Scientific Journal of Astana IT University

DETERMINATION OF THE OPTIMAL CONTROLLABLE KEY INDICATOR OF CALL CENTER IN ORDER TO INCREASE EFFICIENCY FOR GENERATING INCOME

2023-06-01T16:04:26+06:00

This paper focuses on call centers, which have become a common means of communication with potential customers in various companies. Specifically, this paper analyzes call center data and the importance of assessing key indicators for evaluating call center performance. The questions this paper addresses are the criteria for evaluating call center quality and the methods for analyzing call center data. Previous research has shown the significance of call centers as the "face of the company," with the quality of their work reflecting how efficiently a company will serve its customers ' requests in the future. The main goal of this paper is to fill a gap in previous research by identifying the main controlled key indicator for call center quality and to suggest ways to improve efficiency. By using analytical methods to examine call center data, this paper identifies the most important criteria for call center quality and provides recommendations for enhancing service quality.

The main findings of this paper show the importance of call center operator performance in determining call center performance which affects company revenue. By evaluating key indicators such as the number of operators, this paper demonstrates how call centers can reduce service costs and improve efficiency. During the analysis using call center data for two years, it turned out that the company had expenses 1/3 of the total amount of maintenance compared to the previous year, which is not effective in terms of economy. Operational planning has a direct impact on operators’ costs and the main cost component is the hourly cost of operators. If optimal planning turns out to be at least 10% better than the arrangement set in the call center, company will save a good amount. The significance of this paper lies in its potential to improve the quality of service in call centers and its contribution to the field of customer service management. By providing insight into the importance of call center efficiency, this research offers recommendations for predicting the optimal number of operators to improve the customer experience with reducing service costs.

SIMPLIFIED ADAPTIVE TRIANGULATION OF THE CONTACT BOUNDARIES OF THE DAM MODEL

2023-09-04T16:56:59+06:00

To numerically solve the system of integral equations, it is customary to establish a discrete grid within each integration area. In the context of 3D modeling, these areas correspond to surfaces situated in space. The standard discretization technique employed for the computational domain is triangulation. This study addresses the integral equation system pertinent to the electrical tomography of dams. The structural model encompasses an embankment dam, the upstream and downstream water bodies, the dam's base, and a potential leakage region on the upstream side. An alternative configuration may be encountered in specific scenarios with no water downstream. Consequently, the model may incorporate up to nine distinct contact boundaries. Accordingly, the system of integral equations comprises an equivalent number of equations. Effectively resolving this system through numerical methods necessitates applying triangulation techniques to these diverse surfaces. While mathematical packages like Matlab offer triangulation functions, they may not fully address the specific demands of the problem. Additionally, the grid resolution should be heightened in proximity to key elements such as the sounding line, the supply electrode, and the various contact lines within the medium. These considerations transform the triangulation task into a distinct subtask within the numerical simulation of the resistivity tomography problem. In this paper, we provide our specific approach to this problem. The simplification of the triangulation algorithm is rooted in the predominant utilization of the two-dimensional geometric properties inherent to the object under study. For most contact boundaries, the triangulation is constructed layer by layer with a gradual modulation in triangle dimensions as one progresses from one layer to the next, orthogonal to the axis of the dam. Concerning the surface corresponding to the leakage area, cylindrical coordinates are used for surface parameterization. This approach enables partitioning the surface into discrete strata, facilitating a systematic, layer-by-layer grid construction. Additionally, points at the intersections of contact boundaries are integrated into the pre-existing triangulation by applying a standard function within the Matlab package. In the future, the mathematical modeling based on the Integral Equation Method with adaptive discretization will help incorporate real-time computations into information systems related to monitoring hydraulic structures.

DEVELOPMENT OF A QUESTION ANSWERING CHATBOT FOR BLOCKCHAIN DOMAIN

2023-09-21T11:33:07+06:00

Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models.

