Accepted Special Sessions

Special Session 01: AI Supported Collaborative Work in Management and Control in the Digital Era: Methods, Tools, Systems, Applications, and Evaluation

Prof. F. G. Filip, The Romanian Academy, Bucharest, Romania, ffilip@acad.ro (https://acad.ro/cv/FilipF/FGF-CV-en.pdf)

Prof. Constantin Bala Zamfirescu, “Lucian Blaga”, University, Sibiu, Romania, zbc@acm.org (https://web.ulbsibiu.ro/constantin.zamfirescu/)

Prof. Cristian Ciurea, Academy of Economic Studies (ASE), Bucharest, Romania, cristian.ciurea@ie.ase.ro (https://orcid.org/0000-0002-7327-0007)

In the context of the hyperconnected world, characterized by extensive digitalization, and the rather unpredictable developments in industries, societies, and natural environment, new AI methods and corresponding information tools and systems have been proposed and applied in the management of the present-day public and private settings. Since 2022, Generative AI (Gen AI) has taken the centre stage. Consequently, the multi-participant collaborative networks, and activities have gained ever more traction. At the same time, and the modern AI-based information tools have seriously impacted almost all sectors of the economic and social life. Data collection, consensus building, solution selection, network and crowd working-based decisions also involving collaborative/ social robots in decision-making activities have been ever more supported by modern AI-based tools possibly combined with MADM/MCDM algorithms in hybrid schemes as Herbert Simon forecast several decades ago. However, albeit notable results have been reported so far in practical applications in various industries, healthcare, finance and transportation, smart city, and culture sectors, and so on, several serious hindrances to a full extent usage of new generation of solutions cannot be overlooked. Consequently, efforts to devise adequate solutions with a view to overcoming the obstacles have been made. At the same time, the preoccupation for ethic aspects together with wellbeing, safety, and cultural diversity of the people involved, and environment quality preservation has been ever more noticeable in modern management and control settings. Consequently, the concept of digital humanism has been viewed as a framework to ensure that digitalization and AI development aligns with human values and needs, emphasizing ethics, transparency, and accountability with a view to influence the design and usage of the new generations of solutions.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning, but not limited to the following topics:
  • schemes including network, and crowd working methods and corresponding platforms;
  • Modern I&CT enablers for collaborative activities, such as: multi-agent cooperative schemes, data science and analytics, cloud, sky, and mobile computing, social networks, digital twins and co-simulation and so on;
  • Multi-attribute Decision Models (MADM) and multi-person Decision Support Systems (DSS) and platforms;
  • Cobots, digital clones of humans, and people with augmented intelligence and their collaboration in a mutualistic synergy manner.
  • AI-based tools including: custom GPT, EU's interoperable LLs, AI agentic flows, and service-oriented digital cognitive systems;
  • Possible hindrances of current AI tools (functional opacity, hallucinations, data leakage, restricted access to training data sets) and corresponding overcoming/mitigation solutions (explainable AI-XAI, human centered AI-HCAI, trust building, red buttons);
  • Practical recent applications in collaborative learning, management of smart/knowledge-based cities and sustainable villages, healthcare centers, cultural institutions and events, developments toward a Green and Digital Society;
  • Forecast societal impact of using AI in management and control applications
  • AI governance, friendly AI, and back casting approaches;
  • Digital enlightening, and digital humanism in the AI era.

Special Session 02: Innovations and Decision-making approaches for Financial and Economic sustainability at the age of Transformations

Prof. Fuad Aleskerov, Higher School of Economics, Moscow, Russia, fa201204@gmail.com

Prof. Alexander Karminsky, Higher School of Economics, Moscow, Russia, karminsky@mail.ru

