Avatars-Based Decision Support System Using Blockchain and Knowledge Sharing for Processes Simulation

2021 ◽  
Vol 17 (1) ◽  
pp. 72-92
Author(s):  
Vardan Mkrttchian
Keyword(s):  
Human Capital ◽  
Decision Support ◽  
Knowledge Sharing ◽  
Process Simulation ◽  
Efficient Algorithm ◽  
Capital Management ◽  
Human Capital Management ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is an enhancement of the chapter “About Digital Avatars for Control in Virtual Industries” in the book Big Data and Knowledge Sharing in Virtual Organizations. The article discusses the capabilities of the R language for modeling Levy processes that currently most closely correspond to the nature of the organizational learning movements in sliding mode. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language. Overview of CGMY process simulation in practice is use for human capital management in the context of the implementation of digital intelligent decision support systems and knowledge management and for digital intelligent design of avatar-based control with application to human capital management.

Levy Processes Simulation for Modeling in Bioinformatics

2020 ◽  
Vol 10 (1) ◽  
pp. 55-64
Author(s):  
Anna V. Kuzmina
Keyword(s):  
Process Simulation ◽  
Efficient Algorithm ◽  
Stock Price ◽  
Lévy Processes ◽  
Applied Research ◽  
Levy Processes ◽  
Gamma Process ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article discusses the capabilities of the R language for modeling Levy processes, processes that currently most closely correspond to the nature of the evolution of stock price movements. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed with the R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed with the R language. The article is focused on an entirely new area relevant to the scope of the International Journal of Applied Research in Bioinformatics (IJARB).

Sharing and Avatar-Based Innovation Tools on Digital Economy Perspectives Using Levy Processes Simulation

2020 ◽  
Vol 11 (3) ◽  
pp. 52-63
Author(s):  
Vardan Mkrttchian ◽  
Yulia Vertakova
Keyword(s):  
Process Simulation ◽  
Efficient Algorithm ◽  
Stock Price ◽  
Lévy Processes ◽  
Sharing Economy ◽  
Levy Processes ◽  
Digital Economy ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is the Enhancement of the Mkrttchian and Vertakova article “Digital Sharing Economy” published in the International Journal of Innovation in Digital Economy (IJIDE, Volume 10, issue 2) and the chapter “Avatar-Based Innovation Tools for Managerial Perspectives on Digital Sharing Economy” in the book “Avatar-Based Models, Tools, and Innovation in the Digital Economy,” focused on an entirely new area relevant to the scope of IJIDE. The article discusses the capabilities of the R language for modeling Levy processes - processes that currently closely correspond to the nature of the evolution of stock price movements. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language, as Modelling in the Digital Globalization Era.

Strategic human capital management for a new University: a case study of Suan Dusit Rajabhat University

2015 ◽  
Vol 19 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Danai Thienphut ◽  
Suriya Jiamprachanarakorn ◽  
jirusth sirasirirusth ◽  
Rachen Boonloisong
Keyword(s):  
Human Capital ◽  
Knowledge Sharing ◽  
Success Factors ◽  
Practical Implementation ◽  
Capital Management ◽  
Human Capital Management ◽  
Content Type ◽  
Depth Interview ◽  
Strategic Human Capital ◽  
The University

Purpose – This paper aims to study the key success factors (KSFs) that determine the direction and context of a new university, Suan Dusit Rajabhat University (SDU), to formulate strategic human capital management (SHCM) for the university, and also to recommend a proposal for the human resources (HR) structure and systems that supports SHCM for a new university. Design/methodology/approach – This study used mixed methods. There were four steps, including documentary research to develop a draft of SHCM prototype, in-depth interview and knowledge-sharing technique with 17 key informants to develop the underlying final SHCM prototype, collecting the quantitative data from a questionnaire to develop a prototype of SHCM, and validation and confirmation of the suitability and feasibility of SHCM for a new university by using a focus group and knowledge-sharing technique with 14 HR experts and re-confirm for practical implementation with SDU’s executive team. Findings – The four KSFs were university positioning, talent capability, harmonization, and transformation. The SHCM formulation was categorized into two sections: components including strategy on thinking and planning, implementation and measurement; and procedures including HR policy committee, strategic and operational HR management. The HR proposal for implementation was emerging. Originality/value – The tacit knowledge in SHCM, including human capital-centric driving for KSFs and innovative HR in university transformation comprising of the strategic and operational levels, was revealed.

scholarly journals The Model of Human Capital Management in Decision Support Systems

2019 ◽  
Vol 81 (3) ◽  
pp. 290-298
Author(s):  
O. D. Kazakov ◽  
N. Y. Azarenko
Keyword(s):  
Machine Learning ◽  
Human Capital ◽  
Decision Support ◽  
Random Forest ◽  
Digital Economy ◽  
Machine Learning Algorithms ◽  
System Model ◽  
Capital Management ◽  
Human Capital Management ◽  
Economic Tools

A system model of human capital management has been developed, which, according to the authors, can be an important addition to building an effective decision support system in the field of socio-economic development of the region. The interaction of the subjects of the model can occur in order to implement innovative processes in IT organizations. The developed system model of human capital management takes into account the peculiarities of human capital management in the digital economy and includes three subsystems: “The structure of human capital”, “Tools for the development of human capital”, “Organizational and economic tools for managing human capital”. The work used data obtained in the analysis of more than 100 innovative enterprises engaged in research, including in the field of information technology. As a result of the study, marked-up data were generated on the level of development efficiency of human capital based on the initial processing of financial statements of innovative organizations. To determine the directions of improving the quality of human capital, an approach to assessing the level of efficiency of its development based on the machine learning model is presented. The values ??of the quality metrics of the following machine learning algorithms for solving the problem are presented: linear regression; Random Forest; nearest neighbors method. To classify the region’s enterprises according to the level of development efficiency of human capital based on open financial information, the Random Forest algorithm was chosen. 11 most accurate classification rules in a hierarchical sequential structure were identified and formulated. This will allow for more complete consideration of all aspects of intellectual resources management in the digital economy.

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