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Applied Sciences
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Artificial Intelligence Computing and Applications for COVID-19
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Artificial Intelligence Computing and Applications for COVID-19

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 33122

Special Issue Editors

Prof. Dr. Panagiotis G. Asteris

E-Mail Website
Guest Editor
Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, Athens, Greece
Interests: artificial Intelligence; computational mechanics; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Amir H. Gandomi

grade E-Mail Website
Guest Editor
1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2. University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
Interests: data analytics; machine learning; evolutionary computation; engineering optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) are methods that are applied to transform the way humans will interact with machines and the role that machines will play in all spheres of human life. On one hand, the immense potential of these technologies to enhance and enrich human life has led to a growing exhilaration and excitement on their use, and on the other hand, fear and apprehension of a dystopian future where machines have taken over loom on the horizon. These techniques are considered to be a category in computer science, involved in the research and application of intelligent computers. Traditional methods for modeling and optimizing complex problems require huge amounts of computing resources, and computing-based solutions can often provide valuable alternatives for efficiently solving problems. Due to making nonlinear and complex relationships between dependent and independent variables, these techniques can be performed in the field of bioengineering with a high degree of accuracy. As such, many new intelligence models can be introduced for different applications.

The objective of this Special Issue is to disseminate research results on the prediction of COVID-19 disease and its related health care solutions. Indeed, COVID-19 has dramatically changed the way we perceive science and research, leading to enormous efforts and unprecedented rapid progress in a few months. Multidisciplinary and multi-institutional approaches are necessary to achieve this progress and move research from the bench to the bedside. Contributions from various engineering, scientific, and social settings that exploit data analytics, machine learning, data mining, and other Artificial Intelligence techniques are invited. Specifically, the focus is on the development of computational methods for the modelling, prediction, risk assessment, and severe justification of the COVID-19 pandemic phenomenon. Articles submitted to this Special Issue can also address the most significant recent soft computing, optimization algorithms, hybrid intelligent systems, and their applications in bioengineering sciences. We invite researchers to contribute original research articles and review articles that will stimulate the continuing research effort on applications of the meta-heuristic and computing techniques to assess, solve, or reveal the nature of the SARS-CoV-2.

Prof. Dr. Panagiotis G. Asteris
Prof. Dr. Amir H. Gandomi
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. Applied Sciences 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
  • artificial neural networks (ANNs)
  • computational biology/bioinformatics
  • forecasting models
  • fuzzy set theory and hybrid fuzzy models
  • swarm and evolutionary computation
  • genetic justification of critical COVID-19
  • image processing and computer vision
  • machine learning techniques
  • modelling and risk assessment of the COVID-19 pandemic phenomenon
  • novel biomarkers/ parameters of disease severity and mortality of COVID-19 patients
  • risk stratification tools for clinical evaluation and outcome of COVID-19 patients

Published Papers (9 papers)