Prof. Mikhail Stolbov, MGIMO-U, Moscow, Russia, stolbov_mi@mail.ru

Today's economy requires the integration of technology and analytics for effective financial decisions. Big data helps to create models that support sustainable growth and take into account ESG principles. The session will explore innovations for financial sustainability, including decision support, risk assessment and predictive analytics. Particular attention will be given to digital assets and their impact on banks, as well as the development of rating systems that take into account ESG criteria for more accurate analysis of the sustainability of companies. The session will be organized at hybrid format.
We are waiting for proposals on issues such as:
  • Creating models to decision making for financial and economic sustainability
  • Building big data information systems in banking and financial markets
  • Models based on machine learning for financial and risk forecasting
  • Digital assets and currency. Innovations in banking and digitalization
  • Formation of a rating system in business and finance
  • ESG ratings and their modeling
  • Efficiency models for comparing the usefulness of banks and financial companies
  • Evolution of ecosystems: prospects for emerging markets
  • Banking and financial innovations on FinTech platforms
  • Digitalization of the financial sector and banks

Special Session 03: The 13th Intelligent Decision Making and Extenics based Application

Prof. Xingsen Li, Guangdong University of Technology, China, lixingsen@126.com

Prof. Tao Wang, Beijing Institute of Technology, China, wangtao1020@126.com

Dr. Long Tang, Nanjing University of Information Science and Technology, China, tanglong@gdut.edu.cn

Dr. Chang Gao, Guangdong University of Technology, China, gcift@foxnail.com

Dr. Yiqing Yan, Guangdong University of Technology, China, yanlolo2012@gmail.com

With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important direction of scientific decision-making. Extenics is a new inter-discipline of mathematics, information, philosophy, and engineering including Extension theory, extension innovation methods and extension engineering. It builds the theory and methods of solving contradictory problems using formalized models to explore the possibility of extension and transformation of things and solve problems intelligently. The intelligent methods aim to provide targeted decision-making on the transformation of the practice which is facing the challenges of data explosion. Artificial intelligence and intelligent systems beyond big data offer efficient mechanisms that can significantly improve the quality of decision-making. Through ITQM, participants can further discuss the state-of-art technology in the Intelligent Decision Making and Extenics based application as well as the problems or issues occurred during their research.
The topics and areas include, but not limited to:
  • Extenics based Information methods and technology
  • Intelligent knowledge management based on Extenics
  • Intelligent Information management and Problem Solving on Extenics
  • Knowledge Mining on E-business
  • Intelligent Systems and its Applications based on Extenics
  • Web Marketing and CRM taking Extenics as methodology
  • Intelligent Data Analysis and Financial Management
  • Intelligent technology and Tourism Management
  • Innovation theory and Extenics based Methods
  • Extenics based Decision Making
  • Extension data mining and its Applications
  • Web Intelligence and Innovation on big data
  • Knowledge based Systems and decision-making theory on Extenics
  • Extenics based design technology and applications
  • Intelligent Logistics Management and Web of Things taking Extenics as methodology
  • Extenics Education and its technology

Special Session 04: Digital Education and Innovation/E-learning

Prof. Xiaodan Yu, University of International Business and Economics, China, xyu@uibe.edu.cn

