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Dina Vladimirovna Kataseva
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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Neural Network Models for Assessing the Financial Condition of Enterprises for Supply Chain Maxim Alexandrovich Popov; Alexey Sergeevich Katasev; Amir Muratovich Akhmetvaleev; Dina Vladimirovna Kataseva
International Journal of Supply Chain Management Vol 8, No 5 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (242.829 KB)

Abstract

The paper deals with the task of assessing the financial condition of enterprises. To solve it, we prove the necessity of building a neural network model for supply chain. A set of financial ratios is defined as the input parameters of the model: the current liquidity ratio of the enterprise, the equity ratio, the equity turnover ratio, and the return on equity ratio. The output parameters were the types of the financial condition of enterprises: an unstable state (regression), a normal state (stable) and an absolutely stable state (progression). The volume of input data for building neural network models for assessing the financial condition of enterprises amounted to 210 records. The construction and evaluation of the effectiveness of neural network models are based on the analytical platform Deductor. There have been built 32 modifications of neural network models with different architectures and trained with different samples formed randomly from the source data. To assess the effectiveness of the models built, a technique has been developed, which includes the stages of testing neural networks, evaluating their accuracy and average classification error taking into account weighting factors assigned by an expert. The results of calculations of errors of the first and second type for each financial condition, as well as the average total classification error,  are presented. The best model with a minimum average classification error, which is a single-layer perceptron with 10 hidden neurons, was chosen. The classification accuracy of the model was about 98%. The neural network model is adequate and can be effectively used to solve the problem of assessing the financial condition of enterprises.
Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing Mikhail Mikhailovich Chupin; Alexey Sergeevich Katasev; Amir Muratovich Akhmetvaleev; Dina Vladimirovna Kataseva
International Journal of Supply Chain Management Vol 8, No 5 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (288.846 KB)

Abstract

This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity.
The Neural Network Model of DDoS Attacks Identification for Information Management Fail Fanilevich Mukhametzyanov; Alexey Sergeevich Katasev; Amir Muratovich Akhmetvaleev; Dina Vladimirovna Kataseva
International Journal of Supply Chain Management Vol 8, No 5 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (203.626 KB)

Abstract

The paper discusses the concept and problem of identifying DDoS attacks for information management. The main starting mechanisms and types of DDoS attacks are analyzed. To identify them, signature and behavioral methods of analyzing network traffic are used. Analysis of the advantages and disadvantages of these methods actualized the need for their combined use. To detect and classify DDoS attacks, the need to develop and use a neural network model has been updated. The training and testing of the model were made on the initial data from the NSL-KDD set. All lines in this set are represented as sequences of TCP packets, UDP packets, and ICMP packets of network traffic transmitted from the source of the attack to the attacked network node. The total sample size was 8067 lines. Of these, half of the data corresponded to DDoS attacks, and the rest of the data characterized clear connections. The Deductor modelling environment was used to build the neural network model. The constructed neural network model was a single-layer perceptron with 11 input neurons, 23 hidden neurons and 1 output neuron. The accuracy of the constructed model was calculated based on contingency tables. The accuracy of the initial data classification at the training stage was 97.94%. The classification accuracy at the testing stage was 97.87%. To assess the quality of the neural network model, the errors of the first (0.93%) and second (3.3%) type are calculated. Testing the model showed good results since almost all DDoS attacks were successfully classified. Thus, the neural network model for detecting DDoS attacks has successfully solved the task of identifying and classifying malicious network connections.
Co-Authors Alexey Sergeevich Katasev Amir Muratovich Akhmetvaleev Fail Fanilevich Mukhametzyanov Maxim Alexandrovich Popov Mikhail Mikhailovich Chupin