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Dr. Siva Chandrasekaran

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Guest Editor

Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne 3122, Australia
Interests: Industrial Internet of Things; machine learning; data analytics; engineering education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Akbar Rhamdhani

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Guest Editor

Department of Mechanical Engineering and Product Design Engineering, School of Engineering, Swinburne University of Technology, Melbourne 3122, Australia
Interests: advanced metals/materials refining and impurities removal; extractive metallurgy; solar processing of metals and materials

Special Issue Information

Dear Colleagues,

Digital transformation is empowering people and organisations to make cultural and behavioural changes in new ways. It is important to understand how technology evolves and how each new upgrade of digital technology compounds existing technologies to create a better environment. Future technological developments in digital transformation, such as the continuous evolution of smart devices and rise of artificial intelligence (AI) technology, Internet of Things (IoT)—embedded software and electronic systems and Cloud computing—data storage and maintenance, augmented reality/mixed reality, cybersecurity and data privacy, Big Data, etc., are promising.

This Special Issue is a significant work that responds directly to issues that are crucial to the future of the following sectors: industrial operations, healthcare, defence, education, transport, food and beverage processing, farming, water irrigation and distribution, and energy production and maintenance. Technological evolution and digitalisation are emphasising major changes in human–machine interactions, operational agility, customer experience, operation agility, workforce enablement, culture and leadership, and digital technology integration.

Proposals are invited to discuss and present your research on innovative practices of digital transformation as well as technological evolution and digitalisation. Drawing on various case studies around the globe specifically focused on sustainable technologies and applications, this Special Issue outlines the rationale and implementation of several recent innovative practices of digital transformation.

Papers in all areas of engineering practice are invited, with a particular emphasis on the following current issues in engineering, and academic and industrial practices:

Artificial Intelligence technologies;
Machine learning and deep learning;
Augmented reality and mixed reality;
Internet of Things;
Digital twin;
Cloud computing;
Big Data and big data architecture;
Cybersecurity and data privacy.

Dr. Siva Chandrasekaran
Prof. Dr. Akbar Rhamdhani
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. Sustainability 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

digital transformation
digitalisation
technological development
sustainable applications and technologies

Published Papers (1 paper)

Research

19 pages, 1600 KiB

Open AccessArticle

2dCNN-BiCuDNNLSTM: Hybrid Deep-Learning-Based Approach for Classification of COVID-19 X-ray Images

by Anika Kanwal and Siva Chandrasekaran

Sustainability 2022, 14(11), 6785; https://doi.org/10.3390/su14116785 - 1 Jun 2022

Cited by 6 | Viewed by 1902

Abstract

The coronavirus (COVID-19) is a major global disaster of humankind, in the 21st century. COVID-19 initiates breathing infection, including pneumonia, common cold, sneezing, and coughing. Initial detection becomes crucial, to classify the virus and limit its spread. COVID-19 infection is similar to other types of pneumonia, and it may result in severe pneumonia, with bundles of illness onsets. This research is focused on identifying people affected by COVID-19 at a very early stage, through chest X-ray images. Chest X-ray classification is a beneficial method in the identification, follow up, and evaluation of treatment efficiency, for people with pneumonia. This research, also, considered chest X-ray classification as a basic method to evaluate the existence of lung irregularities in symptomatic patients, alleged for COVID-19 disease. The aim of this research is to classify COVID-19 samples from normal chest X-ray images and pneumonia-affected chest X-ray images of people, for early identification of the disease. This research will help people in diagnosing individuals for viruses and insisting that people receive proper treatment as well as preventive action, to stop the spread of the virus. To provide accurate classification of disease in patients’ chest X-ray images, this research proposed a novel classification model, named 2dCNN-BiCuDNNLSTM, which combines two-dimensional Convolutional Neural Network (CNN) and a Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCuDNNLSTM). Deep learning is known for identifying the patterns in available data that will be helpful in accurate classification of disease. The proposed model (2dCNN and BiCuDNNLSTM layers, with proper hyperparameters) can differentiate normal chest X-rays from viral pneumonia and COVID-19 ones, with high accuracy. A total of 6863 X-ray images (JPEG) (1000 COVID-19 patients, 3863 normal cases, and 2000 pneumonia patients) have been engaged, to examine the achievement of the suggested neural network; 80% of the images dataset for every group is received for proposed model training, 10% is accepted for validation, and 10% is accepted for testing. It is observed that the proposed model acquires the towering classification accuracy of 93%. The proposed network is used for predictive analysis, to prompt people regarding the risk of early detection of COVID-19. X-ray images help to classify people with COVID-19 variants and to indicate the severity of disease in the future. This study demonstrates the effectiveness of the proposed CUDA-enabled hybrid deep learning models, to classify the X-ray image data, with a high accuracy of detecting COVID-19. It reveals that the proposed model can be applicable in numerous virus classifications. The chest X-ray classification is a commonly available and reasonable approach, for diagnosing people with lower respiratory signs or suspected COVID-19. Therefore, it is demonstrated that the proposed model has an efficient and promising accomplishment for classifying COVID-19 through X-ray images. The proposed hybrid model can, efficiently, preserve the comprehensive characteristic facts of the image data, for more exceptional concluding classification results than an individual neural network. Full article

(This article belongs to the Special Issue Innovative Practices of Digital Transformation: Technological Evolution and Digitalization in Modern Era)

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