A BIBLIOMETRIC STUDY ON BLOCKCHAIN CONCEPT: A THEME ANALYSIS AND FUTURE DIRECTIONS FOR COMPUTER SCIENCE TRAINING

2023-09-19T17:59:47+06:00

This paper aims to study the blockchain concept domain in the computer science field due to bibliometric study. Authors employed bibliometric and network analysis techniques to analyze existing literature. In total, 719 articles in the period of 2019 to August 2023 from the Web of Science (WOS) database were analyzed after applying search string, and criteria for inclusion and exclusion. Initial data screening involved the extraction of fundamental information, followed by data analysis based on co-occurrence, bibliographic coupling, and citation using special program software VOSviewer and R program. research areas "compute science" and "engineering". In addition to that, VOSviewer and R-based tools illustrate the application of text mining involves utilizing computational techniques to extract, analyze, and represent the key concepts and relationships within the field of blockchain technology. Data analysis primarily involved co-occurrence analysis, bibliographic coupling, co-authorship examination, citation analysis, and co-citation analysis. In the context of a blockchain concept thematic analysis, was applied clustering by coupling. Furthermore, it was conducted the thematic analysis to scrutinize the content of prior studies in the computer science field using clustering by coupling. Ranking of the authors, organizations, and countries was applied according to total link strength metric which was used to quantify the overall strength of connections between nodes within a network. Besides, citation analysis has also been conducted to assess the articles' ranking, considering both worldwide and localized citations. Bibliometric results indicate blockchain concepts within such thematic frameworks as access control scheme, identity management system, supply chain management, artificial intelligence integration, blockchain technology applications, and blockchain smart contract.

MATHEMATICAL, SOFTWARE AND HARDWARE SUPPORT OF THE CONCEPTUAL MODEL OF THE INFORMATION SYSTEM OF PRECISION AGRICULTURE

2023-10-14T14:03:27+06:00

This study analyzes the current situation of application of precision farming technologies and solutions by agricultural enterprises of the Republic of Kazakhstan. The main players and used solutions have been identified. The statistics of application, as well as the potential of use is examined. Within the framework of the analysis of the applied solutions the advantages and disadvantages of competitors in the market were determined. It was defined that the applied systems provide the possibility of remote management, but EGISTIC is more focused on the management of all processes of the farm, including the warehouse, while John Deere is focused on the management and analytics of agricultural machinery. EGISTIC offers features for warehousing and inventory planning, something not found in the base version of John Deere Operations Center. John Deere focuses on data sharing which can be important for large farms or groups of farmers. EGISTIC makes extensive use of satellite imagery to analyze field conditions which can be a great asset for identifying problem areas and planning interventions. Depending on the specific needs and priorities of an agribusiness, one system may be preferable to another. If machinery management is the main focus, John Deere might be the best choice. If in-depth analysis of field conditions and inventory control is important, EGISTIC may be more appropriate. By analyzing, the directions for research are highlighted. A conceptual model of information system for precision farming is developed. Hardware for realization of the conceptual model is possible on the basis of universal programmable logic controller of modular architecture being developed. Within the limits of the given research the conceptual model of the universal programmable logic controller of modular architecture and the structural model of the software of the universal programmable logic controller of modular architecture have been developed. The interaction with the conceptual adaptive model of information and communication system is also considered. This paper analyzes the key principles and functions of both the universal programmable logic controller and the information and communication system, as well as their possible integration within a single concept.

A MATHEMATICAL MODEL FOR AN AUTOMATED SYSTEM OF MEDICAL DIAGNOSTICS

2023-09-22T16:00:42+06:00

One of the primary focuses of the Republic of Kazakhstan concerning sustainable and stable improvements in the well-being of its population is the advancement of the healthcare sector. A mathematical model for an automated medical diagnostics system integrates machine learning algorithms, statistical models, and decision trees to analyze patient data and facilitate accurate diagnoses. This model enables healthcare professionals to enhance the efficiency and reliability of medical diagnostics by leveraging advanced computational techniques. These distinguishing features can be incorporated by developing a mathematical model for diagnosing diseases, enabling precise identification, and guiding appropriate treatment strategies.

