Plenary Session Speakers

Title & Full Name	Prof. NGUYEN Dinh-Dung
Affiliation	Faculty of Aerospace Engineering, Le Quy Don Technical University, Hanoi, Vietnam
Presentation Title	Applications of drones in smart cities: A comprehensive overview of security vulnerabilities and countermeasures for data communication
Abstract	With the advancement of computing technologies, the Internet of Things (IoT), and Information and Communication Technologies (ICT), the utilization of drones has witnessed a significant surge in various real-world applications. Unmanned aerial vehicles (UAVs) or drones have gained widespread attention for their ability to perform diverse tasks across different sectors, as evidenced by numerous studies available in the existing literature. However, the extensive integration of drones in smart cities presents various technical and societal concerns that must be effectively addressed, including cybersecurity, privacy, and public safety. This study provides a concise overview of cybersecurity vulnerabilities and cyber-attacks related to drones, drawing upon a meta-analysis of relevant literature. The discussed vulnerabilities include Wi-Fi security, drone networking security, malicious software, and others. Additionally, the study elucidates several countermeasures, encompassing detection methods and defense mechanisms aimed at safeguarding drones against these security vulnerabilities. Importantly, this paper aims to raise awareness about cybersecurity among users and proposes avenues for future research in this domain. Notably, the majority of cybersecurity vulnerabilities stem from sensors, communication links, and privacy concerns related to captured photos. Therefore, ensuring drone security necessitates a combination of solutions involving multiple sensors, secure communication links instead of Wi-Fi.
Biographical Sketch	Dinh-Dung Nguyen is a professor at the Faculty of Aerospace Engineering, Le Quy Don Technical University, Hanoi, Vietnam. He received his Ph.D. in Transportation Engineering and Vehicle Engineering at the Budapest University of Technology and Economics, Hungary, in 2021. He has been involved in several national and international studies and projects related to sustainable aviation, aviation engineering, aerial vehicles, system design, and control device integration of aircraft, concentrating on assessing security vulnerabilities and countermeasures. He also served on an organizing committee of international conferences. He is a reviewer for international scientific journals. His research interests are drone management, unmanned aerial vehicles, system design, control device integration of aircraft, transportation system in smart city, urban planning and forecasting.
Research Key Words	Cybersecurity, UAV, IoT, Smart city, Vulnerability.

Title & Full Name	PhD Mohammad Alhuyi Nazari
Affiliation	University of Tehran
Presentation Title	Thermal Management of Electronic Devices and Applications of Intelligent Methods for Modeling of These Systems
Abstract	Proper and efficient thermal management of electronic devices such as processor chips would lead to enhancement in their performance and reliability. In absence of effective scheme for thermal management, temperature increment in hotspots and even failure of these systems are more likely to happen. Furthermore, computer chips, as one of the important electronic devices using thermal management, are shifting towards smaller size and dimensions and number of transistors per area of these mediums are increasing. In this regard, employment of efficient cooling systems has attracted attentions in recent decades. Different thermal management techniques including passive and active are applicable for proper thermal management of electronic devices and particularly computer chips. Depending on the applied cooling systems, variety of factors can affect their performance and effectiveness. Operating condition, temperature range, configuration of the system and applied coolant are among the most important factors in the performance of electronic device cooling systems. Performance prediction and modeling of applied thermal management systems are the main concerns of scholars and designers. In comparison with the conventional numerical simulations, e.g. Computational Fluid Dynamics (CFD), there are some advantages in making use of intelligent methods such as artificial neural network. Significant reduction in the computation cost and relatively high precision are among the most advantages of intelligent methods in modeling of complex systems. There are some critical factors that must be considered in modeling and performance prediction of electronic device cooling systems by use of intelligent methods such as applying proper method and configuration as well as consideration of influential factors as the inputs of the proposed models. In this presentation, some of the most important cooling approaches for electronic devices would be introduced and discussed. Afterwards, the key factors that must be taken into account in modeling of these systems by use of intelligent methods would be presented. The main subtitles of the presentations would be as follows: Importance of electronic device cooling. Applicable cooling techniques for thermal management of electronic devices. Important factors in the performance of each cooling technique. Applications of intelligent methods in modeling of thermal systems. Applications of intelligent methods in modeling the performance of electronic device cooling systems. Factors affecting the precision of intelligent methods in modeling of cooling systems.
Biographical Sketch	PhD Mohammad Alhuyi Nazari is an academic researcher from University of Tehran. The author has contributed to research in topics: Nanofluid & Renewable energy. The author has an hindex of 42, co-authored 70 publications receiving 2803 citations.

