Conditions
Organisers of Special Sessions are responsible for:
- Select a topic of interest to conference delegates.
- Obtain papers on this topic, normally at least 5 for an invited special session, but often more. At least 60% of the papers must be by authors who are neither session chairs from their team nor reviewers for the session.
- If there are short papers, the final accepted papers will be moved to the general track.
- Manage the review process for these papers on time and meet deadlines.
- Provide suitable reviewers for the reviews of the papers.
- Ensure the final versions of the papers are uploaded before the deadline.
- Attend the conference and chair the session.
- Provide a list of international reviewers (name, affiliation, country) who have already accepted to review the papers.
- Disseminate a call for papers for the special session widely.
Special Session 1: Efficiency and Explainability in Machine Learning and Soft Computing
Organizers:
- María M. Martínez Ballesteros. University of Seville, Spain.
- Manuel Jesús Jiménez Navarro. University of Seville, Spain.
- José Enrique Sánchez López. University of Seville, Spain.
- Pablo Reina Jiménez. University of Seville, Spain.
- Ana Rodríguez López. University of Seville, Spain.
- Ángela R. Troncoso García. Pablo de Olavide University, Spain.
Scope:
Explainable Artificial Intelligence (AI) is a current focus in AI research, enabling humans to understand and trust the decision-making of this technology. Although traditional machine learning models have been considered black boxes, new techniques have been developed to extract knowledge from them locally and globally. However, AI has a significant carbon footprint, which is where Green AI comes in. Green AI uses algorithms that promote inclusivity and environmental friendliness, and the key is to balance the amount of data, time, and iterations necessary to train a model. Considering its ecological impact, it is crucial to consider the energy cost and carbon footprint from the beginning and decide how critical it is to create or improve a model. This session aims to address two main topics. Firstly, to propose new methodologies or apply existing explainable/interpretable AI techniques to Machine Learning and Soft Computing models. Secondly, to improve the efficiency of Machine Learning and Soft Computing models without compromising their effectiveness. In addition, the special session will cover assorted topics related to industrial and environmental applications, such as, but not limited to:
- Applications of existing explainable AI methods.
- Efficient or explainable methods for black box models.
- Preprocessing techniques for efficiency improvement and/or explainability support.
- Novel Global and Local model-agnostic methods.
- Novel example-based explanations.
- Hardware/software design for energy-efficient models/explanations.
- Create human-friendly explanations and/or tools for non-technical users.
- Other relevant topics and applications related to efficiency and explainability in Machine learning and Soft Computing models, such as but not limited to: Blackbox models, Parallel computing, Federal learning, Decision support systems, Social impact, Health, Risk factors, Artificial vision, Natural language processing, Time series, Tabular data
Special Session 2: Time Series Forecasting in Industrial and Environmental Applications
Organizers:
- José F. Torres – Pablo de Olavide University of Seville, Spain.
- Federico Divina – Pablo de Olavide University of Seville, Spain.
- Mario Giacobini – University of Torino, Italy.
- Julio César Mello Román – Universidad Nacional de Asunción, Facultad Politécnica, Paraguay.
- Miguel García Torres – Pablo de Olavide University of Seville, Spain.
- Andrés Manuel Chacón Maldonado. Pablo de Olavide University of Seville, Spain.
- Adrián Gil Gamboa. Pablo de Olavide University of Seville, Spain.
Scope:
Time series can be found in almost all disciplines nowadays. Thus, time series forecasting is becoming a consolidated discipline that provides meaningful information in a wide variety of application areas, turning their efficient analysis into the utmost relevance for the scientific community. This session pays attention to the extraction of useful knowledge from time series in the context of industrial and environmental applications. Given its relevance in the emergent context of big data, the analysis of very large time series is also encouraged. Topics of interest for the special session, always in the context of industrial and environmental applications, include but are not limited to:
- Machine learning applied to time series forecasting.
- Deep learning applied to time series forecasting.
- New approaches for big data time series forecasting.
- Hybrid systems for time series analysis.
- Ensemble approaches for time series analysis.
Special Session 3: Quantum Computing
Organizers:
- Francesc Rodríguez-Díaz, Pablo de Olavide University, Spain.
- David Gutiérrez-Avilés. University of Seville, Spain.
- Daniel Martín Pérez. Pablo de Olavide University, Spain.
- Richard Jiang. Lancaster University, United Kingdom.
- Alicia Troncoso. Pablo de Olavide University, Spain.
- Wojciech Bożejko, Wroclaw University of Science and Technology, Poland.
- Francisco Martínez-Álvarez. Pablo de Olavide University, Spain.
Scope:
The rapid advancement of quantum technologies has sparked significant interest and innovation in the field of quantum computing, promising groundbreaking applications across various domains. This special session aims to bring together researchers, practitioners, and enthusiasts from academia and industry to discuss the latest developments, challenges, and opportunities in quantum computing. We invite researchers and practitioners to submit high-quality papers presenting original research, case studies, and innovative applications related to quantum computing.
