https://jtst.ibsu.edu.ge/jms/index.php/jtst International Black Sea University en-US Journal of Technical Science and Technologies 2298-0032 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/156 <p>The paper provides an overview of decision support systems to demonstrate their role in supporting decision-making in university administration. Enhancing university performance and modernizing university administration must be top priorities for university leaders and decision-makers since quality is the foundation that supports innovation. Good decision-making is the first step towards efficiency and quality in the academic setting. The article’s main topic is developing IT solutions to support decision-making business processes of student admission. By utilizing models of university business processes and applying business intelligence principles, with the support of multidimensional data analysis, it can be effectively introduced and integrated. The study attempts to provide selection criteria for selecting a development environment for creating a support system focused on university management based on data from the student information system database and the author’s personal experience. The contributions include creating OLAP analytical models and a data warehouse model to support university managerial decisions.</p> <p> </p> <p><strong>References</strong></p> <p>Kovacic, I., Schuetz, C. G., Neumayr, B., &amp; Schrefl, M. (2022). OLAP Patterns: A pattern-based approach to multidimensional data analysis. Data and Knowledge Engineering, 138. https://doi.org/10.1016/j. datak.2021.101948<br />Tripathi, K. P. (2011). Decision Support System Is a Tool for Making Better Decisions in the Organization. Indian Journal of Computer Science and Engineering, 2(1), 112–117. http://www.ijcse.com/docs/ IJCSE11-02-01-054.pdf<br />Bresfelean, V. P., Lacurezeanu, R., Ghisoiu, N., &amp; Sitar-Taut, D. A. (2009). Developing a decision support system for university management. International Journal of Information Technology and Decision Making, 8(4), 611-632</p> <p>Ghisoiu, N., Bresfelean, V. P., Lacurezeanu, R., &amp; Sitar-Taut, D. A. (2009). Developing a decision support system for university management. International Journal of Information Technology and Decision Making, 8(4), 611-632</p> <p>Allen, G., &amp; Parsons, J. (2010). Is query reuse potentially harmful? Anchoring and adjustment in adapting existing database queries. Information Systems Research, 21(1), 56-77</p> Hakan Ergun Copyright (c) 2024 2024-12-18 2024-12-18 8 2 7 16 10.31578/jtst.v8i2.156 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/157 <p>This article presents the development and implementation of a pressure difference measuring system tailored for clean rooms, essential in medical facilities to control airflow and reduce nosocomial infections. The study evaluates various pressure sensors, leading to the selection of intelligent sensors that meet specific performance criteria. Key features and operating principles of the system are outlined, including sensor types, measurement techniques, and system architecture. A detailed comparison of sensors such as BMP180 and BMP280 highlights their suitability based on parameters like resolution, power consumption, and communication interfaces. The system’s structure includes dual pressure sensor units connected to a main control unit, enabling real- time pressure monitoring across different room zones. The sensors, equipped with a microcontroller for initial data processing, relay information to the control unit for further analysis and display. Designed with a calibration feature, the system ensures measurement accuracy by allowing recalibration under identical atmospheric conditions. Operating on low voltage, the system provides safe and reliable performance with applications extending to isolation rooms and environments requiring precise pressure regulation. Integrating the measuring system with ventilation systems significantly mitigates the risk of airborne disease transmission, contributing to the maintenance of sterile environments. The article concludes that this cost-effective, intelligent pressure monitoring solution not only enhances safety in healthcare settings but also supports regulatory compliance and improves overall infection control measures in high-risk areas.