Title & Full Name	Prof. Dr. Pakizar Shamoi
Affiliation	Kazakh-British Technical University, Almaty, Kazakhstan
Presentation Title	Fuzzy Image Processing: Methods, Applications, and Future Directions
Abstract	Real-world environments and the images captured from them are full of uncertainties. Making computers understand and interpret images as humans do is a big challenge in computer vision. Here, it comes to fuzzy logic, which deals with the imprecise nature of real-world images. Fuzzy Image processing differs from traditional image processing by utilizing fuzzy sets and logic to handle uncertainty, imprecision, and partial truth. It aims to bridge the gap between human cognitive processing and computer vision, mimicking human perception of images. There are many kinds of uncertainty in images, and they span the whole hierarchy of processing levels, from fuzziness in geometrical description to uncertain knowledge at the top processing level based on pixel-based ambiguity. Many fundamental terms in pattern analysis, such as an edge or a corner, are also challenging to define precisely. The core fuzzy concepts and techniques are discussed in the context of image processing, including fuzzy sets and membership functions, fuzzy rules, aggregation, and defuzzification and their roles in image processing tasks. Furthermore, we showcase real-world applications where FIP shows great promise, from fuzzy image similarity measures and preprocessing to edge detection, pattern classification, image quality improvement before further processing, and content-based image retrieval. Applications of FIP include a wide range, including Marketing and Art, Medical Imaging, and Remote Sensing Biometrics, among others. In medical imaging, fuzzy techniques improve the accuracy of diagnosing diseases by enhancing image quality and extracting relevant features. In remote sensing, they facilitate better land use classification and environmental monitoring by dealing with the inherent uncertainty in satellite images. Trends in Fuzzy Image Processing include integration with Deep Learning, Edge Computing, and Hybrid Approaches that combine fuzzy logic with other computational approaches like genetic algorithms and neural networks.
Biographical Sketch	Prof. Dr. Pakizar Shamoi received the B.S. and M.S. degrees in information systems from Kazakh-British Technical University, Almaty, Kazakhstan, in 2011 and 2013, respectively, and the Ph.D. degree in engineering from Mie University, Tsu, Japan, in 2019. In her academic journey, she has held various teaching and research positions at Kazakh-British Technical University, where she has been a Professor with the School of Information Technology and Engineering, since August 2020. She is the author of one book and more than 28 scientific publications. Her research interests include artificial intelligence and machine learning in general, with a focus on fuzzy sets and logic, soft computing, representing and processing colors in computer systems, natural language processing, computational aesthetics, and human-friendly computing and systems. She received awards for the best paper at conferences five times. She took part in the organization and worked in the organization committee (as the Head of the Session and responsible for special sessions) of several international conferences, such as IFSA-SCIS 2017, Otsu, Japan; SCIS-ISIS 2022, Mie, Japan; and EUSPN 2023, Almaty. She served as a Reviewer for several international conferences, including IEEE: SIST 2023, SMC 2022, SCIS-ISIS 2022, SMC 2020, ICIEV-IVPR 2019, and ICIEV-IVPR 2018.

Title & Full Name	Professor Elhadj Benkhelifa
Affiliation	Staffordshire University
Presentation Title	Cloud Data Governance, I Don’t Mean Data Management
Abstract	In today’s data-driven world, the management of data has evolved into a formidable challenge. The conventional methods are proving inadequate and expensive, struggling to keep pace with the escalating complexity of data. As businesses increasingly rely on data for decision-making and innovation, there’s a pressing need for a more sophisticated approach. This is where data governance steps in as the linchpin of effective data management strategies. Historically, attempts at data governance often fell short, constrained by their narrow focus on IT-driven solutions. These initiatives were characterized by rigid processes and fragmented efforts, failing to address the broader organizational context. However, forward-thinking entities now recognize that effective data governance extends beyond technological fixes. It demands a comprehensive framework encompassing clear regulations, organizational alignment, and widespread support. Despite its paramount significance, data governance remains an underexplored and underdeveloped domain. Nowhere is this more apparent than in the realm of cloud computing, where governance challenges loom large. Indeed, apprehensions about data governance often serve as a significant barrier to the adoption of cloud technologies, with security concerns taking center stage. Research illuminates the extent of the governance-security nexus in the cloud, revealing that a substantial portion of security issues can be traced back to governance shortcomings. Alarmingly, 41% of such challenges stem directly from governance and legal complexities. This underscores the critical importance of delving deeper into the intricacies of cloud data governance, particularly from a security perspective. In this Talk, I will delve into key research findings within the realm of cloud data governance, unravelling their profound implications. By shining a light on these critical insights, our aim is to foster a deeper appreciation for the indispensable role of robust data governance in navigating the complexities of modern data management. Specifically, we seek to underscore the imperative of effective governance in addressing the challenges posed by cloud computing, thereby empowering organizations to harness the full potential of their data assets while safeguarding against potential risks.
Biographical Sketch	Elhadj Benkhelifa is a Full Professor of Computer Science and the Head of Professoriate at Staffordshire University. He is also the founding Director of the Smart Systems, AI and Cybersecurity Research Centre, managing 20 research staff and 32 PhD Students. He was previously the Director of the Cloud Computing and Applications research centre (2015-2020) and the Director of the Mobile Fusion Applied Research Centre (2014-2016).  Elhadj research areas cover cloud computing and applications in its centralised and decentralised forms (Fog/Edge computing, Cloudlet, Blockchain etc), Software Defined Systems. Service Computing, Cybersecurity & Digital Forensics, Data (Governance, Semantics, analytics, Social Networks), Artificial Intelligence and Software Engineering methods . Elhadj has delivered 40+ keynote talks internationally and has edited a number of conference proceedings and special editions of Scientific Journals. He has published 170+ research papers in conferences and journals and has been the Principal Investigator of a number of collaborative projects. Elhadj has chaired many prominent IEEE conferences in different parts of the world. Elhadj is currently the Chair of the IEEE UK&I Section’s Education office and sits on the West Midland Cyber Resilience Centre’s Advisory Board and he sits on the Staffordshire Police Digital Forensics Board. Elhadj is Senior Member of IEEE, a Fellow of the UK Higher Education Academy and Prince2 Practitioner.