Machine learning algorithms play a crucial role in automated systems for medical diagnostics. An ensemble of multiple algorithms, such as combining decision trees with gradient boosting or using a combination of neural networks and traditional machine learning, can yield improved diagnostic accuracy and robustness. Predicting the progression of diseases is a crucial aspect of healthcare, enabling personalized interventions and improved patient outcomes. A mathematical approach can facilitate this prediction by monitoring changes in diagnostic results aligned with the severity of symptoms, which inherently vary over the observation period. By employing mathematical modeling techniques, healthcare professionals gain valuable insights into disease progression, supporting informed decision-making and tailored treatments.

In conclusion, developing a mathematical model for an automated medical diagnostics system, incorporating machine learning algorithms, statistical models, and decision trees, significantly contributes to healthcare. These models enhance the accuracy, efficiency, and personalization of medical diagnoses. Additionally, mathematical models aid in the differential diagnosis of challenging conditions and provide predictions regarding disease progression, ultimately benefiting patient care and treatment outcomes.

GESTURE RECOGNITION OF MACHINE LEARNING AND CONVOLUTIONAL NEURAL NETWORK METHODS FOR KAZAKH SIGN LANGUAGE

2023-09-07T14:03:17+06:00

Recently, there has been a growing interest in machine learning and neural networks among the public, largely due to advancements in technology which have led to improved methods of computer recognition of objects, sounds, texts, and other data types. As a result, human-computer interactions are becoming more natural and comprehensible to the average person. The progress in computer vision has enabled the use of increasingly sophisticated models for object recognition in images and videos, which can also be applied to recognize hand gestures. In this research, popular hand gesture recognition models, such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were examined. These models vary in their approaches, processing time, and training data size. The important feature of this research work is the use of various machine learning algorithms and methods such as CNN, LSTM, and SVM. Experiments showed different results when training neural networks for sign language recognition in the Kazakh sign language based on the dactyl alphabet. This article provides a detailed description of each method, their respective purposes, and effectiveness in terms of performance and training. Numerous experimental results were recorded in a table, demonstrating the accuracy of recognizing each gesture. Additionally, specific hand gestures were isolated for testing in front of the camera to recognize the gesture and display the result on the screen. An important feature was the use of mathematical formulas and functions to explain the working principle of the machine learning algorithm, as well as the logical scheme and structure of the LSTM algorithm.

DEVELOPMENT OF A LINEAR REGRESSION MODEL BASED ON VEGETATION INDICES OF AGRICULTURAL CROPS

2023-10-09T09:24:56+06:00

The article is devoted to the study of vegetation indices for assessing the productivity of agricultural crops of the North Kazakhstan Agricultural Experimental Station (NKAES) LLP. The research was carried out using a modern software package for processing satellite images, EOS Land Viewer. The work used images from the Landsat 8 (USA) and Sentitel 2 (European Space Agency) spacecraft. Digitized Earth remote sensing data for the last 3 years are presented, showing changes in the amount of moisture reserves on the territory of NKAES LLP. Time series of distribution of the studied coefficients were constructed according to different phases of active vegetation biomass in the study area. The resulting time series made it possible to identify annually repeating patterns, a linear trend of increasing and decreasing NDWI and NDVI on the territory of the NKAES LLP.

Review of studies over the past 5 years, published in highly rated foreign journals, on various vegetation indices, including indices designed to assess moisture content in vegetation and soil. It is noted that the first normalized water index, NDWI, using the SWIR infrared channel, unlike the widely used NDVI vegetation index, actually penetrates 80% of the atmosphere.