Prof. Juanqiong Gou, Beijing Jiaotong University, China, jqgou@bjfu.edu.cn

Prof. Alanah Mitchell, Drake University, USA, alanah.mitchell@drake.edu

Digital technologies have been leveraged in education to create engaging, inclusive, and effective learning environments across various contexts, including K-12, higher education, and lifelong learning. The Digital Education and Innovation/E-Learning track explores the transformative potential of digital technologies in education and focuses on innovative approaches to teaching and learning in the digital intelligence age. This track provides a platform for scholars, researchers, and practitioners to share their insights, experiences, and research findings related to the effective integration of technology in education, the development of digital learning environments, the creation of personalized learning experiences, and the development of innovative pedagogical strategies. Track submissions may emphasize the behavioral aspects of e-learning, technology design, best practices of introducing AI into teaching and learning environments to achieve augmented intelligence between humans and AI.
Submissions may focus on, but are not limited to, the following topics:
  • Behavioral aspects of e-learning and learner engagement
  • User experience (UX) and user interface (UI) design in e-learning platforms
  • Best practices in e-learning course development and delivery
  • Application of artificial intelligence (AI) in e-learning, including adaptive learning, intelligent tutoring systems, and personalized learning
  • Learning analytics and data-driven decision-making in education
  • Gamification and game-based learning
  • Mobile learning and responsive e-learning design
  • Accessibility and inclusivity in e-learning
  • Learning design and instructional strategies for online and blended learning
  • Collaborative and social learning in digital environments
  • Assessment and evaluation in e-learning, including digital badges and micro-credentials
  • Learning management systems (LMS) and virtual learning environments (VLE)
  • Open educational resources (OER) and their impact on e-learning
  • E-learning in corporate training and professional development
  • Quality assurance and best practices in online education
  • Ethical considerations in e-learning, including data privacy and security
  • Cultural and cross-cultural perspectives in e-learning design and delivery
  • Emerging technologies in e-learning, such as virtual reality (VR), augmented reality (AR) and blockchain
  • Case studies and success stories of e-learning implementations in various contexts, including K-12, higher education, lifelong learning, and corporate training

Special Session 05: Intersection between Information Technology and Quantitative Management

Dr. Qihang Hu, School of Economics, Peking University, China, huqihang@pku.edu.cn

Dr. Yi Qu, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China, quyi17@mails.ucas.ac.cn

Recent advancements in information technology, particularly the cutting-edge artificial intelligence (AI) tools like large language models (LLMs), have significantly impacted the research paradigms of management science. Leveraging the powerful capabilities to analyze and process unstructured data including texts, images, and videos, these advanced IT techniques have enabled numerous tasks to be performed with greater efficiency and effectiveness. This Special Session titled “Intersection of Information Technology and Quantitative Management” is a multidisciplinary forum for researchers and practitioners, aiming to discuss and explore the integration of cutting-edge IT advancements with quantitative management methodologies. Participants will have the opportunity to publish their research findings, and ensure that innovative works in this domain have global audience.

This workshop will focus on a range of interdisciplinary topics, including but not limited to:
  • Big Data Mining Analyzing Tools and Theories
  • Quantitative Management Tools and Theories
  • IT-enabled Decision Support System
  • Optimization Techniques in Data Mining
  • Business Intelligence and Analytics
  • Machine Learning and Deep Learning Tools with Applications
  • Human-Machine Collaborative Decision Making

Special Session 06: Human–Machine Decision-Making with Generative AI

Prof. Xianneng Li, Dalian University of Technology, China, xianneng@dlut.edu.cn

Asso. Prof. Zhaoguang Xu, Dalian University of Technology, China, zhgxu@dlut.edu.cn

Asst. Prof. Deqiang Hu, Dalian University of Technology, China deqianghu@dlut.edu.cn

Prof. Yanzhong Dang, Dalian University of Technology, China yzhdang@dlut.edu.cn

Prof. Yong Shi, Chinese Academy of Sciences, China, yshi@ucas.ac.cn

The rapid advancement of Generative AI (GAI) is reshaping how organizations make decisions and manage complex systems in the digital business era. As AI technologies become increasingly embedded in business processes, decision-making across industries such as manufacturing, healthcare, finance, logistics, and digital platforms is evolving from tool-assisted analytics toward deeper forms of human-AI collaboration. By integrating GAI such as Large Language Models (LLMs) into business and management decision environments, organizations can enhance decision quality, improve operational efficiency, and support more adaptive and responsive management strategies. This special session aims to provide a multidisciplinary forum for researchers and practitioners to discuss recent advances, practical applications, and emerging challenges of human-machine decision-making with GAI.
This session will cover but not limited to the following topics:
  • Generative AI for Real-Time Decision-Making: Exploring the role of GAI in optimizing decision-making in various sectors such as production, healthcare, finance, and logistics.
  • Human-in-the-Loop (HITL) for Complex Decision-Making: Examining the role of HITL models to enhance decision-making in high-stakes environments such as healthcare and operations management.
  • Transparency and Explainability in AI-Driven Decision-Making: Exploring the importance of explainable AI (XAI) in ensuring that decisions made by LLMs and GAI systems are understandable and justifiable to human stakeholders.
  • Case Studies of AI-Driven Decision-Making Across Industries: Analyzing real-world applications and outcomes of LLMs and GAI in sectors like manufacturing, finance, healthcare, and beyond.
  • Other topics related to human-machine systems in decision-making: Exploring emerging trends and innovations in human-machine collaboration, with applications across various industries.