Title & Full Name	Dr.Korhan Kayisli
Affiliation	Gazi University, Ankara, Turkey
Presentation Title	ARTIFICIAL INTELLIGENT APPLICATIONS on SMART CITIES
Abstract	Smart cities are complex and their definitions keep changing as technology progresses. Various groups and government bodies have tried to define what a smart city is, focusing on common objectives: improving life quality through data use, making systems work together better, and aiming for the highest level of sustainability and environmental care. While there’s agreement on these goals, the specific areas of focus, like transportation and energy, can vary by region. It’s evident that key areas to focus on are making energy production and management more eco-friendly, considering economic impacts, ensuring safety and security, promoting health and well-being, improving transportation, supporting education, and effective governance. At its core, a smart city aims to use data to make things better and thus improve people’s lives, with a big emphasis on being kind to the environment. Achieving this goal demands a lot of data crunching, managing knowledge, and controlling processes efficiently. Research has shown that to make systems more efficient in real-time, it’s crucial to have the ability to learn from data, process it, and then make smart decisions quickly. This is where artificial intelligence (AI) comes in, as it can handle these tasks by mimicking human thought processes and making decisions swiftly. The application areas are Smart City Control and Modelling, Smart City and Smart Infrastructure, Energy Production, Management and Efficiency, Communication, Data Processing and Management, Area Planning, Traffic Management, Smart City Security and Privacy, Smart City Components, The Dangerous Situation Detection etc.
Biographical Sketch	Dr.Korhan Kayisli received a BSc degree in electronics education from Sakarya University, Sakarya, Turkey, in 2001, and an MSc degree in Electronics and Computer Science from Firat University, Elazig, Turkey, in 2004. He received PhD degree at the area of power electronics in Electric and Electronics Engineering at Firat University, Elazig, Turkey, in 2012. He worked as a research assistant between 2002 and 2012. He has worked in Firat University, Bitlis Eren University, Gelisim University, Nisantasi University, respectively. He is currently an associate professor in the Department of Electrical Electronics Engineering, Engineering Faculty, Gazi University, Ankara, Turkey. He is an IEEE Senior Member. Also, he is the co-editor of International Journal of Renewable Energy Research and International Journal of Engineering Science and Application. He also served as reviewer to many high ranked scientific journals. His fields of interest are power electronics, converter circuits, power factor correction, robust control, and educational technologies. He has published journal and conference papers on these areas. Additionally, he has worked as researcher in two EU mobility projects and other some projects.

Title & Full Name	Professor Ardashir Mohammadzadeh
Affiliation	Multidisciplinary Center of Infrastructure Engineering, Shenyang University of Technology, Shenyang, China; Associate professor at university of Bonab, Bonab, Iran
Presentation Title	Application of AI in automation and control systems: Fourier-based type-2 fuzzy neural networks
Abstract	One of the key trends in information technology is the rise of artificial intelligence (AI) and machine learning. AI has the potential to revolutionize industries by automating processes, improving decision-making, and enhancing customer experiences. Machine learning algorithms are being used to analyze vast amounts of data and extract valuable insights, enabling organizations to make more informed decisions and drive innovation. On the other hand, intelligent automation and control systems are a trend that has primarily hit the manufacturing and production units and is estimated to only grow more in the coming years. Intelligent automation has also enabled processes to work faster and would allow companies to reach their goals much more efficiently. In this talk, first, the AI systems and majors are defined, the main application and challenges of AI in control systems are summarized, and a new approach of intelligent fuzzy systems is presented as a solution to deal with high dimensional problems. The concept of Fourier-based type-2 fuzzy neural networks is presented and by some examples such as face recognition problem, English handwriting digit recognition, and modeling problem with real-world data its effectiveness is illustrated
Biographical Sketch	Ardashir Mohammadzadehis Professor at Multidisciplinary Center of Infrastructure Engineering, Shenyang University of Technology, Shenyang, China and Associate professor at university of Bonab, Bonab, Iran. Ardashir research areas cover Intelligent control, Artificial intelligence, Adaptive control, Robust control, Fuzzy neural networks, Optimization and Machine learning. He is the author more than 170+ paper publications  and co- author of two books.