Analysis of the obtained NDWI allowed us to identify dry, moderately dry and fairly humid periods on the territory of the NKAES LPP from 2020 to 2023. Based on the research carried out, the feasibility of using normalized difference water indicators and normalized vegetation indices for further use in forecasting yields in the conditions of the North Kazakhstan region is substantiated. Next, using vegetation indices and additional agrometeorological factors, a linear model for predicting crop yields was developed. The coefficient of determination of the resulting model is 0.90 which indicates that the selected trend line reliably approximates the process under study.

MATHEMATICAL FRAMEWORK FORMULATION AND IMPLEMENTATION FOR HYPERSPECTRAL AEROSPACE IMAGES PROCESSING

2023-10-12T12:16:15+06:00

This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth's surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate.

SYSTEMATIC DATA PROCUREMENT IN AN OWL-EMBEDDED INFORMATION AND ANALYTICAL FRAMEWORK FOR THE MONITORING OF WATER RESOURCES IN THE ILE-BALKHASH BASIN

2023-10-06T13:38:39+06:00

The world is facing an escalating water shortage crisis, with dire consequences for ecosystems, human health, and socio-economic development. This article explores the multifaceted nature of the water shortage problem of Ile-Balkhash basin that falls under the jurisdiction of the Balkhash-Alakol Republic of Kazakhstan, its underlying causes, and the complex web of challenges it presents. The predicament in the Ile-Balkhash basin is a complex interplay of various factors. Climate change has led to erratic precipitation patterns, exacerbating the problem. The simultaneous rise in population places additional stress on the already limited water resources. Moreover, inefficient water management practices have perpetuated the issue, hindering the equitable distribution of water. The challenge of conducting a comprehensive basin analysis is a formidable task due to the numerous variables and indicators involved. It demands an enormous amount of time and effort. To address this issue, a web application framework integrated with the Web Ontology Language database, allowing for the execution of advanced queries to extract valuable insights from various objects and indicators, has been developed. The database underpinning this system is meticulously compiled, drawing upon data from the Hydrological monitoring of water bodies and the National Hydrometeorological Service of the Republic of Kazakhstan. These sources provide critical data that forms the bedrock of the analysis. Recognizing the importance of data storage and management in this endeavor, integrated components have been established. These components play a pivotal role in structuring the diverse data sources and maintaining their currency. The water shortage issue in the Ile-Balkhash basin serves as a stark reminder of the urgency with which such crises must be addressed. The tools and methods offer hope and underscore global water management sustainability.

THE TASK OF CHOOSING PARTNERS FOR THE ORGANIZATION OF COOPERATION IN THE FRAMEWORK OF SCIENTIFIC AND EDUCATIONAL PROJECTS

2023-10-18T10:00:19+06:00

The primary objective of this article is to establish a set of fundamental criteria for the selection of scientific partners for collaborative research efforts. Achieving this objective entails addressing the challenge of identifying criteria that are both objective and universally applicable, capable of encompassing various fields of scientific research, such as natural sciences, technical sciences, and economic sciences, among others.

One such criterion, applicable to scientists, may involve assessing their publication activity within specific research areas that align with the objectives of the relevant scientific research or international projects. In the contemporary landscape of scientific research, there is a growing urgency to enhance the effectiveness of research endeavors and to foster efficient collaboration within scientific communities. This is particularly vital for organizations oriented towards project-based research.

In the formation of research project teams, a conventional approach is to select partners from the pool of scientists possessing the requisite qualifications and experience in the execution of such projects. A widely accepted yardstick for evaluating the outcomes of scientists' research endeavors is the citation metrics associated with their publications. Typically, these metrics take the form of scalar values. While this approach offers several advantages, it is not without its limitations. One notable drawback is the potential loss of information when converting raw data into scalar metrics, and the existence of certain edge cases where the parameter remains unchanged despite variations in the number of citations and publications.