Special Session 07: Harmonizing AI, Green Transition, and Sustainable Business Models

Prof. Xiaobo Xu, Xi'an Jiaotong-Liverpool University, China, Xiaobo.Xu@xjtlu.edu.cn

The dual challenges of digital transformation and green transition are reshaping global business landscapes. This session aims to explore how Artificial Intelligence (AI) can be synergistically integrated with sustainable business practices and harmony-driven (HeXie) management frameworks to foster resilient, equitable, and ecologically responsible enterprises.
Topics of interest include, but are not limited to:
  • AI-driven decision-making for green supply chains and circular economy
  • Quantitative models for harmonizing economic, social, and environmental goals
  • Digital tools for sustainable innovation and corporate governance
  • AI-enhanced energy efficiency and carbon footprint optimization in operations
  • Behavioral insights and AI for promoting sustainable consumer practices
  • Green fintech and AI applications in sustainable investment and ESG analytics
  • Resilience and risk management in sustainable systems using predictive AI
  • Policy informatics and AI support for green regulatory compliance and reporting
  • Ethical AI and inclusive growth in the digital-green era
  • Case studies on cross-sector collaboration for sustainable development

Special Session 08: Intelligent Decision Making and Consensus

Prof. Francisco Javier Cabrerizo, University of Granada, Spain, cabrerizo@decsai.ugr.es

Prof. Juan Antonio Morente-Molinera, University of Granada, Spain, jamoren@decsai.ugr.es

Prof. Ignacio Javier Pérez, University of Granada, Spain, ijperez@decsai.ugr.es

Prof. Enrique Herrera-Viedma, University of Granada, Spain, viedma@decsai.ugr.es

Intelligent decision-making processes are developed by automatic decision-making systems that support individual and organizational decision-making processes using different information technologies (as web and social networks) and artificial intelligence tools. The intelligent decision-making processes involve the use of preference modelling and consensus processes. The preference modelling deals with the representation and modelled of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision-making. On the other hand, consensus is an important area of research in intelligent decision-making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences.
The objective of the proposed session is to highlight the ongoing research on intelligent decision-making, fuzzy preference modelling and consensus processes under uncertainty. Focusing on theoretical issues and applications on various domains, such as global green digital economy, ideas on how to solve consensus processes in intelligent decision-making under fuzzy preference modelling, both in research and development and industrial applications, are welcome. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Topics appropriate for this special session include, but are not limited to:
  • Fuzzy preference modelling in intelligent decision-making.
  • Intelligent decision-making system applications.
  • Consensus in fuzzy multi-agent decision-making.
  • New models of fuzzy preference modelling.
  • Intelligent decision-making systems for big data.
  • Intelligent decision-making in web.
  • Intelligent decision-making in global green digital economy.
  • Aggregation of preferences.
  • Intelligent decision-making in dynamic environments.

Special Session 09: High Performance Data Analytics for Diverse Applications

Prof. Vassil Alexandrov, Professor in Computational Science and Chief Science Officer at Hartree Centre, UK, vassil.alexandrov@stfc.ac.uk

Prof. Ying Liu, University of Chinese Academy of Sciences, China, yingliu@ucas.ac.cn

