General method of synthesis by PLIC/FPGA digital devices to perform discrete orthogonal transformations

A general method is proposed to synthesize digital devices in order to perform discrete orthogonal transformations (DOT) on programmable logic integrated circuits (PLIC) of FPGA class. The basic and the most "slow" operation during DOT performance is the operation of multiplying by a constant factor (constant) - OMC. To perform DOT digital devices are implemented at the use of the same type of IP-cores, which allow to realize OMC. According to the proposed method, OMC is determined on the basis of picturing set over the elements of the Galois field. Due to the distributed computing of nonlinear polynomial function systems defined over the Galois field in PLIC/FPGA architecture, the reduction in the estimates of time complexity concerning OMC performance is achieved. Each non-linear polynomial function, like OMC, is realized on the basis of the same type of IP-cores according to one of the structural schemes in accordance with the requirements for the device to perform DOT. The use of IP cores significantly reduces the cost of designing a device that implements DOT in the PLIC/FPGA architecture.Keywords: digital signal processing, discrete orthogonal transformations, distributed computing, nonlinear polynomial functions, Galois fields, FPGAs, digital device

Fuzzy regression analysis using trapezoidal fuzzy numbers

© 2020 KIIE As a widely used method, regression analysis plays an increasingly important role in creating statistical models and making forecasts in the field of economics and finance. The use of traditional regression for modeling socio-economic processes is not sufficiently substantiated in some situations. Currently, a new direction is being actively developed, associated with fuzzy regression analysis and its application as an alternative to classical methods for modeling economic phenomena. Fuzzy regression methods are based on the theory of fuzzy sets. A number of methods and their modifications are proposed for constructing fuzzy regression models, but most of them use triangular fuzzy symmetric numbers. In this paper, we propose a new method for constructing linear fuzzy regression using trapezoidal fuzzy numbers. The method is based on dividing the sample using a regression model which is estimated by using the ordinary least squares. Two fuzzy regressions using triangular numbers are estimated from the formed samples, on the basis of which a fuzzy model with trapezoidal fuzzy numbers is constructed. Basing on the proposed method, a linear fuzzy model of the gross regional product as an indicator of the economic development of the Republic of Tatarstan of Russia is constructed depending on a number of factors. A comparative assessment of the quality of fuzzy regression models using triangular and trapezoidal numbers was performed

Identification of bots in social networks based on data mining technologies

© International Research Publication House This article solves the problem of intelligent models constructing and their accuracy evaluating for identifying bots in social networks. The relevance of solving this problem is noted. The construction and accuracy assessment of the neural network model, decision tree and linear regression are performed. The initial data source was Twitter social network. To collect the initial data, we used our own database, consisted of 3428 users, about half of which contained characteristic features of bots. The initial data were randomly divided into the training and testing sets, each of them included approximately 50% of the records. 15 attributes were used as the model’s input parameters, in particular, the number of symbols in the username, the user’s number of tweets, the number of readers, etc. The models construction and study was carried out on the Deductor analytical platform base. Each model was tested on data set consisted of 1719 records. For all models, the corresponding classification matrices were constructed and the first, second kind errors and the general model’s error were calculated. In terms of minimizing these errors, the neural network model showed the best results, and the linear regression model showed the worst. This allowed us to conclude, that in order to minimize classification errors, it is advisable to use a neural network model. This indicates its effectiveness and the possibility of practical use in intelligent decision-making support systems for bots identifying in social networks

Formation of a knowledge base to analyze the issue of transport and the environment

© by University of Guilan, Printed in I.R. Iran. The environmental impact of transport is significant because transport is a significant user of energy, and burns most of the world's petroleum. This issue creates air pollution, including nitrous oxides and particulates, and is a substantial contributor to global warming through emission of carbon dioxide. This article analyzes the Issue of Transport and the Environment, then solves the evaluation problem of the functional state of vehicle drivers based on the formation and use of a fuzzy knowledge base. The provided the classification of human functional state types. The expediency of using pupillometry as an objective method to analyze the pupillary reaction of a human eye to illumination change is pointed out to assess its functional state. The Analysis of the neural network approach is carried out to determine the functional state of a person's intoxication. It points out its main drawback associated with the impossibility of interpreting the solution obtained using a neural network. To eliminate this drawback and improve the efficiency of decision support to assess the functional state of vehicle drivers, it is proposed to use the mathematical apparatus of fuzzy neural networks to form fuzzy knowledge bases and provide their use in inference mechanisms. In this case, the solution to the problem will be a binary answer ("drunk", "not drunk") with the interpretation of the solution obtained in the form of a set of fuzzy rules written in a natural language understandable to humans. The tasks are set for the formation of a knowledge base to assess the functional state of drivers. The scheme of pupillogram initial data collection is described, as well as the stages of their preparation for Analysis. Pupillogram parameters that significantly characterize the pupillary response of a person to illumination change were identified by an expert method using the methods of correlation analysis: The minimum diameter of the pupil, the diameter of its half constriction, the amplitude of constriction and the time of half expansion. The structure of the generated data sample with the volume of 1000 records is described. A knowledge base was formed after their Analysis, consisting of 2632 fuzzy production rules. To assess the accuracy of determining the functional state of a person based on the knowledge base, a balanced test sample of 400 records (200 records of each class of functional state) was compiled. The test results showed that the number of type 1 errors was 1%, and the number of type 2 errors was 3%. The overall accuracy of determining the functional state of a person based on the generated knowledge base was 96%. The generated fuzzy knowledge base can be effectively used in decision support systems to assess the functional state of vehicle drivers when they undergo a pre-trip medical examination