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1

Use a cluster approach to organize and analyze data inside the cloud

Boyko N., Mykhailyshyn P., Kryvenchuk Y.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2018

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Vol. 7, No 2

15--22

EN

In this paper, description of a concept of cloud storage is offered. Cloud data storage is a model of an online storage where the data is being stored in multiple, divided between network servers that are provided for clients’ usage, mostly by a third-party company. The majority of the cloud storages (as opposed to file-exchangers) are offering almost boundless set of functions for free, by only limiting the size of the available storage (mostly a couple of gigabytes). Integrated data mining is being used for extracting potentially useful information from unprocessed data. The methods of data analysis are quite important with cloud computing. The implementation of the methods of integrated data mining inside the cloud will let the users receive the helpful information from non-structured or half-constructed web data sources. The main purpose of this work is to organize huge diverse data coming from different sources into clusters, depending on the type of data.

2

Forecasting economic and financial indicators by supply of deep and recovery neural networks

Boyko N., Ivanets A., Bosik M.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2018

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Vol. 7, No 2

3--8

EN

This paper studies the potential of the application of the Recurrent Neural Networks, as well as the Deep Neural Networks in the field of the finances and trading. In particular, their use in the stock price predicting software. The concepts of the RNNs and DNNs are provided and explained thoroughly. Both techniques RNNs and DNNs are utilized in the implementation of the stock price predicting software. Two separate versions of the software are created in order to demonstrate the main differences between the algorithms, as well as to determine the best of the two. Each version is thoroughly examined. The comparison of each of the algorithms is performed and highlighted. Examples of the implementations of the software, utilizing each of the algorithms on big volumes of stock data, for stock price prediction are provided. The article summarizes the concept of stock price prediction backed by the popular machine learning algorithms and its application in the nowadays world.

3

Building computer vision systems using machine learning algorithms

Boyko N., Dokhniak B., Korkishko V.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2018

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Vol. 7, No 2

9--14

EN

This article is devoted to the algorithm of training with reinforcement (reinforcement learning). This article will cover various modifications of the Q-Learning algorithm, along with its techniques, which can accelerate learning using neural networks. We also talk about different ways of approximating the tables of this algorithm, consider its implementation in the code and analyze its behavior in different environments. We set the optimal parameters for its implementation, and we will evaluate its performance in two parameters: the number of necessary neural network weight corrections and quality of training.

4

Use of a cloud storage for implementation informational processes

Boyko N.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2017

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Vol. 6, No 2

3--8

EN

In this paper, description of a concept of cloud Storage age is offered. One of the most practical methods of storing required information to cloud storage is also considered. Method of creating screenshots is proposed. Theoretical research is carried out and there are justified advantages and disadvantages of different methods of storing information in social networks. The best technique of creating screenshots has been practically implemented. Problems that might come up while working with approach given in this article and its solutions are stipulated. There is analyzed a necessity of setting a zoom parameter which is to transfer after transmitting size value of the picture in social networks. In the article the parameter that specifies the width of the final image and clearly affects the quality of the image is also considered. There is analyzed the effectiveness of creating an information system that saves time for such information processes as tracking the photo and its comments. In the paper the task of changing bets, which are not immediately fixed in social networks is optimized. Also there is implemented in practice a scalability problem of information processes in social networks. In the article a separation of the script is also put into practice. One part of which directly performs the request of image and downloads it to cloud storage. With help of another part the information process is transmitted on the photo where the user identifies.

5

Building computer vision systems using machine learning algorithms

Boyko N., Sokil N.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2017

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Vol. 6, No 2

15--20

EN

In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.

6

Basic concepts of evolution in agents calculating and agents system

Boyko N., Kutyuk O.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2016

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Vol. 5, No 2

69--76

EN

The basics concepts of evolution in agents calculating are discovered in this work and are showed their directions and applications. Before explaining what is agent and its description, there were given a bit of its history and the difference between agents and programs. Were given basic types of agents on examples and figures. The main task of agents is to require a large number of interactions for which most mathematical modeling methods are unsuitable. Were analyzed agent systems architecture and a description of their main parts. Principles of work with mobile and intelligent agents are considered. Furthermore, were exemplify the reasons and situations of use either intelligent agents or mobile agents. Also, their examples were showed on different examples and figures. Technology and application tools which uses in the process are represented. Analysis of JADE-technology are carried out. On the market today there are analogues of JADE, but most of the systems are relatively new and require many improvements, some are under development prototypes. Also, were given description of main tools and features of JADE. It will help a lot in elaboration of agents. Advantages and disadvantages of using agent approach are showed for creating system of data processing and they show their versatility compared with other systems.

7

Basic concepts of dynamic recurrent neural networks development

Boyko N., Pobereyko P.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2016

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Vol. 5, No 2

63--68

EN

In this work formulated relevance, set out an analytical review of existing approaches to the research recurrent neural networks (RNN) and defined precondition appearance a new direction in the field neuroinformatics – reservoir computing. Shows generalized classification neural network (NN) and briefly described main types dynamics and modes RNN. Described topology, structure and features of the model NN with different nonlinear functions and with possible areas of progress. Characterized and systematized wellknown learning methods RNN and conducted their classification by categories. Determined the place RNN with unsteady dynamics of other classes RNN. Deals with the main parameters and terminology, which used to describe models RNN. Briefly described practical implementation recurrent neural networks in different areas natural sciences and humanities, and outlines and systematized main deficiencies and the advantages of using different RNN. The systematization of known recurrent neural networks and methods of their study is performed and on this basis the generalized classification of neural networks was proposed.