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Open access

Darya Plinere and Arkady Borisov

A Negotiation-Based Multi-Agent System for Supply Chain Management

A supply chain is a key definition in logistics. The supply chain is a set of logistics system nodes that is linearly ordered by the material, information or financial flow in order to analyze or synthesize a specific set of logistic functions and (or) costs. Multi-agent systems are suitable for the domains that involve interactions between different people or organizations with different (possibly conflicting) goals and proprietary information. They view the supply chain as a set of intelligent agents, each responsible for one or more activities in the supply chain. The ontology, in turn, describes the domain area and becomes a mechanism to aid in understanding and analyzing the information flow between agents. The use of ontologies for multi-agent system provides the following benefits: the ontology enables knowledge structuring and sharing, increases the reliability of agent system and provides the basis for the interaction between the agents. This paper proposes a method of multi-agent system application for supply chain node cooperation and shows the interaction between agents inside one of the supply chain nodes - manufacturer node.

Open access

Sergejs Provorovs and Arkady Borisov

Use of Linear Genetic Programming and Artificial Neural Network Methods to Solve Classification Task

This paper presents a comparative analysis of linear genetic programming and artificial neural network methods to solve classification tasks. Usually classification tasks have data sets containing a large number of attributes and records, and more than two classes that will be processed using, for example, created classification rules. As a result, by using classical method to classify a large number of records, a high classification error value will be obtained. The artificial neural networks are often used to solve classification task, mostly obtaining good results. The linear genetic programming is a new direction of evolution algorithms that is not widely researched and its application areas are not well defined. However, some advantages of linear genetic programming are based on genetic operators whose structure does not require complicated calculations.

During this work approximately 400 experiments were conducted with linear genetic programming and artificial neural network methods, using various data sets with different quantity of records, attributes and classes.

Based on the results received, conclusions on possibilities of using the methods of linear genetic programming and artificial neural networks in classification problems were drawn, and suggestions for improving their performance were proposed.

Open access

Pēteris Grabusts and Arkady Borisov

Clustering Methodology for Time Series Mining

A time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

Open access

Arnis Kirshners and Arkady Borisov

Analysis of Short Time Series in Gene Expression Tasks

The article analyzes various clustering approaches that are used in gene expression tasks. The chosen approaches are portrayed and examined from the viewpoint of use of data mining clustering algorithms. The article provides a short description of working principles and characteristics of the examined methods and algorithms and the data sets used in the experiments. The article presents results of the experiments that are directly connected to the use of clustering algorithms in processing of short time series in bioinformatics tasks, solving gene expression problems as well as provides conclusions and evaluations of each used approach. An analysis of future possibilities to build a new method that is based on data mining approaches and principles but solves bioinformatics tasks that are associated with processing of short time series and the achieved results are interpreted in a way that is easy to perceive for bioinformatics experts, is presented.

Open access

Pavel Osipov and Arkady Borisov

Use of the Deferred Approach in Scientific Applications

In this paper, the implementation of security system that has strict requirements on the time of evaluation of each transaction made by the user is examined on the example of building a system for user behaviour modelling using Markov models. Evaluation of the effectiveness of both the classical approach to the implementation of a server that calculates metric of the user model and with the use of lightweight threads, as well as of a new ideology - Deferred-based event model is performed.

A number of tests of various configurations are conducted, showing the preferred server for the Deferred-based type of system as well as an approach to implementing this type of request service.

Open access

Nadezda Zenina and Arkady Borisov

Transportation Mode Choice Analysis Based on Classification Methods

Mode choice analysis has received the most attention among discrete choice problems in travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. This paper investigates performance of mode choice analysis with classification methods - decision trees, discriminant analysis and multinomial logit. Experimental results have demonstrated satisfactory quality of classification.

Open access

Pavel Osipov and Arkady Borisov

Non-Signature-Based Methods for Anomaly Detection

This paper overviews various approaches to the problem of detecting anomalous behavior within the framework of intrusion detection systems using non-signature-based methods. Each described algorithm has different underlying approach but they all show effective results in the problems of assessing the availability of the wrongfulness of the actions of an authorized user inside an information system.

The techniques discussed in the paper use Markov Chains, Hierarchical Hidden Markov Models, algorithms for filtering noise in the signal in the intrusion detection problem, as well as methods based on ontology and agents. Finally, the experimental system developed at Caldas University, Colombia is considered that uses a lot of different approaches aimed to increase anomaly detection efficiency.

Open access

Darya Plinere and Arkady Borisov

SWRL: Rule Acquisition Using Ontology

Nowadays rule-based systems are very common. The use of ontology-based systems is becoming ever more popular, especially in addition to the rule-based one. The most widely used ontology development platform is Protégé. Protégé provides a knowledge acquisition tool, but still the main issue of the ontology-based rule system is rule acquisition. This paper presents an approach to using SWRL rules Tab, a plug-in to Protégé, for rule acquisition. SWRL rules Tab transforms conjunctive rules to Jess rules in IF…THEN form.

Open access

Pavel Osipov and Arkady Borisov

Usage of Ontologies in Systems of Data Exchange

This paper describes the methods and techniques used to effectively extract knowledge from large volumes of heterogeneous data. Also, methods to structure the raw data by the automatic classification using ontology are discussed. In the first part of the article the basic technologies to realize the Semantic WEB are described. Much attention is paid to the ontology, as the major concepts that structure information on a very high level. The second part examines AVT-DTL algorithm proposed by Jun Zhang which allows one to automatically create classifiers according to the available raw, potentially incomplete data. The considered algorithm uses a new concept of floating levels of ontology; the results of the tests show that it outperforms the best existing algorithms for creating classifiers.

Open access

Andrey Bondarenko and Arkady Borisov

Research of Artificial Neural Networks Abilities in Printed Words Recognition

This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks, namely feedforward back-propagation multilayer perceptron (MLP) and Kohonen self-organizing features map (SOM).