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Conference Scope

2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST) topics include, but are not limited to, the following research areas: Technology and Engineering Management, Governance, Finance, and Economy; IT in Education and Research; Emerging Trends and Technologies in IT Application; Data Science and Advanced Analytics.
The conference will be technically co-sponsored by IEEE Ukraine Section, IEEE Kazakhstan Sub-Section and IEEE Kazakhstan CIS/RA Societies Joint Chapter. The 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST) is a significant event in the scientific society of Kazakhstan which will have business people, scientists, researchers, specialists, and students from different areas of the IT industry from near and far abroad. This conference allows people to exchange their experience, enlarge and broaden their knowledge and perspectives on specific topics, widen their professional network, and contribute to finding responses to some issues on recent trends in IT. The conference may serve as a starting point for ideas for realizing governmental programs and development and research works as a result.

Upcoming conferences 2024-2028 years

2024 4th International Conference on Smart Information Systems and Technologies (SST)Astana IT University,Dates: May 15, 2024-May 17, 2024,Venue: Astana, Kazakhstan
2025 5th International Conference on Smart Information Systems and Technologies (SIST),Astana IT University,Dates: May 14, 2025-May 16, 2025,Venue: Astana, Kazakhstan
2026 6th International Conference on Smart Information Systems and Technologies (SIST),Astana IT University,Dates: May 13, 2026-May 15, 2026,Venue: Astana, Kazakhstan
20277th International Conference on Smart Information Systems and Technologies (SIST),Astana IT University,Dates: May 12, 2027-May 14, 2027,Venue: Astana, Kazakhstan
20288th International Conference on Smart Information Systems and Technologies (SIST),Astana IT University,Dates: May 17, 2028-May 19, 2028,Venue: Astana, Kazakhstan

Conference Organizers/Partners:

Conference sponsor:

Information partner:

Conference Program

Core topics

1. Technology and Engineering Management
2. IT in Education and Research
3. Emerging Trends and Technologies in IT Application
4. Data Science and Advanced Analytics

1. Technology and Engineering Management

  • Digital Innovation Management
  • Smart Services and Software Platforms
  • Sustainable Engineering
  • Smart City
  • Future Mobility & Smart Mobility
  • Research and Innovation in Creative Industries
  • Business Process Management, re-Engineering and Modelling
  • Rethinking Project Management
  • Metadata and Public Standards
  • Business Models and Economics of Community
  • Internet as Base of Distributed Business Models
  • Location and Context Management
  • Data Science and Business Intelligence
  • e-Commerce and Mobile Commerce
  • Digital Project Management
  • Digital transformation
  • Information Technologies in Management and Administration
  • Technological Entrepreneurship

2. IT in Education and Research (IITU)

  • Human-Computer Challenge and Interaction
  • Public Access to Information and Globalization
  • Different Approaches to Implementation of IT in S&T Education
  • Industry-University Partnership
  • Knowledge Management in e-Learning
  • Virtual Classrooms and Universities
  • Learning & Content Management Systems
  • Gender equality in STEM
  • Mobile Learning Applications
  • e-Learning Platforms and Tools
  • Management in Education
  • Digital Literacy

3. Emerging Trends and Technologies in IT Application

  • Technologies in Cybersecurity
  • Web-based systems Advanced Database and Web Applications
  • Human-Computer Interaction
  • Green IT Technologies
  • Virtualization Concept
  • Social Applications
  • GIS and Remote Sensing
  • Distributed and Parallel Computing
  • Information System Tools, Standards, Architectures, Platforms
  • Cloud Computing
  • Edge Computing
  • Distributed Processing
  • Decision Support Systems
  • Concepts and Development of Information Systems
  • New SPI Models: SaaS, PaaS, IaaS, CaaS, XaaS
  • Location and Mobility Semantics
  • Social Network Analysis Methods and Applications
  • Context-aware Computing and Location-based Services
  • Web2 and Web3 New Development Paradigm
  • Web Ontology and Development
  • Semantic Web: Concepts, Technologies and Applications
  • Augmented Reality
  • Service Oriented Architecture
  • Cybersecurity
  • IoT and Rural Wireless Networks
  • AgroTech

