A hybrid intelligent system for risk assessment based on unstructured data

Skvortsova, Maria; Terekhov, Valery; Grout, Vic

doi:10.1109/eiconrus.2017.7910616

Cited by 14 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis of publications related to the risk issues concludes that risk is a complex phenomenon comprising many diverse and sometimes contradicted components. In general, the risk is defined as a set of hazards having the potential of a negative outcome which can bring unpredictable losses [3], [7], [8]. Threat-making risk is the risk where the possibility of a negative outcome is increased proportionally to the negative actions.…”

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

“…Information risk is considered as an event that directly impacts information integrity, confidentiality, and availability [3], [7], [8]. The information risks can be systemised using the criteria represented in Fig.…”

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

“…The system applied for the information risk analysis should have a known structure including input and output parameters [7], [8]. For this system, the input variables are the values of five risk factors in the interval [0, 1] described using a linguistic term-set (very low, low, average, high, very high) as shown in Table. 1.…”

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

See 2 more Smart Citations

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Skvortsova

Terekhov

Proletarsky

et al. 2020

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

Self Cite

View full text Add to dashboard Cite

Decision-making is one of the essential functions of any manager. The means of visualization play a crucial role in supporting the manager in the decision-making process. This paper discusses the visualisation of information risks based on integral indicators. It provides a description of a mathematical model of the method of dynamic meta-anamorphosis and an example of its practical implementation. The advantages of using a hybrid approach to the development of dynamic metaanamorphosis are discussed.

show abstract

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

Section: A Theoretical Investigation Of the Risk Domainmentioning

confidence: 99%

See 1 more Smart Citation

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Skvortsova

Terekhov

Proletarsky

et al. 2020

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The generalized structure of HIS risk assessment and the functions of its structure of modules were discussed in detail in (Skvortsova et al, 2017). The peculiarity of the structure of the "Intellectual data analysis" module of the risk controlling HIS under consideration is that in its work it uses one of the methods of cognitive visualization, namely the method of dynamic meta-anamorphosis.…”

Section: Description Of the Module "Intellectual Data Analysi" In Rismentioning

confidence: 99%

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

Skvortsova¹,

Terekhov²,

Chernenky³

et al. 2019

Proceedings of the 16th International Conference on Applied Computing 2019

Self Cite

View full text Add to dashboard Cite

Currently developed hybrid intelligent systems that use a combination of traditional data processing methods and intelligent methods are able to analyze and assess the existing risks in all areas of human activity. This article shows the positive effect of cognitive computer visualization as a means of activating the mechanisms of visual-figurative thinking of the person making a decision in the tasks of controlling the integral indicator of risks. The essence of the anamorphic method is shown, which is the transition from one visual image built on the basis of Euclidean metrics to another visual image based on the quantitative metric of the selected risk. The mathematical formulation of the dynamic meta-anamorphine method is disassembled. The advantages of the constructed meta-anamorphosis in the risk controlling HIS are given. The problem of calculating the integral risk index, which combines the risks of different spheres of activity and visualizations built on this basis, is analyzed. Conclusions are made regarding the construction of a hybrid model based on the hierarchy analysis method and the dynamic meta-anamorphosis method. The directions of further research are determined.

show abstract

“…Hybridization is performed for the purpose of investing better methods of problem solving [CHENNAKE-SAVA 2008]. Using a system created on the basis of a hybrid approach, existing methods and algorithms can be improved and more effective approaches can be developed to solve existing problems [SKVORTSOVA et al 2017].…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for modelling the salinity of the Tafna River in Algeria

Houari

Hartani

Rémini

et al. 2019

Journal of Water and Land Development

View full text Add to dashboard Cite

In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).

show abstract

A hybrid intelligent system for risk assessment based on unstructured data

Cited by 14 publications

References 4 publications

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

A hybrid model for modelling the salinity of the Tafna River in Algeria

Contact Info

Product

Resources

About