Title & Full Name	Professor  Oleksandr Mitsa
Affiliation	Uzhhorod National University
Presentation Title	A comparative study of machine learning algorithms and the prompting approach using GPT-3.5 turbo for text categorization
Abstract	The study focuses on text categorization tasks, comparing the effectiveness of traditional Machine Learning (ML) models with Large Language Models (LLM), such as GPT-3.5 turbo. The literature review tracks the historical progress in text categorization from early ML algorithms to LLMs, which automatically determine contextual features, simplifying the process. The goal of the research is to evaluate whether LLMs with a prompt-based approach can outperform traditional ML models in text categorization. A dataset of 55,235 questions in nine categories is used. The effectiveness of categorization is determined by the F1 score. Various ML models such as Logistic Regression and Random Forest were used for categorization, while models like curie, davinci, and GPT-3.5 turbo were used for categorization with LLM. The study found that traditional ML models provided better categorization (F1 score – 88%), whereas LLMs, particularly GPT-3.5 turbo, offered competitive but inferior results without prior training (F1 score – 72%). The discussion highlights the advantages of LLMs, such as their suitability in scenarios without historical data for training and their ease of use. Disadvantages are also cited, such as higher costs for large data volumes and potential instability in API operation. In conclusion, the study recommends LLMs for certain applications, such as new applications or those with limited categorization needs. Traditional ML models remain more suitable for scenarios requiring high accuracy or processing sensitive data.
Biographical Sketch	Mitsa Oleksandr – Head of the Department of Information Control Systems and Technologies, Doctor of Technical Sciences, Full Professor,  author of more than 100 scientific publications, foreign member of Hungarian Academy of Sciences, member of Association for Computing Machinery.

Title & Full Name	Professor Mourat A. Tchoshanov
Affiliation	University of Texas at El Paso, Texas, USA
Presentation Title	Designing and Studying STEM Learning in Digital Age
Abstract	Raising demands of digital age require interdisciplinary approaches to face challenges of intensive implementation of the ICT in STEM education [2]. In this theoretical proposal, relationship between engineering and didactics is closely examined in order to conceptualize the construct of didactical engineering as an application of engineering methodology to designing and studying STEM learning. Engineering design thinking as a human activity may be applicable to various professions and it involves analysis, design, construction, and quality control of objects and processes for practical purposes [1]. Key terms (e.g., engineering, didactics, engineering design, and engineering didactics) are analyzed to validate the new construct. Subject domain of the didactical engineering is determined as design and study of outcome-oriented teaching products through application of a scientific method to the analysis of didactical systems, processes, and situations for creating effective online STEM learning environments. The new construct provokes rethinking of traditional didactics which is limited to theory and practice of teaching and learning. In the digital era with emphasis on interdisciplinary approaches, didactics encompasses scientific method, engineering methodology, and design thinking to meet the demands of quality instruction in STEM education. Therefore, the ICT-integrated didactics is considered as science, engineering, and art of teaching and learning in digital age with emphasis on the quality of STEM education content design, content interactivity, and content communication. Significant practical implication of the ICT-integrated didactics is that it supports the emerging design-based paradigm as a metaphor of learning-by-designing to address the needs of educational practice (e.g., STEM Education). In its term, the Design-based Paradigm suggests to re-think existing approaches: to learning – by focusing on the development of design thinking, to teaching – by shifting from teaching to engineering learning, to research – by employing the iterative engineering design process: design-implement-evaluate-redesign.

