•   10:00 - 10:10: Welcome Speech and Participants Congratulation

•   10:10 - 10:25: Local Image Contrast Enhancement Based on Power Model of Visual Perception by Roman Vorobel, Olena Berehulyak, Iryna Ivasenko and Teodor Mandziy. This study delves into enhancing local image contrast through the utilization of a power model inspired by visual perception. By analyzing different forms of local contrast and proposing a novel method based on human visual system response, the research aims to improve image quality enhancement algorithms. The experimental validation of the proposed approach demonstrates its effectiveness in enhancing image quality, thus offering promising prospects for applications in computer vision and image processing.
•   10:25 - 10:40: Iron Coagulation Optimization During Water Treatment Using Artificial Intelligence Tools by Andriy Safonyk and Vladyslav Danchenkov. In this research, artificial intelligence (AI) tools are applied to optimize iron coagulation during water treatment processes. Through thorough data analysis and the implementation of AI algorithms, the study seeks to refine iron detection methods, ultimately enhancing the efficiency of water treatment systems. The findings contribute to advancements in water treatment technology, offering sustainable solutions for improving water quality and resource management.
•   10:40 - 10:55: Simulation of Multiport Hetero-Associative Memory (MPHAM) Based on Equivalent Models Implemented on Matrix-Matrix Multipliers by Vladimir Krasilenko, Illya Chikov, Diana Nikitovich and Serhii Shatnyi. This paper explores the simulation of Multiport Hetero-Associative Memory (MPHAM) using equivalent models implemented on matrix-matrix multipliers. By leveraging simulation techniques and mathematical modeling, the research aims to advance associative information processing systems. The study's results provide insights into hardware support for memory systems, with potential applications in pattern recognition and artificial intelligence.
•   10:55 - 11:10: Usage of Information Dashboards in Smart Cities by Oleh Palka and Lesia Dmytrotsa. Focusing on smart city initiatives, this research investigates the utilization of information dashboards to monitor key performance indicators (KPIs). By analyzing the role of information dashboards in urban governance, the study offers insights into effective decision-making processes. The findings contribute to the development of smart city architectures, fostering sustainable and efficient urban environments.
•   11:10-11:25: Intelligent Carbon Dioxide Monitoring in Smart Home System with Nondispersive Infrared Sensors by Arsenii Zyma, Oleh Berest and Viacheslav Moskalenko. Within the realm of smart home systems, this study examines intelligent carbon dioxide monitoring using nondispersive infrared (NDIR) sensors. By evaluating various monitoring methods, the research addresses the critical need for indoor air quality management. The findings provide valuable insights for optimizing smart home environments, enhancing energy efficiency, and ensuring occupant comfort.
•   11:25-11:40: Determining Job Ads Category Using AWS Machine Learning Cloud Services by Vitalii Pavliuk, Volodymyr Drevetskyi and Yuriy Turbal. This research explores the automatic categorization of job advertisements using Amazon Web Services (AWS) machine learning cloud services. By harnessing cloud technologies, the study aims to streamline job ad management processes, offering practical solutions for talent acquisition and recruitment. The findings contribute to advancements in human resource management practices and automation.
•   11:10-11:25: Cross-Platform Application for NFT Tokens by Olena Hladka, Ivan Karpovich, Mykhailo Opanasiuk and Viktor Zhukovskyy. In this study, a cross-platform application for managing Non-Fungible Tokens (NFTs) is developed, facilitating token creation, storage, and trading. By providing a seamless NFT marketplace experience, the application contributes to the ongoing development of decentralized digital asset ecosystems. The research findings offer insights into secure and efficient NFT token management, fostering innovation in blockchain technology applications. 
•   11:25-11:40: Approach to Predicting Spread of COVID-19 in Ternopil Region by Vasyl Martsenyuk, Mykhaylo Andreychyn, Andriy Sverstiuk, Oksana Bahrii-Zaiats, Volodumur Panychev and Svitlana Nykytyuk. This study presents an innovative approach to predicting the spread of COVID-19 in the Ternopil region, employing both linear and nonlinear differential equation models. By integrating trend lines and mathematical models, the research offers insights into forecasting infectious disease dynamics and recovery rates. The study's outcomes provide valuable tools for public health preparedness and response strategies, aiding in epidemic control and management in the region.
•   11:40-11:55:  Requirements Engineering Issues and Challenges in Ukrainian Agile-projects by Roman Kruk and Nataliia Zhukovska. This research delves into the complexities and challenges of requirements engineering in Ukrainian Agile projects. Through a comprehensive review of scholarly studies and survey data from the IT community, the study identifies current issues impacting project outcomes. The findings shed light on the unique challenges faced in Agile environments, paving the way for future research and enhancing understanding of requirements engineering practices in Ukrainian IT projects.

