Schemes for Verification of Resources in the Cloud: Comparison of the Cloud Technology Providers

Sulich, Adam; Zema, Tomasz; Zema, Piotr

doi:10.1007/978-3-030-40417-8_9

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…These changes enable organizations to gain a competitive advantage [35,36] and possibly sustainability [37,38] based on digitalization [9,39] and technology implementation [40,41] called digitization [42]. Digitization involves the use of (Information and Communication Technologies) ICT solutions to increase the efficiency of the existing processes [43,44] through automation [45,46]. However, in the European context [42], digitization as the core of the Industry 4.0 concept [47,48] is much more than just automation development [44,49].…”

Section: Literature Review 21 Definitional Issuesmentioning

confidence: 99%

“…Digitization involves the use of (Information and Communication Technologies) ICT solutions to increase the efficiency of the existing processes [43,44] through automation [45,46]. However, in the European context [42], digitization as the core of the Industry 4.0 concept [47,48] is much more than just automation development [44,49]. Digital transformation is business transformation enabled by digitalization [12,50].…”

Section: Literature Review 21 Definitional Issuesmentioning

confidence: 99%

“…Strategies described in the Economic Scale School perspective [77], present the possibilities of implementing digital solutions in large-scale production, commercial, and service processes [34]. These are related to running Industry 4.0 technologies: big data, automatization, Internet of Things, artificial intelligence, additive manufacturing, and cloud computing [44,78]. In the energy sector, these solutions affect the scale and the speed of The transformation of the economy towards a digital and sustainable one is a subject of the European Commission studies and issued documents [52,53].…”

Section: Digital Strategiesmentioning

confidence: 99%

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Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions

et al. 2021

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Digital transformation is a concept based on the use of digitalization and digitization. Digitalization leads to change of business models and provides a competitive advantage also in the energy sector. The process of change towards a digital business requires a specific strategy type, aimed to solve problems with uncertainty caused by Industry 4.0 implementation. This paper aims to propose a theoretical model combining different digitalization strategies and business models. Their theoretical foundations were discussed in the literature review part and related empirical research questions were attempted to be answered by the reference method analysis. The quantitative method of analysis was based on the secondary data from Eurostat for all EU member states and backed the theoretical part in terms of ICT variables. The novelty of this research is based on Hellwig’s reference method used in management sciences and the presented managerial implications. The discussed challenges of the energy sector are related to the digital strategy implementation, relationships between digital transformation and business models, and solutions for such issues as strategy communication and new roles for managers, who should become digital leaders in the energy sector organizations. The main consequence of the proposed model in this study, for the energy sector companies’ managers, is that uncertainty in modern energy sector organizations is more related to employees and their technical skills than implemented ICT itself.

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Section: Literature Review 21 Definitional Issuesmentioning

confidence: 99%

Section: Literature Review 21 Definitional Issuesmentioning

confidence: 99%

Section: Digital Strategiesmentioning

confidence: 99%

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Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions

et al. 2021

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“…This type of architecture has several advantages over grid computing and other types of computing configurations and infrastructures [15]. The current literature collects numerous reviews on CC [16], [17] [18].…”

Section: 1mentioning

confidence: 99%

Deep Learning Assisted Cognitive Diagnosis for the D-Riska Application

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Zornoza

Tesoriero

et al. 2021

Preprint

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In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class. Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 \% of Accuracy, 94 \% of Precision, 91 \%, of Recall and 92\% of F1-Score.

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“…This type of architecture has several advantages over grid computing and other types of computing configurations and infrastructures (Srivastava and Khan 2018). The current literature collects numerous reviews on CC (Sulich et al 2020), (Singh and Yadav 2019) (Verma and Sharma 2019).…”

Section: Related Workmentioning

confidence: 99%

Deep learning assisted cognitive diagnosis for the D-Riska application

et al. 2021

View full text Add to dashboard Cite

In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.

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Schemes for Verification of Resources in the Cloud: Comparison of the Cloud Technology Providers

Cited by 8 publications

References 22 publications

Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions

Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions

Deep Learning Assisted Cognitive Diagnosis for the D-Riska Application

Deep learning assisted cognitive diagnosis for the D-Riska application

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