Title & Full Name	Prof. Dr. Çetin ELMAS
Affiliation	Gazi University, Technology Faculty, Electrical Electronics Engineering Department, Turkey
Presentation Title	Artificial Intelligence in Project Management
Abstract	In recent years, the integration of Artificial Intelligence (AI) into Project Management (PM) has emerged as a promising frontier for enhancing the efficacy and predictability of project outcomes. AI, with its roots deeply embedded in a confluence of disciplines including computer science, information theory, cybernetics, linguistics, and neurophysiology, endeavours to replicate human cognitive functions through sophisticated algorithms and data processing capabilities. Lotfi Zadeh, a pivotal figure in the development of Fuzzy Logic, coined the term “soft computing,” emphasizing AI’s capacity to emulate the nuanced and adaptable reasoning processes of the human mind amidst ambiguity and uncertainty. This conceptual foundation of AI aligns closely with the demands of PM, a field characterized by its inherent complexities, uncertainties, and the critical need for adaptive decision-making based on incomplete information. Project Management, traditionally challenged by a myriad of uncertainties and the high stakes of decision-making, stands to benefit significantly from AI’s predictive and analytical prowess. The application of AI in PM spans various functions, including project evaluation, diagnosis, decision-making, and forecasting, aiming to automate and refine management processes. The utilization of predictive analytics, a facet of machine learning, exemplifies AI’s potential to transform PM by leveraging project data to forecast completion rates and the likelihood of meeting predefined objectives and deadlines. This capability addresses a critical challenge in PM: the scarcity of reliable data for assessing project performance and outcomes. The synergy between AI and PM heralds a new era of efficiency and accuracy in project execution. By harnessing AI’s capabilities, project managers can navigate the complexities of their roles with enhanced support, making informed decisions that mitigate risks and capitalize on opportunities. This presentation delves into the recent advancements in AI techniques applied to PM, shedding light on how these innovations are reshaping the landscape of project management. Through a detailed exploration of current trends and applications, it becomes evident that AI’s role in PM is not just transformative but also indispensable for meeting the evolving demands of project execution in a rapidly changing world.
Biographical Sketch	Prof. Dr. Çetin Elmas, a distinguished academic in electronics and electrical engineering from the University of Birmingham, holds a Full Professorship at Gazi University, Ankara. His career is marked by founding and leading various academic units, alongside managerial roles in both public and private sectors. Currently, he significantly contributes to the International Project Management Association, serving as the Global Head of IPMA AI SIG, and President of both the Project Management Association and the Industrial Artificial Intelligence Association. Notably, he chaired the 24th IPMA World Congress in Istanbul and played a pivotal role in developing the Turkish National Project Management Competencies. Prof. Elmas is also a prolific author, with numerous books, papers, and patents to his name

Title & Full Name	Professor  Sadok BEN YAHIA
Affiliation	University of Southern Denmark
Presentation Title	Resilient Data-Driven enabled Zero-touch   framework for urban mobility and safety
Abstract	The transportation sector is responsible for 23% of energy-related CO2 emissions. Decarbonizing transportation is challenging, as it is still 92% dependent on non-renewable resources. However, current transport decarbonization-related policies are insufficient to decrease CO2 emissions to the expected level. Therefore, strategic approaches to reducing emissions from urban transport are critical to addressing the challenges of climate change. In this talk, we present our recent research activities on a framework to build the next level of innovative data-driven traffic light strategies as the most impactful action to reduce CO2 emissions within the context of urban mobility for Connected and Autonomous Cars. This Framework is committed to embracing the next generation of Edge-AI, benefiting from the ease of implementation and increased computation power toward more composable, distributed, and federated intelligence, as well as security by design frameworks. Powerful eye-bird-view multimodal data fusion approaches feed AI models for accurate CO2 and urban noise level predictions, that feed to dashboards for awareness purposes. Advanced reinforcement learning techniques make use of urban noise predictions to implement the best traffic light strategy in real time. We will also discuss the challenges to achieve resilience by proactively detecting misbehaving entities within Vehicle-to-Everything settings
Biographical Sketch	Sadok BEN YAHIA Full Professor at the University of Southern Denmark  (SDU) since September 2023. Before joining SDU, he was full professor at Technology University of Tallinn (TalTech) since January 2019. He obtained his HDR in Computer Sciences from the University of Montpellier (France) in April 2009. His research interests mainly focus on data-driven approaches for near-real-time Big Data analytics, e.g., urban mobility in smart cities (e.g., information aggregation & dissemination, traffic congestion prediction), Recommendation System. and fake content fighting.

Title & Full Name	Associate Professor Bohdan Haidabrus
Affiliation	Riga Technical University, Riga, Latvia
Presentation Title	Generative AI in Agile, Project and Delivery Management
Abstract	Generative AI has the potential to revolutionize Agile, Project, and Delivery Management by automating repetitive tasks, predicting potential roadblocks, and suggesting optimal strategies for project completion. By analyzing vast amounts of data from previous projects and team interactions, generative AI algorithms can offer valuable insights into resource allocation, task prioritization, and risk mitigation. This technology enables project managers to make data-driven decisions quickly, adapt to changing circumstances, and optimize project workflows for maximum efficiency. Integrating generative AI into Agile methodologies can foster a culture of continuous improvement, enabling teams to iterate faster, deliver higher-quality results, and ultimately achieve project success more consistently. With its ability to learn and evolve over time, generative AI holds promise for reshaping the landscape of project management, driving innovation, and enhancing productivity in diverse industries.
Biographical Sketch	Experienced Project and Program Management leader, Agilist. More than 12 years of successful project management experience, more than 5 years of high level managerial experience. Successful experience in project and delivery management; implementation of Project Management methodologies (PMI, IPMA, P2M, etc.); and Agile ways of working. Over 5 years experience as Scrum Master in Software Development teams (Data science, Digital, DevOps). Certified Scrum Master (PSM I) and Scaled Agile Framework (SAFe 5 Practitioner). Has successful experience as a tutor, facilitator and coach in IT/Software Development/Agile Teams.