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Research

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16 pages, 1027 KiB  
Article
Predictability of COVID-19 Infections Based on Deep Learning and Historical Data
by Rafat Zrieq, Souad Kamel, Sahbi Boubaker, Fahad D. Algahtani, Mohamed Ali Alzain, Fares Alshammari, Badr Khalaf Aldhmadi, Fahad Saud Alshammari and Marcos J. Araúzo-Bravo
Appl. Sci. 2022, 12(16), 8029; https://doi.org/10.3390/app12168029 - 11 Aug 2022
Cited by 4 | Viewed by 1547
Abstract
The COVID-19 disease has spread worldwide since 2020, causing a high number of deaths as well as infections, and impacting economic, social and health systems. Understanding its dynamics may facilitate a better understanding of its behavior, reducing the impact of similar diseases in [...] Read more.
The COVID-19 disease has spread worldwide since 2020, causing a high number of deaths as well as infections, and impacting economic, social and health systems. Understanding its dynamics may facilitate a better understanding of its behavior, reducing the impact of similar diseases in the future. Classical modeling techniques have failed in predicting the behavior of this disease, since they have been unable to capture hidden features in the data collected about the disease. The present research benefits from the high capacity of modern computers and new trends in artificial intelligence (AI), specifically three deep learning (DL) neural networks: recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM). We thus modelled daily new infections of COVID-19 in four countries (Saudi Arabia, Egypt, Italy, and India) that vary in their climates, cultures, populations, and health systems. The results show that a simple-structure RNN algorithm is better at predicting daily new infections and that DL techniques have promising potential in disease modeling and can be used efficiently even in the case of limited datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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13 pages, 751 KiB  
Article
A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews
by Chetanpal Singh, Tasadduq Imam, Santoso Wibowo and Srimannarayana Grandhi
Appl. Sci. 2022, 12(8), 3709; https://doi.org/10.3390/app12083709 - 07 Apr 2022
Cited by 46 | Viewed by 4694
Abstract
User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards [...] Read more.
User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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26 pages, 6905 KiB  
Article
In Silico Screening of Potential Phytocompounds from Several Herbs against SARS-CoV-2 Indian Delta Variant B.1.617.2 to Inhibit the Spike Glycoprotein Trimer
by Muruganantham Bharathi, Bhagavathi Sundaram Sivamaruthi, Periyanaina Kesika, Subramanian Thangaleela and Chaiyavat Chaiyasut
Appl. Sci. 2022, 12(2), 665; https://doi.org/10.3390/app12020665 - 11 Jan 2022
Cited by 8 | Viewed by 2825
Abstract
In October 2020, the SARS-CoV-2 B.1.617 lineage was discovered in India. It has since become a prominent variant in several Indian regions and 156 countries, including the United States of America. The lineage B.1.617.2 is termed the delta variant, harboring diverse spike mutations [...] Read more.
In October 2020, the SARS-CoV-2 B.1.617 lineage was discovered in India. It has since become a prominent variant in several Indian regions and 156 countries, including the United States of America. The lineage B.1.617.2 is termed the delta variant, harboring diverse spike mutations in the N-terminal domain (NTD) and the receptor-binding domain (RBD), which may heighten its immune evasion potentiality and cause it to be more transmissible than other variants. As a result, it has sparked substantial scientific investigation into the development of effective vaccinations and anti-viral drugs. Several efforts have been made to examine ancient medicinal herbs known for their health benefits and immune-boosting action against SARS-CoV-2, including repurposing existing FDA-approved anti-viral drugs. No efficient anti-viral drugs are available against the SARS-CoV-2 Indian delta variant B.1.617.2. In this study, efforts were made to shed light on the potential of 603 phytocompounds from 22 plant species to inhibit the Indian delta variant B.1.617.2. We also compared these compounds with the standard drug ceftriaxone, which was already suggested as a beneficial drug in COVID-19 treatment; these compounds were compared with other FDA-approved drugs: remdesivir, chloroquine, hydroxy-chloroquine, lopinavir, and ritonavir. From the analysis, the identified phytocompounds acteoside (−7.3 kcal/mol) and verbascoside (−7.1 kcal/mol), from the plants Clerodendrum serratum and Houttuynia cordata, evidenced a strong inhibitory effect against the mutated NTD (MT-NTD). In addition, the phytocompounds kanzonol V (−6.8 kcal/mol), progeldanamycin (−6.4 kcal/mol), and rhodoxanthin (−7.5 kcal/mol), from the plant Houttuynia cordata, manifested significant prohibition against RBD. Nevertheless, the standard drug, ceftriaxone, signals less inhibitory effect against MT-NTD and RBD with binding affinities of −6.3 kcal/mol and −6.5 kcal/mol, respectively. In this study, we also emphasized the pharmacological properties of the plants, which contain the screened phytocompounds. Our research could be used as a lead for future drug design to develop anti-viral drugs, as well as for preening the Siddha formulation to control the Indian delta variant B.1.617.2 and other future SARS-CoV-2 variants. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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14 pages, 2112 KiB  
Article
COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images
by Chandrakanta Mahanty, Raghvendra Kumar, Panagiotis G. Asteris and Amir H. Gandomi
Appl. Sci. 2021, 11(23), 11423; https://doi.org/10.3390/app112311423 - 02 Dec 2021
Cited by 21 | Viewed by 2168
Abstract
The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used [...] Read more.
The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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25 pages, 2479 KiB  
Article
Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data
by Joanna Henzel, Joanna Tobiasz, Michał Kozielski, Małgorzata Bach, Paweł Foszner, Aleksandra Gruca, Mateusz Kania, Justyna Mika, Anna Papiez, Aleksandra Werner, Joanna Zyla, Jerzy Jaroszewicz, Joanna Polanska and Marek Sikora
Appl. Sci. 2021, 11(22), 10790; https://doi.org/10.3390/app112210790 - 15 Nov 2021
Cited by 4 | Viewed by 2408
Abstract
New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when [...] Read more.
New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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22 pages, 6340 KiB  
Article
COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs
by Amal A. Al-Shargabi, Jowharah F. Alshobaili, Abdulatif Alabdulatif and Naseem Alrobah
Appl. Sci. 2021, 11(16), 7174; https://doi.org/10.3390/app11167174 - 04 Aug 2021
Cited by 19 | Viewed by 3504
Abstract
COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is [...] Read more.
COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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Graphical abstract