</p> <p> </p> <p><strong>References</strong></p> <p>Sammy Al-Benna //Negative pressure rooms and COVID-19. Journal: Journal of Perioperative Practice 2020 p (18–23) ((last<br />transcribed 6.07.2021)</p> <p>Azmaifarashvili, Z, Tomaradze, O. Sensors and intelligent measuring devices. Technical University Publishing House 2017. 498 p.<br />ISBN: 978-9941-20-754-9</p> <p>A. Frangishvili, T. Dzagania, Z. Azmai- farashvili, E. Butskhrikidze, M. Meskhia. differential pressure monitoring system medical for institutions . automated control systems . works # 1(33), Vol. 1.1 2022, pp<br />. 5-12</p> <p>Menabde, T., Otkhozoria, N., &amp; Otkhozoria,<br />V. (2024). Use of the theory of measurement uncertainty in procedures for data processing and results obtained by checking-calibration gas flow meters. International Science Journal of Engineering &amp; Agriculture, 3(2), 40–46. htt ps: // doi. org/ 10.46299/ j. isjea.20240302.03</p> <p>Chkheidze, I., Otkhozoria, N., &amp; Narchemashvili, M. (2021). Evaluation of Measurement Quality uding the Monte- Carlo Method. Universum, 65-70. doi: DOI: 10.32743/UniTech.2021.84.3-4.65-70</p> <p>Otkhozoria, N., Otkhozoria, V., Narche- mashvili, M. (2021) Fractality Of Measurements Of Quantities And Real Processes. International Trends In Science And Technology, Engineering Sciences June 2021 Doi: https://doi.org/10.31435/ rsglobal_conf/30062021/7620</p> <p> </p> <p> </p> Zaal Azmaiparashvili Elguja Butskhrikidze Nona Otkhozoria Marina Meskhia Copyright (c) 2024 2024-12-18 2024-12-18 8 2 17 22 10.31578/jtst.v8i2.157 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/158 <p>Speech recognition applications can increase the efficiency of simple tasks and greatly expand the range of tasks that call center operators perform. To increase the productivity of the public security management center, a software module was created which analyzes incoming calls, converts them into text form and places this text in the database. Operators are given the opportunity to read the call text after the end of the call to better understand its content and to improve the quality of the corresponding response. The article discusses the module, which was created to convert the incoming calls into text form. The service is a .NET Core Web API application created using the C# programming language. It uses a NuGet package to communicate with Google’s Speech service. The components of the system are described, the JSON format of the message is given, the structure of the database is discussed, the corresponding software codes of the module are given. The article also shows the customer irritating factors, analyzes the situations that create a series of calls, presents the results of customer surveys, which relate to the needs of customers in terms of waiting time. The paper discusses the mechanism for prioritizing calls, the algorithm for its implementation, as well as ways to manage non-targeted calls. The article shows the pros and cons of a system for queuing priorities.</p> <p> </p> <p><strong>References</strong></p> <p>Darchiashvili N., Kobiashvili A. (2020). Call Center Data Analysis and Monitoring Electronic System. Georgian Technical University, Proceedings, Automated Systems, N1(30), Tbilisi, pp. 54-61<br />Kobiashvili A., Darchiashvili N., Gegechkori M. (2019). Text Recognition Technology from the Conversation. Archil Eliashvili Institute of Control Systems of the Georgian Technical University, Proceedings, #23,Tbilisi, pp.171-177<br />ht t ps : / / www. ex plaint hat s t uf f . c om / voicerecognition.html,was checked on 7.08.2024<br />https://cloud.google.com/speech-to-text, was checked on 01.10.2024<br />https://summatti.com/top-5-use-cases- for-speech-analytics/ was checked on 01.10.2024<br />https://www.datapine.com/blog/call- center-metrics-and-kpis/, was checked on 17.08.2024<br />https://www.openaccessbpo.com/blog/6-forms-of-data-analytics-in-the-call-center/, was checked on 17.08.2024<br />https://ww w.tenfo ld.com/customer- ex perienc e/ managing- call- queues - customer-service-call-centers, was checked on 17.08.2024<br />https://docs.microsoft.com/en-us/dotnet/ core/whats-new/dotnet-core-3-0, was chec- ked on 10.07.2024</p> Ana Kobiashvili Nodar Darchiashvili Tea Todua Copyright (c) 2024 2024-12-18 2024-12-18 8 2 23 30 10.31578/jtst.v8i2.158 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/159 <p>Data lakes, which collect and store huge amounts of structured and unstructured data, are currently one of the most important technological tools. Their structure differs from traditional databases, as they are more flexible and allow organizations to store diverse data in a single repository for further processing and analysis. Their use is advisable in many fields, ranging from business and science to public administration. However, the rapid development of data lakes presents new challenges. The paper presents the key characteristics of data lakes and data warehouses, along with a comparative analysis. It discusses the opportunities for using data lakes, which are related to the diversity of the data stored within them. The main stages of data mining from lakes are presented. The strengths of using data lakes are also described. The paper places great emphasis on analyzing the risks associated with data lakes and proposes ways to mitigate them and the future prospects of data lakes are presented. The paper places significant emphasis on analyzing the risks associated with data lakes and proposes ways to mitigate them. Future perspectives for data lakes are also presented. Working with data lakes is a complex but important process. With the right approach and consideration of the challenges outlined in this paper, organizations will be able to maximize the potential of data lakes and gain competitive advantages.</p> <p> </p> <p><strong>References</strong></p> <p>Badri Meparishvili, Guram Tsertsvadze, Gulnara Janelidze Big Data Analitics, Tbilisi, GTU 2020, ISBN 244pp<br />Philip Russom, Data Lakes, © 2017 by TDWI, a division of 1105 Media, Inc., 40pp<br />Ben Sharma, Architecting Data Lakes, USA 2018, 50pp<br />Rihan Hai Christoph Quix Matthias Jark, Data lake concept and systems:17jun, 2021<br />Tomcy John, Pankaj Misra, Data Lake for Enterprises: Lambda Architecture for building enterprise data systems, 31 may, 2017<br />Corinna Giebler, Christoph Gröger, Eva Hoos, Rebecca Eichler, Holger Schwarz, Bernhard Mitschang, The Data Lake Architecture Framework: A Foundation for Building a Comprehensive Data Lake Architecture, Conference Paper· March 2021 DOI: 10.18420/btw2021-19z</p> Gulnara Janelidze Ia Aptsiauri Lela Tsitashvili Copyright (c) 2024 2024-12-18 2024-12-18 8 2 31 35 10.31578/jtst.v8i2.159 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/160 <p>This paper describes the development of a service for tracking the university schedule, implemented using a microservice architecture with a chat-bot as the client interface. During the development process, the needs of students and professors, as well as publicly available university data, were considered, allowing for the selection of an optimal platform and the creation of an interactive chat-bot interface. All stages of the project’s development are listed, starting from defining the main service objectives and ending with configuration and testing. Information is also provided about the chosen technologies and tools used during the development process, such as the Python and Java programming languages, the BeautifulSoup and Requests libraries, the Cloud Native and Domain-Driven Design architectural approaches, as well as containerization with Docker and orchestration with Kubernetes. The service is designed to help students stay informed about upcoming classes and events by providing information in an accessible and intuitive format. It serves as a convenient addition to traditional methods of information delivery.</p> <p> </p> <p><strong>References</strong></p> <p>N. Beraia, L. Tokadze, I. Aptsiauri, M. Shiukashvili. Prospects and problems of using artificial intelligence in higher education. 10th International new york conference on evolving trends in interdisciplinary research &amp; practices. Manhattan, New York City, IKSAD Publications - 2024©, Issued: 24.06.2024, Proceeding book, 308-313 pp.