Scientific Journal of Astana IT University

4-14

Authors: O. Bielienkova, K. Izmailova, Y. Zapiechna, Y. Loktionova

The article deals with the issue of ensuring the competitiveness of construction contractors depending on the level of business confidence, which is esteemed as the amount paid on schedule construction contracts. To improve the competitiveness of enterprises the authors propose indicators to identify the existing potential for efficiency enhancement and ensuring competitiveness depending on the level of business confidence of the enterprise. Indicators of competitiveness of construction companies are determined by fuzzy sets, including pricing policy, the efficiency of fixed assets and the level of diversification. The main direct and indirect factors of competitiveness of the contracting enterprise are outlined.

32-41

Authors: S. Bushuyev, V. Bushuieva, H. Tanaka

Agile transformation is a necessary process for companies in various fields of activity to ensure their competitiveness in modern business conditions when the uniformity of production processes and the growth of the level of customer (client) demands reduce the impact of traditional ones that remain competitive. Modern business is a «customer-oriented» business, in which instead or in addition to technological or marketing advantages comes the highest value of human resources and teamwork. That means Agile transformation provides companies with a transition to another level, and those who have not moved to this level remain outside of competitiveness, if not in the near term, then in a strategic perspective. Agile transformation is comparable to the need to intro-duce new technologies into production processes to replace obsolete ones since, without new technological solutions, the products created do not meet modern quality requirements or do not have a competitive cost price. Thus, Agile transformation ensures the introduction of new technology not only into the production process but into the management system and product creation processes within the framework of the project or project-oriented activities.

55-62

Authors: А. Mukasheva

The purpose of this article is to study one of the methods of social networks analysis – text sentiment analysis. Today, social media has become a big data base that social network analysis is used for various purposes – from setting up targeted advertising for a cosmetics store to preventing riots at the state level. There are various methods for analyzing social networks such as graph method, text sentiment analysis, audio, and video object analysis. Among them, sentiment analysis is widely used for political, social, consumer research, and also for cybersecurity. Since the analysis of the sentiment of the text involves the analysis of the emotional opinions expressed in the text, the first step is to define the term opinion. An opinion can be simple, that is, a positive, negative or neutral emotion towards a particular object or its aspect. Comparison is also an opinion, but devoid of emotional connotation. To work with simple opinions, the first task of text sentiment analysis is to classify the text. There are three levels of classifications: classification at the text level, at the level of a sentence, and at the aspect level of the object. After classifying the text at the desired level, the next task is to extract structured data from unstructured information. The problem can be solved using the five-tuple method. One of the important elements of a tuple is the aspect in which an opinion is usually expressed. Next, aspect-based sentiment analysis is applied, which involves identifying aspects of the desired object and assessing the polarity of mood for each aspect. This task is divided into two sub-tasks such as aspect extraction and aspect classification. Sentiment analysis has limitations such as the definition of sarcasm and difficulty of working with abbreviated words.

77-88

Authors: Zh. Sultanov

In this article, computer vision is considered as modern technology of automatic processing of graphic images, and the relationship between the terms “computer vision” and “machine vision” is investigated. A diagram of a typical computer vision system is given and the possibility of using a system based on an artificial neural network for image analysis is considered. The article analyses the current situation with the use of computer vision systems and the possibility of its application. This article presents face recognition algorithms for existing categories, including: empirical method; feature method – invariant feature; use the template specified by the developer for identification; study the method of detecting the system by external signs. The empirical method of “top-down knowledge-based methods” involves creating an algorithm that implements a set of rules that image segments must satisfy in order to be recognized as faces. Feature-invariant approaches (Feature-invariant approaches) based on bottom-up knowledge constitute the second group of face detection methods. The methods of this group have the ability to recognize faces in different places as an advantage. Use the template set by the developer for identification (template matching method). Templates define specific standard images of face images, for example, describing the attributes of different areas of the face and their possible mutual positions. A method for detecting faces by external signs (a method for performing the training stage of the system by processing test images). The image (or its fragments) is somehow assigned a calculated feature vector, which is used to classify the image into two categories – human face/non-human face.