Search

In the paper, the intuitionistic fuzzy sets based InterCriteria Analysis (ICA) and big data systems are applied to the 2021 dataset of the National Statistical Institute with respect to the Bulgarian system of higher education. The results are produced using the Big Data platform Hortonworks and the ICA software ICrAData. Discussion is presented on the discovered relationships regarding tendencies between enrolled and graduated students, and teaching staff.

It is shown that the apparatus of the Index Matrices (IM) can be used for representation of a BigData (BD) structure. An algorithm for searching of facts in the IM-form of a given BD is described. Ideas for further extensions of the BD-structure and of its facts, are formulated.

In the current research work a multimodal biometric system is investigated. It combines the palm vein authentication and palm geometry recognition methods. The system will be used to manage the access control. The apparatus of generalized nets is applied to model the biometric authentication processes. The constructed generalized net model of biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations can be used for simulation of the real processes. The intuitionistic fuzzy evaluations are used to compare the user traits with the templates stored in database.

The concept of a temporal intuitionistic fuzzy pair is introduced. It is used for an evaluation of the results of a Game Method for Modelling applied over some areas of fire in a two-dimensional orthogonal grid of cells.

A generalized net model of information security activities in automated information systems is constructed. The processes of information security are investigated, and the separate steps are analyzed. The constructed generalized net model describes the procedures of detecting security vulnerabilities, calculating the risk of attack, implementation of threats and security policy execution.

InterCriteria analysis (ICA) is a new tool for decision making. This method can help to make decision according to the similarities or dissimilarities between the criteria. In the current research work an investigation of the Emerging Europe and Central Asia (EECA) university rankings is discussed. The aim of the ICA application over the datasets from Emerging Europe and Central Asia (EECA) university rankings is to identify the behavior of the universities in different countries. The datasets are downloaded by QS World University Rankings website. The aim of ICA application is to discover possible dependencies, correlations or opposite behavior between the universities in different countries. The indicators’ correctness is estimated by second ICA application. The resulting sets contain a segmentation of the universities activities according to the selected indicators. The investigations are made using ICrAData software.

A generalized net model of the process of classification with intuitionistic fuzzy estimations is presented. Classification methods are used to group the input data in classes according to selected labels. The standard classification procedure is improved by the step of intuitionistic fuzzy evaluations calculating. The generalized net model of classification with intuitionistic fuzzy evaluations contains the possibility for indicating the necessity of a new data selection or new algorithm determining.

An application of InterCriteria Analysis (ICA) over intuitionistic fuzzy data is presented. The proposed approach contains two steps: definition of intuitionistic fuzzy evaluations and an application of InterCriteria Analysis over intuitionistic fuzzy data. The investigation uses datasets for emissions of pollutants in the air from industrial, combustion and production processes. The datasets are extracted from the website of the National Statistical Institute of the Republic of Bulgaria.

In the paper is proposed a method for evaluation of student’s knowledge obtained in the university e-learning courses. For the assessment of the student’s solution of the respective assessment units the theory of intuitionistic fuzzy and interval valued intuitionistic fuzzy sets is used. The obtained intuitionistic fuzzy estimations reflect the degree of each student’s good performances, or poor performances, for each assessment unit and for all units.

In the current paper a generalized net model of the biometric identification is presented. The modeled process contains combination of the steps included in unimodal, multibiometric and hybrid identification systems. The constructed generalized net model can be used for analyzing and simulating the processes included in biometric identification systems. It can be helpful in procedures of managing and optimizing the biometric identification processes. In the second part of the paper a MATLAB implementation of the biometric fingerprint identification is presented.

The mathematical model for predicting the forest dynamics from [16] is extended with intuitionistic fuzzy estimations for the rules for Game Method for Modelling.

The topic in the current research work is one of the Big data frameworks known as MapReduce paradigm. This is a programming model for parallel processing of large volumes of data in distributed environment. MapReduce is applied in the clusters of commodity machines. The workflow of the MapReduce computational model is constructed using the possibilities of Generalized nets. The Generaliezed net model which is presented allows us to observe and monitor the Mapreduce framework in detail.

The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network’s processing of data and images.

The artificial neural networks (ANN) are a tool that can be used for object recognition and identification. However, there are certain limits when we may use ANN, and the number of the neurons is one of the major parameters during the implementation of the ANN. On the other hand, the bigger number of neurons slows down the learning process. In our paper, we propose a method for removing the number of the neurons without reducing the error between the target value and the real value obtained on the output of the ANN’s exit. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria.