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Artificial Intelligence Solutions and Applications for COVID-19 Pandemic

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23234

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Special Issue Editors

Dr. Inés Sittón

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Guest Editor

1. BISITE Research Group, University of Salamanca, Salamanca, Spain
2. GITCE Research Group, Technological University of Panama, Panama 0801, Panama
Interests: edge computing; artificial intelligence; Internet of Things

Dr. Sara Rodriguez

E-Mail Website
Guest Editor

BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Dr. Lilia Muñoz

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Guest Editor

GITCE Research Group, Technological University of Panama, Panama 0801, Panama
Interests: Internet of Things; computer applications; software quality

Special Issue Information

Dear Colleagues,

In December 2019, the spread of the SARS-CoV-2 virus began, which resulted in the COVID-19 disease on a pandemic level. This unprecedented crisis affected healthcare systems around the world, as well as other major sectors, intensifying the role of disruptive technologies and requiring the use of the latest advances in intelligent management of large volumes of data (big data), which implies that these latest advances are significantly different from conventional decision-making contexts.

Due to the exceptional nature of the current crisis, implementing solutions that provide significant value and that enable data management is important. Therefore, the integration of solutions based on technologies such as artificial intelligence (AI) and machine learning has allowed for the creation of some solutions, such as efficient data sets, in real time and has made them available to all stakeholders. A large number of investigations are currently under development to provide solutions in mitigating the effects of the pandemic.

For this Special Issue, we invite you to submit high-quality research on emerging solutions and/or applications based on AI techniques, such as machine learning, that address the recent challenges caused by COVID-19. Topics of interest include but are not limited to the following:

application of artificial intelligence for COVID-19;
application of machine learning algorithms for COVID-19;
condition monitoring, prognosis, and diagnosis of COVID-19 patients using AI techniques;
Internet of Things (IoT) devices with application in intelligent systems for COVID-19;
AI applications in image processing to detect abnormalities (e.g., pulmonary) using X-rays to provide diagnoses of potential COVID-19 cases;
AI-enabled robots to minimize human-to-human contact;
AI applications for contact tracing;
management solutions for large volumes of data (big data); and
pandemic data management and visualization systems.

Dr. Inés Sittón
Prof. Dr. Sara Rodriguez
Dr. Lilia Muñoz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial intelligence
COVID-19
Edge computing
Machine learning
Deep intelligence
Internet of Things

Published Papers (9 papers)

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Research

16 pages, 2124 KiB

Open AccessArticle

Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer

by Sezai Tunca, Bulent Sezen and Yavuz Selim Balcioglu

Electronics 2023, 12(9), 1964; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12091964 - 23 Apr 2023

Cited by 2 | Viewed by 1976

Abstract

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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20 pages, 6058 KiB

Open AccessArticle

Deep Feature Extraction for Detection of COVID-19 Using Deep Learning

by Arisa Rafiq, Muhammad Imran, Mousa Alhajlah, Awais Mahmood, Tehmina Karamat, Muhammad Haneef and Ashwaq Alhajlah

Electronics 2022, 11(23), 4053; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11234053 - 06 Dec 2022

Cited by 1 | Viewed by 2273

Abstract

SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial phase may aid successful recovery. The rapid and exact identification of COVID-19 limits the risk of spreading this fatal disease. Patients with COVID-19 have distinctive radiographic characteristics on chest X-rays and CT scans. CXR images can be used for people with COVID-19 to diagnose their disease early. This research was focused on the deep feature extraction, accurate detection, and prediction of COVID-19 from X-ray images. The proposed concatenated CNN model is based on deep learning models (Xception and ResNet101) for CXR images. For the extraction of features, CNN models (Xception and ResNet101) were utilized, and then these features were combined using a concatenated model technique. In the proposed scheme, the particle swarm optimization method is applied to the concatenated features that provide optimal features from an overall feature vector. The selection of these optimal features helps to decrease the classification period. To evaluate the performance of the proposed approach, experiments were conducted with CXR images. Datasets of CXR images were collected from three different sources. The results demonstrated the efficiency of the proposed scheme for detecting COVID-19 with average accuracies of 99.77%, 99.72%, and 99.73% for datasets 1, 2 and 3, respectively. Moreover, the proposed model also achieved average COVID-19 sensitivities of 96.6%, 97.18%, and 98.88% for datasets 1, 2, and 3, respectively. The maximum overall accuracy of all classes—normal, pneumonia, and COVID-19—was about 98.02%. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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17 pages, 6486 KiB

Open AccessArticle

A Convolutional Neural Network for COVID-19 Diagnosis: An Analysis of Coronavirus Infections through Chest X-rays

by Avani Kirit Mehta, R. Swarnalatha, M. Subramoniam and Sachin Salunkhe

Electronics 2022, 11(23), 3975; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11233975 - 30 Nov 2022