With the latest advances in HPC, AI and Emerging Computing paradigms the ability to analyse and deal with Big Data and computing at scale became a really active research area in the past few years. Modern applications require the analysis of huge amounts of unstructured and structured complex data, historic data as well as data coming from real time feeds (e.g. Business data, meteorological ones from sensors, remote sensing data, sociological and economics data etc.). These applications present significant challenges to the capability of traditional data processing techniques and tools especially when considered at scale. Therefore, heterogeneous approaches that often need to apply a combination of HPC, AI and Quantum Computing methods and other emerging computing paradigms to address these challenges. The challenges include data capture, storage, search, sharing, transfer, analysis, and visualization as well as applying the above heterogeneous approaches to diverse applications. These challenges often require the combination of computational science, AI and high performance computing and quantum computing methods and algorithms. Scalable mathematical methods and algorithms, parallel and distributed algorithms, cloud computing, etc. are some of the methodologies that are developed to efficiently tackle these complex big data and compute intensive problems. This session will focus on the issues of high performance data analysis in the aforementioned heterogeneous setting. Theoretical advances, mathematical methods, algorithms and systems, as well as diverse application areas will be in the focus of the special session.

This year the session aims at organizing a special theme session exploring emerging trends in high performance data analysis. We welcome papers on all aspects of high performance data analysis, including, but not limited to:
  • Advanced AI and Deep Learning methods and algorithms for diverse applications
  • Data processing exploiting hybrid architectures and accelerators (multi/many-core, CUDA-enabled GPUs, FPGAs)
  • Data processing exploiting dedicated HPC machines and clusters
  • Data processing exploiting cloud
  • High performance data-stream mining and management
  • Efficient, scalable, parallel/distributed data mining methods and algorithms for diverse applications
  • Quantum methods and algorithms for diverse applications.
  • Advanced methods and algorithms for big data visualization
  • Parallel and distributed KDD frameworks and systems
  • Theoretical foundations and mathematical methods for mining data streams in parallel/distributed environments
  • Applications of parallel and distributed data mining in diverse application areas such as business, science, engineering, medicine, and other disciplines
Program Committee
  • Di Zhao, Computer Network and Information Center, Chinese Academy of Sciences, China
  • Xiaofei Zhou, Institute of Information Engineering, Chinese Academy of Sciences, China
  • Jun Xu, Remin University of China, China
  • Zhenyu Cui, Tsinghua University, China
  • Ying Liu, University of Chinese Academy of Sciences, China
  • Vassil Alexandrov, Hartree Centre-STFC, UK
  • Michalis Smyrnakis, Hartree Centre – STFC, UK
  • Aneta Karaivanova, IICT, Bulgarian Academy of Sciences, Bulgaria
  • Emanouil Atanasov, IICT, Bulgarian Academy of Sciences, Bulgaria

Special Session 10: Advanced Multi-Criteria Decision Making Methods for Intelligent and Sustainable Systems

Prof. Alessio Ishizaka, Neoma, France, Alessio.Ishizaka@neoma-bs.fr

Multi-Criteria Decision Making (MCDM) has become a fundamental framework for solving complex decision problems involving multiple, often conflicting criteria. With the rapid growth of artificial intelligence, big data analytics, and intelligent information systems, decision makers are increasingly required to evaluate alternatives under uncertainty, vagueness, and incomplete information. This special session aims to bring together researchers and practitioners working on innovative MCDM models, algorithms, and applications in intelligent decision support systems and sustainable management. The session will provide a platform to discuss theoretical developments, computational techniques, and real-world applications across engineering, management, economics, and data science.

The main objectives of this special session are:
  • To present new theoretical developments in multi-criteria decision-making methods.
  • To explore uncertainty modeling approaches such as fuzzy, probabilistic, and hybrid frameworks in decision analysis.
  • To investigate integration of AI, machine learning, and big data with decision-making models.
  • To highlight real-world applications of MCDM in sustainability, healthcare, smart cities, finance, and risk analysis.
  • To encourage collaboration between researchers in information technology, operations research, and decision sciences.

Topics of Interest Potential topics include, but are not limited to:
  • Multi-Criteria Decision Making (MCDM) models and methods
  • Multi-Attribute Decision Making (MADM) techniques
  • Fuzzy decision-making approaches
  • Pairwise comparison methods
  • Group decision making and consensus models