Information Technology of Operative Formation and Evaluation of Relevant Alternatives of Decision-Making in Human-Machine Systems of Critical Application

Peredery, V.; Voronenko, Mariia; Borchik, Eugene; Krejčí, Jan

doi:10.1109/stc-csit.2018.8526750

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Determination of the functional dependence of the effectiveness of multi-level systems of critical purpose on the influence of external factors and the fuzzy risk of making inadequate decisions by the decision maker, as well as the construction of fuzzy cognitive models of DMP under conditions of fuzzy risk is considered in [13][14][15][16][17] In [18][19][20] the development of mathematical models and algorithms for determining and evaluating the optimal influence of external factors of the working environments on the cognitive state of decision makers.…”

Section: Review Of the Literaturementioning

confidence: 99%

Evaluation of the Influence of Environmental Factors and Cognitive Parameters on the Decision-Making Process in Human-Machine Systems of Critical Application

Perederyi,

Borchik,

Zosimov

et al. 2024

RIC

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Context. A feature of human-machine systems of critical application operating in real time is that they include as elements both technical systems and people interacting with these systems. At the same time, the main difficulties are associated not only with the improvement of hardware and software, but also with the insufficient development of methods for reliably predicting the impact of the production environment on the human factor and, as a result, on the relevance of decisions made by decision makers. As a result, the task of developing methods for determining the mutual influence of environmental factors and cognitive parameters of decision makers on the decision-making process becomes very relevant. Objective. The aim of the work is to propose methodological foundations for the development and study of fuzzy hierarchical relational cognitive models to determine the influence of environmental factors and cognitive parameters of decision makers on the DMP. Method. When building FHRCM methods of “soft computing”, methodologies of cognitive and fuzzy cognitive modeling were used, providing an acceptable formalization uncertainty of mutual influence of factors on the DMP. Results. A fuzzy cognitive model based on a fuzzy Bayesian belief network has been developed, which makes it possible to draw a connection between qualitative and quantitative assessments of mutually influencing factors on the DMP. The proposed model makes it possible to probabilistically predict the influence of factors and choose rational ways of their interaction in the DMP. Conclusions. The results of the experiments make it possible to recommend using the developed model, which takes into account the mutual influence of factors of various nature, including cognitive ones, in the DMP in order to improve the efficiency of HMSCA management as a whole.

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Section: Review Of the Literaturementioning

confidence: 99%

Evaluation of the Influence of Environmental Factors and Cognitive Parameters on the Decision-Making Process in Human-Machine Systems of Critical Application

Perederyi,

Borchik,

Zosimov

et al. 2024

RIC

View full text Add to dashboard Cite

show abstract

“…The literature on big data analysis covers a wide spectrum of studies that have used classification algorithms for decision making in different fields [21], [22], [23], and [24]. In their study [21], they reported the successful use of Naïve Bayesian www.ijacsa.thesai.org algorithm to classify and evaluate relevant alternatives for decision making in human-machine systems of critical applications. They indicated that although Naïve Bayesian is a simple classifier, trials have revealed that it is as effective as other complicated algorithms.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Alshareef¹,

Majrashi²,

Helal³

et al. 2021

IJACSA

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The decision-making process, promptly on time, is a crucial success factor in large organizations. Generally, the data warehouses of these organizations grow rapidly with the data generated from various business activities. This huge volume of data needs to be analyzed and decisions must be made quickly to meet the market challenges. Accurate knowledge extraction and its visualization from big data can guide decisionmakers to conduct key analysis and make correct predictions. This paper proposes a decision-making framework that not only takes into account knowledge extraction and visualization but also considers the security of the data. The proposed framework uses data mining techniques to extract useful patterns, then, visualizes those patterns for further analysis and decision making. The significance of the proposed framework lies in the mechanism through which it protects the data from intruders. The data is first processed and then stored in an encrypted format on the cloud. When the data is needed for analysis and decision making, a temporary copy of the data is first decrypted, and then important patterns are visualized. The proposed framework will assist managers and other decision-makers to analyze and visualize the data in real-time with an enhanced security mechanism.

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Information Technology of Operative Formation and Evaluation of Relevant Alternatives of Decision-Making in Human-Machine Systems of Critical Application

Cited by 2 publications

References 1 publication

Evaluation of the Influence of Environmental Factors and Cognitive Parameters on the Decision-Making Process in Human-Machine Systems of Critical Application

Evaluation of the Influence of Environmental Factors and Cognitive Parameters on the Decision-Making Process in Human-Machine Systems of Critical Application

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

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