Cited by 1 | Viewed by 1219

Abstract

Coronavirus (COVID-19) disease has not only become a pandemic but also an overwhelming strain on the healthcare industry. The conventional diagnostic methods include Antigen Rapid Kits and Reverse Transcription–Polymerase Chain Reaction (RT-PCR) tests. However, they entail several drawbacks such as low precision in diagnosis, increased time in obtaining test results, increased human–patient interaction, and high inaccuracy in the diagnosis of asymptomatic individuals, thus posing a significant challenge in today’s medical practice in curbing an extremely infectious disease such as COVID-19. To overcome these shortcomings, a machine learning (ML) approach was proposed to aid clinicians in more accurate and precise infection diagnoses. A Convolutional Neural Network was built using a sample size of 1920 chest X-rays (CXR) of healthy individuals and COVID-19-infected patients. The developed CNN’s performance was further cross-checked using the clinical results of the validation dataset comprising 300 CXRs. By converting the final output to binary, an intuitive classification of whether a specific CXR is of a healthy or a COVID-infected patient was accomplished. The statistical analysis of the CNN was: Accuracy: 95%; Precision: 96%; Specificity: 95%; Recall: 95%, and F1 score: 95%, thus, proving it to be a promising diagnostic tool in comparison to the other existing ML-based models. The datasets were obtained from Kaggle, GitHub, and European Institute for Biomedical Imaging Research repositories. The prospects of the proposed CNN lie in its flexibility to be altered and extrapolated in diagnosing other lung infections, such as pneumonia and bacterial infections, with relevant training algorithms and inputs. Additionally, the usage of other bio-imaging modalities as input datasets such as CT scans, Lung Ultrasounds and Heat Maps gives the CNN immense potential to assess for better insights on the severity of infection in both infected and asymptomatic patients as well as other related medical diagnoses. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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13 pages, 3436 KiB

Open AccessArticle

Deep-COVID: Detection and Analysis of COVID-19 Outcomes Using Deep Learning

by Muhammad Ibrahim Khalil, Saif Ur Rehman, Mousa Alhajlah, Awais Mahmood, Tehmina Karamat, Muhammad Haneef and Ashwaq Alhajlah

Electronics 2022, 11(22), 3836; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11223836 - 21 Nov 2022

Cited by 2 | Viewed by 1460

Abstract

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model’s experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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15 pages, 5588 KiB

Open AccessArticle

An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

by Inderpreet Singh Walia, Deepika Kumar, Kaushal Sharma, Jude D. Hemanth and Daniela Elena Popescu

Electronics 2021, 10(23), 2996; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10232996 - 01 Dec 2021

Cited by 28 | Viewed by 4252

Abstract

SARS-CoV-19 is one of the deadliest pandemics the world has witnessed, taking around 5,049,374 lives till now across worldwide and 459,873 in India. To limit its spread numerous countries have issued many safety measures. Though vaccines are available now, still face mask detection and maintain social distance are the key aspects to prevent from this pandemic. Therefore, authors have proposed a real-time surveillance system that would take the input video feed and check whether the people detected in the video are wearing a mask, this research further monitors the humans for social distancing norms. The proposed methodology involves taking input from a CCTV feed and detecting humans in the frame, using YOLOv5. These detected faces are then processed using Stacked ResNet-50 for classification whether the person is wearing a mask or not, meanwhile, DBSCAN has been used to detect proximities within the persons detected. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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15 pages, 641 KiB

Open AccessArticle

Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

by Lilia Muñoz, Vladimir Villarreal, Mel Nielsen, Yen Caballero, Inés Sittón-Candanedo and Juan M. Corchado

Electronics 2021, 10(23), 2901; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10232901 - 24 Nov 2021

Cited by 4 | Viewed by 2042

Abstract

The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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20 pages, 3624 KiB

Open AccessArticle

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

by Vitoantonio Bevilacqua, Nicola Altini, Berardino Prencipe, Antonio Brunetti, Laura Villani, Antonello Sacco, Chiara Morelli, Michele Ciaccia and Arnaldo Scardapane

Electronics 2021, 10(20), 2475; https://doi.org/10.3390/electronics10202475 - 12 Oct 2021

Cited by 15 | Viewed by 2776

Abstract

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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13 pages, 17371 KiB

Open AccessArticle

An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2

by Mukunthan Tharmakulasingam, Nouman S. Chaudhry, Manoharanehru Branavan, Wamadeva Balachandran, Aurore C. Poirier, Mohammed A. Rohaim, Muhammad Munir, Roberto M. La Ragione and Anil Fernando

Electronics 2021, 10(17), 2065; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10172065 - 26 Aug 2021

Cited by 8 | Viewed by 3226

Abstract

An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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16 pages, 3285 KiB

Open AccessArticle

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

by Svetozar Zarko Valtchev, Ali Asgary, Michael Chen, Felippe A. Cronemberger, Mahdi M. Najafabadi, Monica Gabriela Cojocaru and Jianhong Wu

Electronics 2021, 10(14), 1626; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10141626 - 07 Jul 2021

Cited by 8 | Viewed by 2295

Abstract

Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for COVID-19 Pandemic)

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