4. Data Science and Advanced Analytics

  • Advanced Statistical Analytics
  • Software Techniques and Architectures in Cloud/Grid/Stream Computing for Big Data
  • Domain-Specific Applications of Data Analytics
  • Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
  • New Computational Models for Big Data
  • Data Visualization at Scale
  • Forecast of Time Series
  • Game Data Science
  • Big Data Open Source Platforms
  • Software Systems to Support Big Data Computing Data Management in the Social Web
  • Large-Scale data Management and Analysis
  • Big Data and Mobility
  • Big Data Analytics in Government, Public Sector and Society in General
  • Complex Big Data Applications in Various Areas Data-intensive Computing and Map Reduce
  • Pattern Recognition and Machine Learning
  • Deep Learning, NLP
  • Information Theory for Knowledge Models
  • Computing and Metaheuristics
  • Computer Linguistics

Keynote Speakers

Title & Full Name Prof. NGUYEN Dinh-Dung
AffiliationFaculty of Aerospace Engineering, Le Quy Don Technical University, Hanoi, Vietnam
Presentation TitleApplications of drones in smart cities: A comprehensive overview of security vulnerabilities and countermeasures for data communication
AbstractWith 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 SketchDinh-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 WordsCybersecurity, UAV, IoT, Smart city, Vulnerability.
Title & Full Name PhD Mohammad Alhuyi Nazari
AffiliationUniversity of Tehran
Presentation TitleThermal Management of Electronic Devices and Applications of Intelligent Methods for Modeling of These Systems
AbstractProper 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 SketchPhD 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 NameProf. Dr. Pakizar Shamoi
AffiliationKazakh-British Technical University, Almaty, Kazakhstan
Presentation TitleFuzzy Image Processing: Methods, Applications, and Future Directions
AbstractReal-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 SketchProf. 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
AffiliationStaffordshire University
Presentation TitleCloud Data Governance, I Don’t Mean Data Management
AbstractIn 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 SketchElhadj 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 NameDr.Korhan Kayisli
AffiliationGazi University, Ankara, Turkey
AbstractSmart 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 SketchDr.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 NameProfessor Ardashir Mohammadzadeh
AffiliationMultidisciplinary Center of Infrastructure Engineering, Shenyang University of Technology, Shenyang, China; Associate professor at university of Bonab, Bonab, Iran
Presentation TitleApplication of AI in automation and control systems: Fourier-based type-2 fuzzy neural networks
AbstractOne 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 SketchArdashir 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 NameProfessor  Oleksandr Mitsa
AffiliationUzhhorod National University
Presentation TitleA comparative study of machine learning algorithms and the prompting approach using GPT-3.5 turbo for text categorization
AbstractThe 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 SketchMitsa 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 NameProfessor Mourat A. Tchoshanov 
AffiliationUniversity of Texas at El Paso, Texas, USA
Presentation TitleDesigning and Studying STEM Learning in Digital Age
AbstractRaising 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 NameProf. Dr. Çetin ELMAS  
AffiliationGazi University, Technology Faculty, Electrical Electronics Engineering Department, Turkey
Presentation TitleArtificial Intelligence in Project Management
AbstractIn 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 SketchProf. 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 TitleResilient Data-Driven enabled Zero-touch   framework for urban mobility and safety
AbstractThe 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 SketchSadok 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 NameAssociate Professor Bohdan Haidabrus  
AffiliationRiga Technical University, Riga, Latvia  
Presentation TitleGenerative AI in Agile, Project and Delivery Management
AbstractGenerative 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 SketchExperienced 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 NameProf. Ling Tian 
AffiliationSchool of Computer Science and Engineering, UESTC, Chengdu,  China
Presentation TitleKnowledge Hypergraphs and Reasoning for Smart Systems
AbstractKnowledge 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 SketchLing 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
AffiliationAstana IT University
Presentation TitleSome Audio- and Speech-related researches
AbstractIn 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.