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Article
Publication date: 4 August 2021

Trung Nguyen, Ray Gosine and Peter Warrian

While disruptions as a result of the COVID-19 pandemic resulted in the failure of some companies, others embraced innovative digital technologies to face the challenge posed by…

526

Abstract

Purpose

While disruptions as a result of the COVID-19 pandemic resulted in the failure of some companies, others embraced innovative digital technologies to face the challenge posed by COVID-19. The COVID-19 crisis is also an opportunity for the extractive industry (EI) sectors to review their digitalization processes. The purpose of this paper is to conduct a systematic review of infectious disease mitigation in EI and to evaluate the resilience of these industries as they address pandemic prevention and control.

Design/methodology/approach

Multi-case studies including digital and organizational responses to COVID-19 were analyzed to evaluate the readiness of health risk management (HRM) and resilience of EIs against the pandemic. The evaluation uses Google Scholar and Trends searches to compare the level of relevant activity in EIs with other industries.

Findings

Although EI sectors have various plans for minimizing pandemic impacts, unexpected disruptions and delays of the COVID-19 responses revealed many limitations of the existing HRM system. Digital technologies (e.g. artificial intelligence-based public health monitoring, digital collaboration, wearable health tracking and 3D printing) demonstrated their remarkable benefits in the pandemic responses and nontechnical elements affecting technology adoption (TA).

Originality/value

Lessons learned from the deployment of digital technologies against the pandemic help to improve the organizational capacity to deal effectively with future outbreaks and suggest lessons for the future trajectory of TA in these industries.

Details

Journal of Engineering, Design and Technology , vol. 20 no. 2
Type: Research Article
ISSN: 1726-0531

Keywords

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Book part
Publication date: 12 December 2006

Jerome Teelucksingh

The racial diversity of the Caribbean stemmed directly from the historical processes of colonialism, imperialism, slavery, and indentureship. Since the early 17th century, slaves…

Abstract

The racial diversity of the Caribbean stemmed directly from the historical processes of colonialism, imperialism, slavery, and indentureship. Since the early 17th century, slaves have been imported from Africa to work in the Caribbean. In the British West Indies, slavery was abolished in 1834 but these African slaves worked on the sugar estates until the apprenticeship was abolished on August 1, 1838. Even before 1838, planters frequently complained of labor shortages and appealed to Britain for the approval of imported labor. Thus, there were attempts by the planters in colonies, such as Trinidad, to introduce Chinese labor to the plantations. As early as 1806, there was the importation of 192 Chinese from Macao and Penang into Trinidad. However, this experiment soon failed. In 1834 and 1839, laborers from Portugal were imported into Trinidad. This soon ended as Portuguese workers could not withstand the rigorous conditions of the contract labor system.

Details

Ethnic Landscapes in an Urban World
Type: Book
ISBN: 978-0-7623-1321-1

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Publication date: 24 October 2019

Thomas A. Lucey

Abstract

Details

Intersections of Financial Literacy, Citizenship, and Spirituality: Examining a Forbidden Frontier of Social Education
Type: Book
ISBN: 978-1-78973-631-1

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Article
Publication date: 24 March 2022

Elavaar Kuzhali S. and Pushpa M.K.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150…

137

Abstract

Purpose

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

Design/methodology/approach

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

Findings

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

Originality/value

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Details

Journal of Engineering, Design and Technology , vol. 22 no. 3
Type: Research Article
ISSN: 1726-0531

Keywords

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