Hence, it is pertinent to explore the development of new methodologies or modifications to existing ones that can effectively evaluate the results of scientists' research activities while mitigating these limitations.Начало формы The article describes the criteria for the search and selection of partners for joint scientific research. This will make it possible to effectively form teams for narrowly focused scientific research or international collaboration projects in interdisciplinary scientific projects such as the European Horizon Program or educational projects such as the Erasmus plus program. Also, the proposed solution will allow the formation of small teams for joint scientific publications. It is imperative to acknowledge that the process of partner selection is predominantly driven by a consideration of the knowledge, whether it be novel or foundational, possessed by prospective partners who are entrusted with the execution of a project. It becomes crucial to delineate the specific criteria governing partner selection which can vary contingent upon factors such as the typology of partners, the nature of project tasks, the depth of knowledge possessed, and related contextual variables. A vital underpinning for the formation of project consortia is the mathematical conundrum of choice which furnishes a formal rationale for the judicious selection of a particular partner.

NEURAL NETWORK MODEL OF SOIL MOISTURE FORECAST NORTH KAZAKHSTAN REGION

2023-09-21T16:24:37+06:00

Dealing with agriculture, it is valuable to know an amount of moisture in a soil and to know how to forecast the stored soil moisture within particular period. Forecasting the stored soil moisture works for planning an extent and structure of crop production areas and adjustment of plant-growing programs. Having known about an amount of moisture in one-meter soil and the depth of precipitation in a vegetation season shall help farmers to determine a seeding time, type of fertilizers depending on soil quality and to work out an irrigation schedule as well. In this regard, over the last few years some vigorous activities applied to machine training methods of the weather forecast have been launched in the world. The goal of present research is to develop an artificial neuron network which shall afford an opportunity to figure out a stored soil moisture prior to outgoing to winter in a short-term. North Kazakhstan Region agrometeorological measuring stations for the period from 2012 to 2022 were used in the course of the neuron network training. The Levenberg-Marquardt algorithm aimed at non-linear regression models optimization was chosen for network training. The algorithm includes sequential approximation of initial parameter values to a local optimum. The mean squared error (MSE) function and the correlation coefficient ensure accuracy and precision of forecasts. As a result, 7 neural networks under MATLAB environment using the Levenberg-Marquardt algorithm, with different input and output data, and with different number of learning iterations came to realization. Following analysis of the results, the choice was fallen on the ANN9 best network offering minimum error function and actual data maximum correlation. The neural network obtained fits for use to make efficient decisions in the North Kazakhstan region agricultural sector in the short term.

METHODS OF NAVIGATING ALGORITHMIC COMPLEXITY: BIG-OH AND SMALL-OH NOTATIONS

2023-10-12T09:24:14+06:00

This article provides an in-depth exploration of Big-Oh and small-oh notations, shedding light on their practical implications in the analysis of algorithm complexity. Big-Oh notation offers a valuable tool for estimating an upper bound on the growth rate of an algorithm's running time, whereas small-oh notation delineates a lower limit on this growth rate. The piece delves into a comprehensive examination of various complexity classes that emerge through the application of Big-Oh notation, underscoring the significance of small-oh notation as it complements and enriches complexity analysis.

In the realm of programming and computer science, the employment of these notations holds paramount importance. They empower developers and researchers to make informed decisions regarding algorithm selection and optimization. It is crucial to recognize that while complexity analysis is a vital facet of effective programming, ongoing research endeavors may yield more refined methodologies and approaches within this domain.

By understanding and harnessing the power of Big-Oh and small-oh notations, professionals can effectively evaluate algorithm efficiency and scalability. This knowledge equips them with the ability to design and implement algorithms that meet specific performance criteria, which is pivotal in the ever-evolving landscape of technology and computation. As pushing the boundaries of what is possible in the field of algorithm design is being continued, these notations remain invaluable tools for navigating the complex terrain of algorithmic analysis and optimization.

By embracing Big-Oh and small-oh notations, professionals can finely assess algorithmic efficiency, ensuring they meet performance criteria in the evolving technological landscape. These notations remain indispensable for algorithmic analysis.