•   11:55-12:25: Coffee break

•   12:25-12:40: About Approach to Solution of Traveling Salesman Problem Based on Annealing Method With the Fuzziness of Time Perception by Eugene Ivohin, Larisa Adzhubey, Vadym Rets and Oleksiy Oletsky. This paper introduces a novel approach to solving the Traveling Salesman Problem (TSP) by incorporating fuzzy numbers to represent subjective time perception. By applying the annealing method and fuzzy logic, the research offers a robust solution for optimizing travel routes with variable time perceptions. The study's outcomes contribute to advancing heuristic methods for combinatorial optimization problems, offering efficient solutions applicable in various real-world scenarios.
•   12:40-12:55:  Mathematical Formalization and Problem Solution of Beacon Systems Integration Into UAV by Elshan Hashimov, Elkhan Sabziev and Samad Muradov. This study focuses on mathematically formalizing and addressing the integration of beacon systems into Unmanned Aerial Vehicles (UAVs). By developing solutions for continuous flight and target localization in adverse conditions, the research enhances UAV capabilities for combat missions and surveillance operations. The findings contribute to optimizing radio beacon systems, improving UAV navigation, and increasing resilience in challenging environments.
•   12:55-13:10:  Recognition of Unmanned Flying Devices of Shahed-136 Type by Oleksii Siechko, Anton Kitsera and Roman Kochan. This research aims to recognize unmanned flying devices of the Shahed-136 type using advanced sensor systems and image processing techniques. By analyzing data collected from distributed acoustic sensors, the study seeks to improve Ukraine's defense capabilities and situational awareness. The outcomes provide insights into developing software applications for real-time air environment monitoring, thereby enhancing national security and defense capabilities.
•   13:10-13:25:  Improving Efficiency of MPU-6050 Sensor Module for Inertial Drone Navigation by Tahir Alizada, Elkhan Sabziev, Namig Heydarov and Toghrul Karimov. This study focuses on enhancing the efficiency of the MPU-6050 sensor module for inertial drone navigation. By employing methods such as simple averaging and moving average, the research aims to reduce noise in navigation data provided by the accelerometer and gyroscope. Experimental results demonstrate significant error reduction, offering insights into optimizing sensor module performance for precise drone navigation. The findings contribute to advancing drone technology, improving navigation accuracy, and enhancing operational capabilities.
•   13:25-13:40:  UAV Navigation System for Autonomous Control Based on Artificial Intelligence by Azad Bayramov and Samir Suleymanov. This paper explores the development of a UAV navigation system for autonomous control utilizing artificial intelligence technology. By integrating visual or inertial navigation systems, the research enables precise determination of coordinate-time characteristics in autonomous flight mode. The proposed algorithm and control architecture enhance the capabilities of unmanned aerial vehicles for reconnaissance and surveillance missions. The findings offer valuable contributions to the field of UAV technology, facilitating efficient and autonomous operation in various environments.
•   13:40-13:55:  Refined Computer Modeling of Measurement Testing Process in Eddy-Current Structroscopy Based on Consideration of Apriori Information About Most Influential Factors on Objects by Volodymyr Halchenko, Ruslana Trembovetska, Emil Faure, Volodymyr Tychkov and Nataliia Tychkova. This research presents refined computer modeling techniques for the measurement testing process in eddy-current structroscopy. By incorporating apriori information about influential factors on objects, the study enhances the accuracy of electrical conductivity and magnetic permeability distributions in planar conductive objects. The proposed method, utilizing surrogate optimization and PCA algorithm, offers robust solutions for profile reconstruction and non-destructive testing. The findings contribute to advancing eddy-current testing methodologies, providing insights into optimizing measurement accuracy and efficiency.
•   13:55-14:10:  Air Situation Display Map for Distributed Acoustic Sensor System by Anton Kitsera, Oleksii Siechko and Roman Kochan. This study focuses on developing a software application for an interactive map displaying air situation data collected from distributed acoustic sensor systems. By analyzing information on air target movements in real-time, the research aims to enhance Ukraine's defense capabilities and situational awareness. The development of this system offers prospects for further research in air defense and security, including integration with other surveillance systems and optimization of sensor network performance. The findings provide valuable insights into real-time monitoring and analysis of air environments, contributing to national defense and security efforts.
•   13:55-14:10: Workshop Closing Ceremony