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1.
Journal of the International Academy for Case Studies ; 27:1-3, 2021.
Article in English | ProQuest Central | ID: covidwho-1513617

ABSTRACT

[...]this note advocates for and encourages companies to adopt Industry 5.0. According to the study, reducing working hours can increase economic efficiency by increasing labor. [...]the economic and productivity implications of Industry 5.0 on the manufacturing industry and broader economy can be examined, with the conclusion that Industry 5.0 will generate more jobs than it will eliminate.

2.
Preprint in English | EuropePMC | ID: ppcovidwho-292004

ABSTRACT

Background: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Methods: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.

3.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
4.
International Journal of Hybrid Intelligent Systems ; 17(1-2):71-85, 2021.
Article in English | ProQuest Central | ID: covidwho-1318372

ABSTRACT

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

5.
Curr Med Imaging ; 2021 Jul 12.
Article in English | MEDLINE | ID: covidwho-1310013

ABSTRACT

BACKGROUND: This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). OBJECTIVE & METHOD: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. RESULTS: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. CONCLUSION: Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.

6.
Data Science for COVID-19 ; : 175-194, 2021.
Article in English | PMC | ID: covidwho-1244653
7.
Journal of the International Academy for Case Studies ; 26(5):1-2, 2020.
Article in English | ProQuest Central | ID: covidwho-915054

ABSTRACT

According to the scenario of novel coronavirus disease 2019 (COVID-19), it has created a negative impact on businesses all over the world. Artificial intelligence (AI) can help to reduce the crisis of world economics. AI has offered the details analysis of COVID-19 pandemics in the business including probable, pessimistic forecasts, and optimistic scenarios (Mondal, Bharati, Podder, & Podder, 2020). The rapid progress of deep learning and machine learning has made massive strides in natural language processing (NLP) and cognitive computing developing the frameworks for AI business software or applications (Bharati, Podder, & Mondal, 2020). Artificial intelligence is ready to reshape most parts of emerging business sectors;i.e., finance, HRM, labor, advertising, retail, business strategy, supply chain management, services, marketing, and information technologies. Thus, savvy entrepreneurs are investigating the capability of image, facial, and voice recognition to lessen the barriers and costs of doing business, which severally ought to develop productivity. In developing markets, artificial intelligence offers a scientific method to the economic or business challenges faced by firms, governments, and people behind the financial pyramid. Integrating information from various sources (for example, traditional channels, social media, and websites) can assist firms to improve sound business policies, develop data management stages, spur economic growth and build innovative business frameworks (Arora & Rahman, 2017). According to (Strusani & Houngbonon, 2019), companies in developing countries can conduct advanced AI-based technology to enhance service delivery and autonomous goods, improve mobile AI-based apps and develop production automation for credit and services access. By increasing productivity, government services, financial solutions, and AI-based tools can create prospects and advance markets. Using AI, the private and public sectors in developing markets can explore advancing solutions and work together to combat against inequality and poverty during pandemics while increasing economic prosperity and mobility. Despite growing interest, research contributions to business-related artificial intelligence in developing economies keep on negligible. Consequently, empirical and theoretical studies will be required in the experiments of AI in developing markets for present and future pandemics. In future, researchers can utilize AI-powered tools i.e. image recognition, facial recognition, NLP, machine learning, and speech recognition method. Their research can be solved the business problem in their research about business problems in developing markets during COVID-19. The Journal of the International Academy for Case Studies (JIACS) provides an opportunity for researchers in present and future challenging times. With a number of data available now, state-of-the-art data analysis methods are required to expand rule-based data that explores and often-insufficient purposive.

8.
Inform Med Unlocked ; 20: 100391, 2020.
Article in English | MEDLINE | ID: covidwho-634281

ABSTRACT

Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction. The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.

9.
Inform Med Unlocked ; 20: 100374, 2020.
Article in English | MEDLINE | ID: covidwho-598717

ABSTRACT

This paper describes different aspects of novel coronavirus disease (COVID-19), presents visualization of the spread of the infection, and discusses the potential applications of data analytics on this viral infection. Firstly, a literature survey is done on COVID-19 highlighting a number of factors including its origin, its similarity with previous coronaviruses, its transmission capacity, its symptoms, etc. Secondly, data analytics is applied on a dataset of Johns Hopkins University to find out the spread of the viral infection. It is shown here that although the disease started in China in December 2019, the highest number of confirmed cases up to June 04, 2020 is in the USA. Thirdly, the worldwide increase in the number of confirmed cases over time is modelled here using a polynomial regression algorithm with degree 2. Fourthly, classification algorithms are applied on a dataset of 5644 samples provided by Hospital Israelita Albert Einstein of Brazil in order to diagnose COVID-19. It is shown here that multilayer perceptron (MLP), XGBoost and logistic regression can classify COVID-19 patients at an accuracy above 91%. Finally, a discussion is presented on the potential applications of data analytics in several important factors of COVID-19.

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