Evaluation of Road Safety Performance Based on Self-Reported Behaviour Data Set

Self-reported behavioural data, being often linguistic variables that represent a qualitative measure of respondents’ opinions/attitudes, are vague, uncertain, and fuzzy in nature. A road safety performance index, based on these fuzzy data, should consider this uncertainty. In this study, fuzzy numbers were used to describe self-reported behaviour on Montenegrin roads, which was further integrated into the data envelopment analysis (DEA), a technique for measuring the relative performance of decision-making units (DMUs). The vagueness of the performance scores obtained in this way was treated with grey relational analysis (GRA). GRA was applied to the cross-efficiency (CE) matrix constructed by the DEA to distinguish Montenegrin municipalities’ performance, with the main goal of describing road safety in the observed territories in the environment of uncertain/grey data. It is concluded that the proposed DEA–GRA model, based on fuzzy data, provides a more reasonable and encompassing measure of performance, and with which the overall ranking position of municipalities can be obtained.

Research on automobile insurance fraud identification based on fuzzy association rules

With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.

Decision-making system with homo and hetero elements

Experts from all over the world provide an opportunity in filling decision-making systems. But the filling of decision-making systems with data does not have an exact quantitative characteristic. It is good when the expert is completely confident in the decision. But some decisions can add up to their own internal assessment without justification or experimentation. Other decisions are hampered by past experience. To overcome this type of problem, it is necessary to develop a system that will be based on clear and fuzzy data behaviour. This article is aimed at describing the method for constructing a decision-making system on clear and fuzzy data using Petri nets.

Cognitive Modelling of the State of an Object-Based on a Thought Experiment

Introduction. At the current level of development of research in the field of artificial intelligence, it is defined as a set of technological solutions that allow simulating cognitive functions, obtaining results comparable to the results of human intellectual activity. In this regard, the problem arises of creating a technology that imitates the cognitive functions of analyzing the state of objects when the conditions of their functioning change. Analysis of the status of objects in the different problems of environmental, technical, social, political and other nature is carried out most often on information models. The peculiarity of their solution lies in the fact that it is necessary, as a rule, to restore indefinite, sometimes not amenable to quantitative analysis, dependencies and patterns. Since full-scale experiments in these subject areas are often impossible, and sometimes very expensive and even dangerous, the only research method in this case is a thought experiment using the method of experimental perturbations of the state of an object. Purpose. The purpose of the article is to create a method of cognitive modelling based on a thought experiment for the problem of assessing the state of an object from incomplete and fuzzy data. Methods. To implement the method of cognitive modelling based on a thought experiment, the method of a mental complete factor experiment (MСFE) is applied using the method of experimental perturbations. Results. To implement the method of cognitive modelling based on a mental experiment, a procedure has been created that evaluates the state (behaviour) of an object in a present or anticipated future situation based on the method of a mental complete factor experiment (MСFE) using the method of experimental perturbations. The developed procedure makes it possible to obtain solutions to the problem of predicting the state of a certain object in the future using incomplete and fuzzy data and using an expert “built in” to evaluate the forecasting results. Conclusion. The results of the research presented in this article, which are conceptual in nature, show the possibility of creating elements of technology that imitate the cognitive functions of analyzing the state of objects when changing the conditions of their functioning using a thought experiment. The developed method can be used to solve the problems of assessing the state of various objects when creating intelligent information analysis systems in order to obtain new knowledge about the object.

Neurotic Fuzzy-Data-Envelopment Analysis to Forecast Efficiency of Bank Branches

In this study the prediction of efficiency of four different Bank Branches have been done by using Neurotic Fuzzy Data Envelopment Analysis approach. In the first stage of the study, Artificial Neural Network (ANN) model has been modelled and trained using the last five years data. The data belonging any year has been taken as input of ANN, next year data has been defined as output of ANN. Fuzzyfication process has been applied to obtained predictions based on asking managers of bank branches, after Fuzzy Data Envelopment Analysis process has been applied to fuzzy values. As a result, the bank branches parameters belonging to 2021 year have been obtained. The efficiency of 2021 for bank branches have been calculated based on Fuzzy Data Envelopment Analysis (FDEA).