and interventions 427 direct clinician consultations were delivered to these 138 patients [122 initial telephone assessments;53 initial home visit assessments;168 follow-up telephone consultations;84 follow-up home visits] Two of the 138 patients assessed died, both were expected deaths No clinical incidents occurred and no staff contracted COVID-19 during this period Feedback from the services' staff survey was very positive highlighting the supportive value of virtual training and MDT and the enjoyment of being part of creating and delivering this new service to patients recovering from COVID-19 Conclusions Utilising the skills of pulmonary rehabilitation staff to deliver a holistic rehabilitation and treatment service to those discharged from hospital after suffering respiratory complications of COVID-19 was feasible, safe and well tolerated by staff and patients This service is now being used to address the needs of post-COVID-19 patients presenting with respiratory needs in the community We aim also to assess clinical outcome

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
1,264 Research products, page 1 of 127
  • Open Access
    Authors: 
    Victoria Rayskin;
    Publisher: AIP Publishing

    Utilization of multiple trajectories of a dynamical system model provides us with several benefits in approximation of time series. For short term predictions a high accuracy can be achieved via switches to new trajectory at any time. Different long term trends (tendency to different stationary points) of the phase portrait characterize various scenarios of the process realization influenced by externalities. The dynamical system's phase portrait analysis helps to see if the equations properly describe the reality. We also extend the dynamical systems approach (discussed in \cite{R5}) to the dynamical systems with external control. We illustrate these ideas with the help of new examples of the rental properties HOMES.mil platform data. We also compare the qualitative properties of HOMES.mil and Wikipedia.org platforms' phase portraits and the corresponding differences of the two platforms' users. In our last example with COVID-19 data we discuss the high accuracy of the short term prediction of confirmed infection cases, recovery cases and death cases in various countries. arXiv admin note: substantial text overlap with arXiv:1912.06939

  • Publication . Conference object . 2020
    Open Access
    Authors: 
    Maha A. Alanezi; Nabil M. Hewahi;
    Publisher: IEEE

    The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.

  • Open Access
    Authors: 
    Saksham Tewari; Utkarsh Agrawal; Siddharth Verma; Sunil Kumar; S. Jeevaraj;
    Publisher: IEEE

    Coronavirus is a virus of RNA-type that can infect both humans and animal and causes a wide variety of respiratory infections. In humans, it also causes pneumonia. Since coronavirus has been declared a pandemic, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been the standard method for detection but is a time consuming operation and due to sudden surge in demand it has a high cost. In this study, coronavirus was detected from X-ray scans of chest using a deep learning model consisting of fuzzy image enhancement, offline data augmentation, image segmentation and classification through Convolutional Neural Network. For training and classification, an ensembeled model consisting of the features of VGG-16, ResNet-50 and MobileNetV2 was built and optimized with bayesian optimization. The proposed model achieved an overall accuracy of 96.34%. The precision, recall and F1-Score for COVID-19 class was 100%, 96% and 98% respectively.

  • Open Access
    Authors: 
    Luxin Zhang; Wei Qi Yan;
    Publisher: IEEE

    The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.

  • Open Access
    Authors: 
    Hendra Adinanta; Edi Kurniawan; Suryadi; Jalu A. Prakosa;
    Publisher: IEEE

    World Health Organization (WHO) has confirmed that the spreading of coronavirus disease 2019 (COVID-19) could be avoided by keeping the physical distance at least 3 feet (1 meter). Then, we have a motivation to employ computer vision techniques to monitor social distancing violations. The principle of the works are to detect persons, then to assess the physical distancing violation from their distance. Most of the researchers have tried to utilize object detection methods such as faster RCNN, Yolo, and SSD to detect persons from the frame. Those methods rely on, the support of Graphics Processing Unit (GPU) to execute their heavy computation. In this works, we propose social distancing monitoring by applying background subtraction methods based on Gaussian Mixture Models (GMM) i.e. Geo-metric Multigrid (GMG), k-Nearest Neighbor (KNN), Mixture of Gaussian (MOG), and Mixture of Gaussian 2 (MOG2). These methods have been used to filter persons from the frame with computational process. Some parameters evaluation measures have been determined to check the best method suitable for this works. In terms of performance, better methods are ranked as KNN, MOG, MOG2, and GMG.

  • Publication . Article . 2020
    Open Access
    Authors: 
    Basu, Debarati; Gopalkrishna, Niveditha;
    Publisher: ACM
  • Open Access
    Authors: 
    Xinggan Peng; Huiping Zhuang; Guang-Bin Huang; Haizhou Li; Zhiping Lin;
    Publisher: IEEE

    Due to the outbreak of the novel coronavirus (or known as COVID-19), people are advised to wear masks when they stay outdoors in many countries. This could result in difficulty for some public safety surveillance systems involving face detection or tracking. Therefore, the development of face detection and tracking algorithms for people wearing face masks is particularly important. In this paper, a real-time tracking algorithm for people with or without face masks is proposed. This algorithm is trained on public face datasets with faces without masks. Although the training does not involve face images of people wearing face masks, we show that the proposed algorithm is robust as it is able to perform well in face tracking for people wearing face masks. We also discuss the possible scenarios where the algorithm could lose track of the target when experimenting in tracking masked faces. This can motivate future research in this area.