<br />ISBN: 978-625-367-739-8</p> <p>Beraia N., Shekunov A., Timofeev P., Tokadze L. On the issue of effective use of information technologies in the adaptation of foreign students and the optimization of educational processes. Scientific discussion (Praha, Czech Republic), Vol 1, No 82, (2023), р. 32-36. ISSN 3041-4245; DOI:10.5281/zenodo.10117504</p> <p>TIOBE Index for October 2024 (accessed 21.10.2024). https://www.tiobe.com/tiobe- index/</p> <p>Market Share of Github (accessed 21.10.2024). https://6sense.com/tech/ source-code-management/github-market- share#</p> Nino Beraia Oleg Smoliakov Andrei Bykov Copyright (c) 2024 2024-12-18 2024-12-18 8 2 36 45 10.31578/jtst.v8i2.160 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/161 <p>The Internet of Things (IoT) has the potential to modernize healthcare through remote patient monitoring, disease management, and improved health outcomes. IoT devices, such as wearables and sensors, can collect and transmit data in real time, enabling healthcare providers to make informed and timely decisions. This paper examines the current state of the Internet of Things in healthcare, the opportunities presented by the Internet of Things, and the challenges faced during implementation. This paper first describes the benefits of IoT in healthcare, including improving patient outcomes, reducing healthcare costs, and increasing access to care. We look at a variety of IoT devices used in healthcare, including wearables, smart implants, and medical sensors. This paper also outlines the challenges facing the use of IoT in healthcare, including data security, privacy issues, and regulatory issues. The paper concludes with a discussion of the future of IoT in healthcare and its potential impact on the healthcare industry.</p> <p> </p> <p><strong>References</strong></p> <p>Journal of emerjing technologies and innovative research (JETIR) (ISSN-2349- 5162).An international Scholarly Open Access, Peer-reviewed Refereed Journal<br />(2022). www.jetir.org</p> <p>International Journal of Applied Engineering Research. ISSN 0973-4562. Volume 13,Number 15 (2018) pp. 11984-11989. http:// www.ripublication.com<br />International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056. Volume: 09 Issue: 12<br />| Dec 2022 www.irjet.net</p> Artem Sologhashvili Copyright (c) 2024 2024-12-18 2024-12-18 8 2 46 57 10.31578/jtst.v8i2.161 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/162 <p>The development of modern web technologies is associated with unprecedented changes in the digital world around us. This fact determines the existence of diverse software support and technologies for creating websites. The web technology ecosystem is developing fast, driven by emerging technologies, changing user expectations, and the ongoing digital transformation across industries. This article explores the most significant trends reshaped web development in the latest period of time, highlighting the technological innovations that are redefining how developers create, deploy, and optimize digital experiences. From advances in frontend frameworks to the integration of artificial intelligence, serverless architectures, and enhanced performance optimization techniques, the current web technology landscape represents a dynamic intersection of innovation, efficiency, and user-centric design. The paper also discusses the latest trends in web technologies in relation to the Georgian labor market.</p> <p> </p> <p>References</p> <p>Tabatadze B., &amp; Asanidze G. (2023, 7 31). Synthesis of Contemporary Approaches Used In the Development of the Client-Side in Technological Projects. 