Title & Full Name	Prof. Ling Tian
Affiliation	School of Computer Science and Engineering, UESTC, Chengdu,  China
Presentation Title	Knowledge Hypergraphs and Reasoning for Smart Systems
Abstract	Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the knowledge in real world, and as a way to integrate information extracted from multiple data sources. It is widely acknowledged that knowledge graph and reasoning can provide essential information and insights for Intelligent Perception and Prediction across many fields. This keynote will cover our new research on Knowledge Hypergraphs, and its superior efficiency besides traditional KGs. Specifically, we integrate the novel computational and reasoning models into lightweighted edge devices. Finally, this keynote will introduce the industrial applications in object detection and event prediction for smart systems.
Biographical Sketch	Ling Tian is a Full Professor of School of Computer Science and Engineering at University of Electronic Science and Technology of China (UESTC). She received the B.S., M.S., and Ph.D. degrees from the School of Computer Science and Engineering, UESTC in 2003, 2006 and 2010, respectively. Her research areas focus on artificial intelligence, data processing, and so on. She has been principal investigators in more than 10 national major research projects, awarded 6 national and ministerial prizes, published more than 80 papers in top journals and conference, edited 2 books, holds over 50 China patents, and contributed over 10 technology proposals to the standardizations such as IEEE 1857.9 VRU standard and China Cloud Computing standard. She served as the Local Chair for 2021 ACM International Conference on Multimedia.

Title & Full Name	Professor Minsoo Hahn
Affiliation	Astana IT University
Presentation Title	Some Audio- and Speech-related researches
Abstract	In my presentation, I am intending to introduce some of my previous research outputs. My previous works this time may include ‘binaural 3-D audio,’ ‘vocal information separation of spatial audio object coding (SAOC) MPEG standards,’ ‘a voice color conversion trial with a nearly few-shot learning.’ Binaural 3-D audio: It has been well studied that our head and ear shapes are highly related to the perception of sound-source direction for human beings. It is rather easy to understand the right-to-left directional change of the sound source because the main cue for this kind of direction perception is the sound arrival time difference to our left and right ears. But it becomes a little harder to answer the question how we can distinguish the sound coming from just ahead of us from that from just back of us. This can be answered by introducing the Head-Related Transfer Function (HRTF) concept. That means, our head and ears work as a directional filter and the change in the HRTF produces the frequency characteristics change of the perceived sound. And as you can easily imagine, our brain can perceive this frequency property change and can map it into the directional information. Vocal information separation on Spatial Audio Object Coding (SAOC): The SAOC is one of the multichannel audio coding that aims to reduce the information amount dramatically by encoding multi-channel audio into a stereo audio stream with slightly added directional information for each sound objects. In consideration of using original high-quality instrumental audio and chorus contents by eliminating only the lead-vocal sound for some instances such as Karaoke, there has been some trials to eliminate only the lead-vocal contents from the SAOC musical bitstream and my work is one of them. Voice color conversion trial with a nearly few-shot learning: A few shot learning approach for voice color conversion was tried and produced fairly good results. The analysis of our results shows that the fundamental frequency, i.e., F0, can be learned so quickly but spectral information needs more data to produce the same performance of learning. And it also confirms the basic idea that F0 change is the most important factor for successful voice color conversion.

