Viktoriya Gura
@knu.ua
the Department of European Integration Policy
Taras Shevchenko National University of Kyiv
RESEARCH, TEACHING, or OTHER INTERESTS
Business, Management and Accounting, Public Administration
FUTURE PROJECTS
Mechanisms for implementing the state policy of innovative development of social entrepreneurship in Ukraine
It is my doctoral research
Applications Invited
experts in the field of state regulation and state support of social entrepreneurship
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Volodymyr Koval, Andrii Shyshatskyi, Ruslan Ransevych, Viktoriya Gura, Oleksii Nalapko, Larysa Shypilova, Nadiia Protas, Oleksandr Volkov, Oleksandr Stanovskyi, and Olena Chaikovska
Private Company Technology Center
The object of research are decision support systems. The subject of research is the decision-making process in management problems using bio-inspired algorithms. A method for the search of solutions in the field of national security using bio-inspired algorithms is proposed. The proposed method is based on a combination of an artificial bat algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account the degree of uncertainty; ‒ numbering of bat agents (BA); ‒ placement of bat agents taking into account the degree of uncertainty about the state of the analysis object in the search space; ‒ setting the initial BA speed and the echolocation frequency of each BA; ‒ starting a local search; ‒ launching a global search; ‒ training knowledge bases of bat agents. The originality of the proposed method consists in the arrangement of bat agents taking into account the uncertainty of initial data, improved global and local search procedures taking into account the noise degree of data about the state of the analysis object. Another feature of the proposed method is the use of an improved procedure for training bat agents. The training procedure consists in learning the synaptic weights of an artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 13–21 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems
Galyna O. Chornous and Viktoriya L. Gura
European Center of Sustainable Development
The era of information economy leads to redesigning not only business models of organizations but also to rethinking the human resources paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Predictive analytics and computational intelligence will bring transformative change to HCM. This paper deals with issues of HCM optimization based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The main trends in the implementation of predictive WFA in the world and in Ukraine, as well as the need to protect business data for security of entrepreneurship and the tasks of predictive analysis in the context of proactive HCM were examined. Some models of effective integration of information systems for predictive WFA were proposed, their advantages and disadvantages were analyzed. These models combine ERP, HCM, BI, Predictive Analytics, and security systems. As an example, integration of HCM system, the analytics platform (IBM SPSS Modeler), BI system (IBM Planning Analytics), and security platform (IBM QRadar Security Intelligence Platform) for predicting the employee attrition was shown. This integration provides a cycle ‘prediction – planning – performance review – causal analysis’ to support protected data-driven decision making in proactive HCM The results of the research support ensuring the effective management of all spectrum of risks associated with the collection, storage and use of data. 
 Keywords: Workforce Analytics (WFA), Human Capital Management (HCM), Predictive Analytics, Proactive Management, BI, Information Systems (IS), Integration, Security of Entrepreneurship