8(15), 49-53. doi:https://doi.org/10.35945/gb.2023.15.004<br />K. Schwab. The Fourth Industrial Revolution. (2016). World Economic Forum;</p> <p>K. Schwab. The Fourth Industrial Revolution. <br />(2016). World Economic Forum;<br />https://w3techs.com/, was checked on <br />12.10.2024<br />https://statista.com, was checked on <br />12.10.2024<br />https://gita.gov.ge, was checked on <br />12.10.2024<br />https://pwc.com/ge, was checked on <br />12.10.2024<br />https://techcrunch.com, was checked on <br />12.10.2024</p> Teimuraz Sturua Tea Todua Ana Kobiashvili Copyright (c) 2024 2024-12-18 2024-12-18 8 2 58 67 10.31578/jtst.v8i2.162 https://jtst.ibsu.edu.ge/jms/index.php/jtst/article/view/163 <p>Open Journal Systems (OJS) is a widely used open-source platform for managing and publishing academic journals (Ndungu, 2020), (Hunter, 2010), (Tabatadze B. , 2024). Its modular design and open framework provide opportunities for integrating artificial intelligence (AI) technologies, which hold transformative potential for academic publishing. AI can enhance OJS by automating editorial workflows, improving user experience, and fostering global accessibility. Key areas for AI integration include automated peer review, where natural language processing (NLP) can identify relevant reviewers, detect plagiarism, and ensure structural compliance. AI-powered recommendation systems can personalize content delivery, offering tailored article suggestions based on user preferences and behaviors. Additionally, linguistic diversity can be bolstered through real-time translation tools and speech-to-text features, facilitating broader engagement with a global audience. AI also enables automation in editorial processes such as grammar checks, citation management, and abstract generation. Advanced analytics powered by AI can provide actionable insights into readership trends, engagement metrics, and impact factor predictions, supporting strategic decision-making for journal administrators. Despite its potential, AI integration poses challenges, including technical expertise requirements, financial constraints, and ethical concerns such as data privacy and bias. Overcoming these barriers will require careful planning and community collaboration. The future of AI in OJS looks promising, with opportunities to incorporate blockchain, adaptive learning systems, and AI-driven collaboration tools. By addressing these challenges, OJS can lead the evolution of open-access publishing, enhancing efficiency, accessibility, and inclusivity in scholarly communication.</p> <p> </p> <p><strong>References</strong></p> <p>Hunter, B. (2010). Moving Open Access to Open Source: Transitioning an Open-Ac- cess Journal into the Open Journal Systems Journal Management System. Technical Services Quarterly, 31-40. doi:10.1080/073 17131.2010.500972</p> <p>Ke Zhang, A. B. (2021). AI technologies for education: Recent research &amp; future direc- tions. 2. doi:https://doi.org/10.1016/j.ca- eai.2021.100025</p> <p>Ndungu M. W. (2020). Publishing with Open Journal Systems (OJS): A Librarian’s Per- spective. Serials Review, 21-25. doi:10.108 0/00987913.2020.1732717</p> <p>Official Documentation of OJS(Open Jour- nal System). (2023). Retrieved from https:// docs.pkp.sfu.ca/#appojs3</p> <p>R, S., Vijayan, V., &amp; A.J, F. (2019). De-<br />sign and Implementation of Open Jour- nal System (OJS) for Rajagiri. Library Philosophy and Practice (e-journal), 10. Retrieved from https://d1wqtxts1xzle7. cloudfront.net/61040777/Published_ file_- Library_Philosophy_and_Prac- tice20191027-13938-1gp0tnc-libre.pd- f?1572245003=&amp;response-content-disposi- tion=inline%3B+filename%3DDesign_and_ Implementation_of_Open_Journa.pdf&amp;Ex- pires=1703500665&amp;Si</p> <p>Tabatadze B. (2024, 4 30). Technological Aspects of Open Journal Systems (OJS). 8, 23-29. doi:https://doi.org/10.31578/jtst. v8i1.151<br />Tabatadze B., &amp; Asanidze G. (2023, 7 31). Synthesis of Contemporary Approaches Used In the Development of the Client-Side in Technological Projects. 8(15), 49-53. doi:https://doi.org/10.35945/gb.2023.15.004</p> <p>Zhvania T., Kiknadze M., Todua T., Kap- anadze D. (2024). Application of Neural Net- works for Analysis of Sustainable Regional Development. The 4nd International Confer- ence “Problems of Engineering Sciences”. Batumi</p> <p>Zhvania T., Todua T., Kiknadze M., Kap- anadze, D. (2023). Formation of Similarity Measures for Pattern Recognition Prob- lems. Collective monograph. Contemporary Business Challenges in a Globalized World: Research, Study, Examination (Volume 4). Lambert Academic Publication</p> Besiki Tabatadze Copyright (c) 2024 2024-12-18 2024-12-18 8 2 68 74 10.31578/jtst.v8i2.163