Title & Full Name	Prof. Sergiy Bronin, PhD
Affiliation	Taras Shevchenko National university of Kyiv (associate professor on IST department, international projects coordinator) Tel-Aviv University (research fellow by internship)
Presentation Title	Artificial Intelligence and other smart tools in frame of Digital University
Abstract	For the last decade, the world of education in general and the world of higher education has been going more and more virtual and utilising various digital technologies more deeply. The latest trends in the introduction of Artificial Intelligence (AI) in many areas of human activity cannot bypass the education sector. Unfortunately, a factor of particularly rapid development of the digital world in Ukraine was russia’s invasion of its sovereign territories, which naturally caused huge waves of migration of both students and higher education institutions and their teachers. Therefore, it is in Ukraine that a pioneering project to create an all-Ukrainian inter-university digital platform with artificial intelligence is being developed. The Learning Management System (LMS) that was chosen as the basis for the DigiUni project (Digital University – Open Ukrainian Initiative) is constantly being refined, improved and supplemented with new features and functionality, including the development of its artificial intelligence. Already today, the capabilities of the inbuilt AI allow to automatically check the code generated by students while studying programming courses and much more. The presentation is dedicated to describing the project and the initiative as a whole, the so-called DigiPlatform under the control of an LMS equipped with constantly evolving AI, and a range of smart tools that are planned to be applied on the DigiPlatform. We hope that as a result of the project implementation all practical results as well as scientific conclusions will not only be used in their original place of application, but also applied for further development of intelligent systems, including LMS.
Biographical Sketch	22 years in academia, 18 years in public space, 20+ IT projects in real sector, 3 years responsible for IT sphere in Ukraine, 5 CBHE Erasmus+ & 10+ other programme projects. Prof. Bronin is focused mainly on international scientific and education projects preparation and management. In home university: Taras Shevchenko National University of Kyiv he is responsible for international projects coordination. He is an author of more than 50 scientific papers, methodologic papers, textbook recommended by the Ministry of Education and Science of Ukraine, etc. (the Hirsch Index in Scopus – 5). As a research fellow by internship in Tel-Aviv University his efforts are directed to research in the field of Human-artificial intelligence synergetic interaction. Throughout his professional career, the theme of digitalisation targeted at the individual-society-state, regardless of his changing interests in the field of scientific research, has been a separate line of emphasis.

Title & Full Name	Professor Şeref Sağiroğlu
Affiliation	Head of Computer Engineering Department of Gazi University, Director of Gazi AI Center of Gazi University, Ankara, Turkiye
Presentation Title	Ai in Health and Turkish Brain Project
Abstract	The main objective of this presentation is to deliver a speeh on ai and health in the world and explain a recent Project called and completed Turkish Brain Project supported by the Digital Transformation Office of the TR Presidency. The details of the project should be explained in more details such as producing the new dataset, developing model and platform, testing models in hospitals, sharing valuable experiences, and important issues for the future developments. The talk will be concluded with some suggestions and remarks.
Biographical Sketch	Prof. Dr. Seref Sagiroglu completed his undergraduate education in 1987 at Erciyes University, Department of Electronics Engineering. He completed his doctoral studies at the University of Wales College of Cardiff (now Cardiff University, UK) in 1994. He continues his academic career as a full professor of software engineering at Gazi University’s Computer Engineering Department. Sagiroglu is an outstanding academic with more than 10000 citations and almost 400 articles published in SCI/SSCI-indexed journals, national and international conferences, symposiums, and workshops. Professor Sagiroglu; •               is an author and editor of more than 20 books, owns 4 patents, and has completed national and international projects on security, big data, intelligent modeling and control, biometrics, etc. •               organized more than 50 national and international events on artificial intelligence, 5G, Big Data, Machine Learning, Deep Learning, Information and Cyber Security, Privacy, IPv6, etc., as a chairman or co-chairman. •               also has been a founding member of the Information Security Association (www.bilgiguvenligi.org.tr); Member of IEEE Biometric Task Force, Turkish Science Research Foundation and The Foundation of the People Caring for the Future and had such duties as President and Executive Committee Members of those NGOs. •               completed the duties as the Deans of Graduation School of Science and Technology and Engineering Faculty and Head of the Computer Engineering Department at Gazi University; •               is the Editors of International Journal of Information Security Science; International Journal of Information Security Engineering and was CyberMag; •               had the General Director of FutureTech •               had a Member of Cyber Security Group of Higher Education Council of Turkey. •               contributed to consultants to Havelsan; IT Regulatory Body of Turkey (BTK) and Personal Data Protection Regulatory Body of Turkey (KVKK). •               has delivered as invited or keynote speakers more than 500 seminars, talks, conferences at universities, schools, sectors, TV and Radio Programs, institutions and organisations in the topics of Information Security, Big and Open Data, Cyber Security and Defense, Artificial Intelligence, Computer and Software Engineering, Privacy, Biometrics, Innovation Culture Creation, IPv6, 5G, etc. •               now is the AI and Big Data Center director and Head of the Computer Engineering Department of Gazi University, Ankara, Türkiye.

