Special Issue "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: 30 September 2021.

Special Issue Editors

Prof. Dr. Panagiotis G. Asteris
E-Mail Website
Guest Editor
Prof. Dr. Amir H. Gandomi
grade E-Mail Website
Guest Editor
Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW 2007, Australia
Interests: data mining; genetic programming; artificial intelligence; big data; smart cities
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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 papers will be 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 2000 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 (2 papers)

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Research

Article
COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs
Appl. Sci. 2021, 11(16), 7174; https://doi.org/10.3390/app11167174 - 04 Aug 2021
Viewed by 323
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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Article
Using a Process Approach to Pandemic Planning: A Case Study
Appl. Sci. 2021, 11(9), 4121; https://doi.org/10.3390/app11094121 - 30 Apr 2021
Viewed by 412
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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