Now showing 1 - 10 of 18
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FINANCIAL FORECASTING USING NEURAL NETWORKS

2016-12-10, Zorins, Aleksejs

This paper presents an application of neural networks to financial time-series forecasting. No additional indicators, but only the information contained in the sales time series was used to model and forecast stock exchange index. The forecasting is carried out by two different neural network learning algorithms – error backpropagation and Kohonen self-organising maps. The results are presented and their comparative analysis is performed in this article.

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INFORMATIONAL WARFARE – INFLUENCE ON INFORMATIONAL STRUCTURES

2019, Grabusts, Pēteris, Zorins, Aleksejs, Teilāns, Artis

The concept of information warfare encompasses the use of information and communication technologies to gain an advantage over a potential opponent. The information warfare is the manipulation with the information that trusts the goal, so that the goal should make decisions about its interests in the interests of opponents. Information structures are treated as systems that process different types of information, provide storage and access to users. Such structures may enclose neural networks, self-learning systems etc. They need to be ready to learn, respond to threats and ensure their safety, which is topical in today's information warfare. This paper will address aspects related to the security of information systems from a system theory point of view. The knowledge base of information structures can be elements of artificial intelligence, which security must be protected against various threats. The authors considers artificial neural networks to be one of the potential threats in the context of information warfare.

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APPROACHES AND SOLUTIONS FOR SIGN LANGUAGE RECOGNITION PROBLEM

2018, Zorins, Aleksejs, Grabusts, Pēteris

The goal of the paper is reviewing several aspects of Sign Language Recognition problems focusing on Artificial Neural Network approach. The lack of automated Latvian Sign Language has identified and proposals of how to develop such a system have made. Tha authors use analytical, statistical methods as well as practical experiments with neural network software. The main results of the paper are description of main Sign Language Recognition problem solving methods with Artificial Neural Networks and directions of future work based on authors’ previous expertise.

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SAFETY OF ARTIFICIAL SUPERINTELLIGENCE

2019, Zorins, Aleksejs, Grabusts, Pēteris

The paper analyses an important problem of cyber security from human safety perspective which is usually described as data and/or computer safety itself without mentioning the human. There are numerous scientific predictions of creation of artificial superintelligence, which could arise in the near future. That is why the strong necessity for protection of such a system from causing any farm arises. This paper reviews approaches and methods already presented for solving this problem in a single article, analyses its results and provides future research directions.

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ANALYSIS OF CONTACTLESS RADIO FREQUENCY IDENTIFICATION TECHNOLOGIES AND ITS USAGE IN REZEKNE CITY INFRASTRUCTURE OBJECTS

2016-04-20, Zalužinskis, Artūrs, Zorins, Aleksejs

Author made the analysis of usage of contactless radio frequency identification technologies in Rezekne city infrastructure objects. Author provides the main types of radio frequency hardware, working principles and worldwide usage statistics. In the conclusion of the paper, author describes the further development opportunities of the radio frequency identification technologies.

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EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION

2021, Grabusts, Pēteris, Zorins, Aleksejs

Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines – particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.

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LATVIAN SIGN LANGUAGE RECOGNITION CLASSIFICATION POSSIBILITIES

2017, Zorins, Aleksejs, Grabusts, Pēteris

There is a lack of automated sign language recognition system in Latvia while many other countries have been already equipped with such a system. Latvian deaf society requires support of such a system which would allow people with special needs to enhance their communication in governmental and public places. The aim of this paper is to recognize Latvian sign language alphabet using classification approach with artificial neural networks, which is a first step in developing integral system of Latvian Sign Language recognition. Communication in our daily life is generally vocal, but body language has its own significance. It has many areas of application like sign languages are used for various purposes and in case of people who are deaf and dumb, sign language plays an important role. Gestures are the very first form of communication. The paper presents Sign Language Recognition possibilities with centre of gravity method. So this area influenced us very much to carry on the further work related to hand gesture classification and sign’s clustering.

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SORTING ALGORITHMS REALIZATION AND THEIR FEATURES

2022-05-07, Ivars Cabuļevs, Zorins, Aleksejs

The performance of computers and programs is the most important thing for a user nowadays. Sorting algorithms appeared in the 19th century, but nowadays, developers often forget about the effectiveness of these algorithms and always use only a couple of algorithms, which are not always the best solution for certain tasks. This slows down the performance of certain important applications for professionals as well as for ordinary users. In this work was told and implemented different numerical sorting algorithms. The development environment here was “Microsoft Visual Studio 2017”. Was used the C++ programming language. Knowledge of sorting algorithms will always help you to optimize your program (if there used sorting), which will have a positive impact on user feedback about your application also this knowledge has a positive effect on the thought processes, allowing you to make the right decisions in the shortest time.

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DATA PREPROCESSING METHODS FOR INTERVAL BASED NEURAL NETWORK PREDICTION

2007, Zorins, Aleksejs

The paper examines a task of forecasting stock prices of Riga Stock exchange by the use of interval value prediction approach, which is carried out by modified Kohonen neural network learning algorithm. The data preprocessing methods are analyzed and implemented here to solve stock prices prediction task. The proposed data preprocessing methods has been experimentally tested with two types of artificial neural networks.

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The Influence of Hidden Neurons Factor on Neural Nework Training Quality Assurance

2015, Grabusts, Pēteris, Zorins, Aleksejs

The work shows the role of hidden neurons in the multilayer feed-forward neural networks. The numeric expression of hidden neurons is usually determined in each case empirically. The methodology for determining the number of hidden neurons are described. The neural network based approach is analyzed using a multilayer feed-forward network with backpropagation learning algorithm. We have presented neural network implementation possibility in bankruptcy prediction (the experiments have been performed in the Matlab environment). On the base of bankruptcy data analysis the effect of hidden neurons to specific neural network training quality is shown. The conformity of theoretical hidden neurons to practical solutions was carried out.