Title & Full Name	İkhami Çolak
Affiliation	Istinye University, Faculty of Engineering and Natural Science,Department of Electrical and Electronics Engineering, Istanbul/Turkey
Presentation Title	Electric Vehicles
Abstract
Biographical Sketch	Prof. Dr. ilhami COLAK was born in 1962 in Turkey. He received his diploma in Electrical Engineering from Gazi University, Turkey in 1985. Then he did his MSc in Electrical Engineering in the field of Speed Control of Wound Rotor Induction Machines Using Semiconductor Devices at Gazi University in 1991. After that he did his MPhil at Birmingham University in England by preparing a thesis on High Frequency Resonant DC Link Inverters in 1991. Finally, he did his PhD at Aston University in England on Mixed Frequency Testing of Induction Machines Using Inverters in 1994. He became an assistant professor, an associate professor and a full professor in 1995, 1999 and 2005 respectively. He has published more 450 journal and conference papers, 7 books in different subjects, including electrical machines, drive systems, control, machine learning, reactive power compensation, inverter, converter, artificial neural networks, distance learning automation and alternating energy sources. More than 288 of his papers have been cited in SCI. His papers have received more than 2903 citations by 2600 papers. His Thomson Reuters h-index is 27 and average citation per paper is 10.85. He published more than 400 papers in SCOPUS. h-index is 34. His papers have received 4900 citations from 4090 documents. He supervised 23 MSc students and 14 PhD students. He is a senior member of IEEE, member of IES, IAS, PELS and PES. Last twenty years, he has been concentrated his studies on renewable energy and smart grids by publishing papers, journals (www.ijrer.org) (www.ijSmartGrid.orgg) and organizing international IEEE sponsored conferences (www.icrera.org), (icSmartGrid.org). He also spent around 3 years at European Commission Research Centre (JRC) in Netherlands. He served as the head of department, dean and vice rector at Gazi University, Istanbul Gelisim University and Nisantasi University. He is currently a full professor at Nisantasi University and also vice-rector, and dean of engineering faculty. Professor COLAK achieved a great success of 10% by being included in the “”World’s Most Influential Scientists”” 2% list, which was created USA Stanford University considering the “”Works of the Year 2020-2021-2022-2023″”.

Title & Full Name	Dr.Ibraheem Shayea
Affiliation	​Istanbul Technical University (ITU). ​​Faculty of Electrical and Electronics Engineering. Electronics & Communications Engineering Department
Presentation Title	Drivers and Key Issues of Massive Mobile Data Traffic Growth in Mobile/Cellular Communication Networks
Abstract	The massive and continuous increase in mobile data traffic growth become one of the significant key matters that are studying in academic and research centres. The massive and continuous growth stems from various reasons within communications networks. That including the massive growth of connected mobile devices, coupled with the huge growth of data-intensive applications, the advent of high-speed mobile data services like 4G and 5G, surging demand for high-speed data services, and the occurrence of data-intensive applications. Furthermore, factors like remote work, online education, and telemedicine, accelerated by the COVID-19 pandemic, have further exacerbated the demand for mobile data services. While this surge offerings opportunities for innovation and business evolution, it is producing significant technical challenges for cellular network operators. The main problem lies in managing and accommodating the escalating data demands efficiently while keeping service quality, network performance, and user experience. Striking a delicate balance between capacity expansion, spectrum efficiency, and infrastructure optimization becomes crucial to meet the ever-growing appetite for data while ensuring seamless connectivity and reliable service delivery across cellular networks. Thus, highlighting these issues is the key points that is targeted to be explained to enable researchers give a more focus for addressing them efficiently. This brief presentation will give a brief focus on some of these key technical issues that need to be addressed.
Biographical Sketch	Ibraheem Shayea (Site   WoS) is working as an Assistant Professor since March 2022 at Istanbul Technical University (ITU), Faculty of Electrical and Electronics Engineering, Department of Electronics & Communications Engineering, Istanbul, Turkey. He was an Associate researcher at the same place Since 1st September 2019 to 30 September 2022). Also, He is Associate researcher with Wireless Communication Center (WCC), University of Technology Malaysia (UTM), Malaysia (2021-2022). The key focuses of his research areas are on mobility management in future Heterogeneous (4G, 5G, and 6G) Networks, Mobile Edge Computing, Machine, and Deep Learning, Internet of things (IoT), Propagation of millimetre-wave, Mobile Broadband Technology and Future Data Traffic Growth and Spectrum Gap Analysis. He has published several scientific research Journals, and conference papers. He had worked from the 1st of September 2018 until 31 August 2019 as a Researcher Fellow at Istanbul Technical University (ITU), Istanbul, Turkey. From the 1st of January 2016 until 30 Jun 2018, he worked as a Postdoctoral Fellow at Wireless Communication Center (WCC), University of Technology Malaysia (UTM), Malaysia. From the 1st of January 2011 until 30 December 2015 (during his Ph.D. study), he worked as a Research Assistant and Demonstrator at the Department of Electrical, Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Malaysia. He received his Ph.D. in the field of Electrical & Electronic Engineering, specifically in Wireless Communication Systems, and master’s degree in Communication and Computer Engineering from the Department of Electrical, Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Malaysia, in 2015 and 2010, respectively. In July 2004 he got his degree, Bachelor in Electronics and Communication Engineering from the Faculty of Engineering, University of Diyala. From the 1st of January, 2005 until 27 Jun 2006, he worked in Yemen as a Computer, Electronic maintenance engineer and Maintenance Manager at different companies in Yemen. emails: {shayea@itu.edu.tr; ibr.shayea@gmail.com}.