  • Open Access
    Authors: 
    Nayeeb Rashid; Adnan Faisal Hossain; Mohammad Ali; Mumtahina Islam Sukanya; Tanvir Mahmud; Shaikh Anowarul Fattah;
    Publisher: IEEE

    Radiology examination of chest radiography or chest X-ray (CXR), is currently performed manually by radiologists. With the onset of the COVID-19 pandemic, there is now a need to automate this process which is currently one of the key methods of primary detection of the SARS-Cov-2 virus. This will lead to shorter diagnosis time and less human error. In this study, we try to perform three-class image classification on a dataset of chest X-rays of confirmed COVID-19 patients(408 images), confirmed pneumonia patients(4273 images), and chest X-rays of healthy people(1590 images). In total the dataset consists of 6271 people. We aim to use a Convolutional Neural Network(CNN) and transfer learning to perform this image classification task. Our model is based on a pre-trained InceptionV3 network with weights trained on the ImageNet dataset. We fine-tune the layers of the Inception network to train it to our specific task. We try fine-tuning the network to different extents by freezing a different number of layers and then comparing accuracy for each variation of the network. To evaluate the performance of our network we use several metrics which include Classification accuracy, Precision, Sensitivity, and Specificity. Our proposed method achieves an accuracy of 96.33% on a 3-class classification task (Normal, COVID-19, Pneumonia) and an accuracy of 99.39% on a 2-class (COVID and Non-COVID) classification task.

  • Open Access
    Authors: 
    Gunawansyah; Riska Rahayu; Nurwathi; Bambang Sugiarto; Gunawan;
    Publisher: IEEE

    The use of technology really helps to maximized the effectiveness and efficiency of work expecially in the education field. Elearning is the concept of education that has begun to be widely implemented at this covid-19 pandemic to avoid the spread of transmission through social distancing. One of elearning types is essay but for large participants, it need much effort for evaluate by human rater. The inconsistency of assessment by the rater due to fatigue can also affect the quality of the assessment. Developing a system that can learn and understand on its own without having to be repeatedly programmed by humans used machine learning and computational linguistics to study the interaction between computers and human natural language used natural language processing proposed in this research. Natural language processing and text mining methods are able to provide a good assessment which is influenced by several processes, namely tokenization, stopword, stemming and support with the number of keywords, and the synonym of more complex keywords. The automated essay scoring system is proven to provide consistent and objective assessments and is able to approach human raters assessments.

  • Open Access
    Authors: 
    Ken Donaldson; A Brenton; P Haslam; N Turner; J Talbot; J Newsham; F Clarke; A Kinley; K Prior;
    Publisher: BMJ Publishing Group Ltd and British Thoracic Society

    Background University Hospitals of Morecambe Bay NHS Trust, witnessed an early peak of COVID-19 with related hospital admissions in early 2020, this created a need for a coordinated approach to post COVID-19 rehabilitation needs across the area Objectives A three-armed COVID-19 rehabilitation pathway was devised in March 2020 with Arm 1 aiming to assess and address the immediate rehabilitation needs of those leaving hospital following an admission for respiratory complications of COVID-19 Methods Existing Pulmonary Rehabilitation teams were repurposed by integrated care network (MBRN) to be a new 'Virtual' rehabilitation service A register of patients discharged from hospital sites was remotely screened for pathway suitability Then, using a multi-professional template a holistic assessment needs was conducted using telephone and/or home visit consultations Clinical assessment tools were built into the assessment process Weekly 'acute-community' virtual in-service training sessions and multi-disciplinary case discussions supported the clinicians Results To date 207 patients have entered the service for virtual triage, 138 patients were deemed suitable for further assessment and interventions 427 direct clinician consultations were delivered to these 138 patients [122 initial telephone assessments;53 initial home visit assessments;168 follow-up telephone consultations;84 follow-up home visits] Two of the 138 patients assessed died, both were expected deaths No clinical incidents occurred and no staff contracted COVID-19 during this period Feedback from the services' staff survey was very positive highlighting the supportive value of virtual training and MDT and the enjoyment of being part of creating and delivering this new service to patients recovering from COVID-19 Conclusions Utilising the skills of pulmonary rehabilitation staff to deliver a holistic rehabilitation and treatment service to those discharged from hospital after suffering respiratory complications of COVID-19 was feasible, safe and well tolerated by staff and patients This service is now being used to address the needs of post-COVID-19 patients presenting with respiratory needs in the community We aim also to assess clinical outcome