18 pages, 683 KiB  
Article
Using a Process Approach to Pandemic Planning: A Case Study
by Hana Tomaskova and Erfan Babaee Tirkolaee
Appl. Sci. 2021, 11(9), 4121; https://doi.org/10.3390/app11094121 - 30 Apr 2021
Cited by 7 | Viewed by 2847
Abstract
The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we [...] Read more.
The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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Review

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19 pages, 2527 KiB  
Review
A Systematic Literature Review on Fake News in the COVID-19 Pandemic: Can AI Propose a Solution?
by Tanvir Ahmad, Eyner Arturo Aliaga Lazarte and Seyedali Mirjalili
Appl. Sci. 2022, 12(24), 12727; https://doi.org/10.3390/app122412727 - 12 Dec 2022
Cited by 7 | Viewed by 6285
Abstract
The COVID-19 pandemic has led to an incredible amount of fake news and conspiracy theories around the world. Calls for the integration of COVID-19 and fake news-related research have been advanced in various fields. This paper aims to unpack a structured overview of [...] Read more.
The COVID-19 pandemic has led to an incredible amount of fake news and conspiracy theories around the world. Calls for the integration of COVID-19 and fake news-related research have been advanced in various fields. This paper aims to unpack a structured overview of previous research topics and findings and identify gaps. Our goal in this systematic review is to (a) synthesize the selected earlier studies, (b) offer researchers a structural framework for future COVID-19 and fake news research, and (c) recommend relevant areas for future research. In this study, we focus on eighty conceptual and empirical studies on misinformation of COVID-19-related news on social media. We identify vital publications and methodological and theoretical approaches that exist in the COVID-19 literature. The articles were systematically analyzed, focusing on the research context and time frame, data collection/analysis procedures, and equivalence issues. While COVID-19 research has been advancing significantly over the past couple of months, numerous questions remain unexplained in the domain of the social media landscape. For example, our review suggests that researchers should begin to concentrate on a process framework blending Artificial Intelligence (AI) to curb the fake news problem. This can be achieved in all three phases, e.g., the study of individual decisions and experiences, the experiences of groups and organizations and the interactions between them, and finally, the interactions at the broadest level (micro, meso, and macro stages). Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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28 pages, 4700 KiB  
Review
Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022
by Fan Liu, Delong Chen, Xiaocong Zhou, Wenwen Dai and Feng Xu
Appl. Sci. 2022, 12(8), 3895; https://doi.org/10.3390/app12083895 - 12 Apr 2022
Cited by 6 | Viewed by 3050
Abstract
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based [...] Read more.
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic. Full article
(This article belongs to the Special Issue Artificial Intelligence Computing and Applications for COVID-19)
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