Topics of interest for this special session include (but are not limited to):
- Quantum algorithms and applications
- Quantum programming languages and software tools
- Quantum simulation and optimization
- Hybrid quantum-classical computing
- Quantum machine learning and optimization
- Quantum networking and communication protocols
- Ethical and societal implications of quantum computing
Special Session 4: Soft Computing and Intelligent Methods in Manufacturing and Management Systems
Organisers:
- Damian Krenczyk. Silesian University of Technology, Poland.
- Anna Burduk. Wroclaw University of Science and Technology, Poland.
- Bożena Skołud. Silesian University of Technology, Poland.
- Marek Placzek. Silesian University of Technology, Poland.
Scope:
Management of manufacturing systems involves the development of advanced solutions supporting complex decision-making and problem-solving processes. Decisions are taken in areas such as process organization, planning, and control of manufacturing systems. These challenges are increasingly influenced by growing system complexity, increased interconnectivity, and progressing digitalisation, which require rapid and adaptive decision-making. Special attention is paid to complex and approximate solutions for problems for which no exact polynomial-time algorithms are known. In this context, soft computing and artificial intelligence methods play a crucial role in dealing with uncertainty, system dynamics, and multi-criteria decision problems in digitally supported manufacturing systems. Research into production management methods is driven by the need to increase the autonomy and flexibility of production systems.
These needs are addressed by production paradigms based on cyber-physical systems within the Industry 4.0 concept, supported by data-driven models and digital representations of production systems, enabling analysis, prediction, and optimisation of manufacturing and management processes. These digital tools also influence engineering education and competence development, requiring new approaches to academic and professional training that bridge research, education, and industrial practice. The aim of this special session is to present research results related to production system management, with emphasis on soft computing and intelligent methods as adequate approaches to complex manufacturing and management problems. Topics:
- Optimization of Manufacturing Systems
- Modelling, Simulation and Digital Representation of Production Systems
- Control and Supervision
- Industry 4.0
- Intelligent Production Planning and Scheduling
- Virtual Organisation
- Data Analytics and Knowledge Discovery in Manufacturing Systems
- Production System Organization
- Production Management
- Discrete Optimization
- Parallel Algorithms
- Artificial Intelligence and Soft Computing Methods
- Innovative Methods for Engineering Education in Manufacturing and Management Systems
- Digital and Simulation-Based Learning Environments for Manufacturing Education and Production Management
Special Session 5: Intelligent Techniques Applied to Modelling and Control in Engineering focused on marine energy systems and robotics
Organizers:
- J. Enrique Sierra García. University of Burgos, Burgos, Spain.
- Matilde Santos Peñas. Complutense University of Madrid, Spain.
- Fares M’zoughi. British Columbia, Canada.
- Payam Aboutalebi. University Complutense of Madrid, Spain.
- Eduardo Muñoz-Palomeque. UNED.
- Zennir Youcef. Automatic laboratory of Skikda, Skidda, Algeria.
- Antonio Herculano de Jesus Moreira. Polytechnic Institute of Cávado and Ave, Portugal.
Scope:
This special session focuses on the application of soft computing and intelligent methods to the modeling, identification, and control of engineering systems, with particular emphasis on marine renewable energy and robotics as representative domains. These fields pose demanding challenges related to dynamic environments, operational variability, and performance optimization, where intelligent techniques have demonstrated significant practical impact. The session aims to gather contributions presenting novel soft computing models, intelligent control strategies, learning and optimization methods, and real-world applications, highlighting methodological advances and engineering results rather than system-level hybridization architectures.
Session topics include, but are not limited to, the following strategies and approaches applied to modelling and control of marine energy and robotic systems:
- Intelligent control: fuzzy control, neuro-control, neuro-fuzzy, intelligent-PID control, …
- Learning systems: reinforcement learning, machine learning, and deep learning applications applied for modelling and control
- Optimization by heuristic techniques of control strategies
- Modelling and identification by Soft Computing techniques
- Hybrid models and hybrid control of complex systems
Special Session 6: Machine Learning and Computer Vision in Industry 4.0
Organizers:
- Enrique Dominguez. University of Malaga, Spain.
- Jose Garcia Rodriguez. University of Alicante, Spain.
- Ramon Moreno Jiménez. Grupo Antolin, Spain.
Scope:
In the coming years, the use of machine learning and computer vision in industry will become a trend that affects not only large corporations but also small and medium-sized businesses. Thanks to these technologies, innovation in the industrial sector is giving rise to “smart factories,” which allow them to obtain multiple advantages.
This special session tries to provide a common platform for academics, developers, and industry-related researchers to discuss, share experiences, and explore new technological advances. The objective is to integrate an international scientific community working on industrial applications of machine learning and computer vision for fruitful discussions and ideas on the evolution of these technologies. Topics:
- Computational intelligence
- Machine learning
- Deep learning
- Self-organization and self-adaptation
- Computer vision
- Video and image processing
- Biometric features extraction
- Pattern recognition
- Surveillance systems
- Hardware implementations
- Smart Manufacturing
- Autonomous vehicles/machines
- Quality control
- Demand prediction
- Data visualization
Special Session 7: Optimization, Modeling and Control by Soft Computing Techniques
Organizers:
- Eloy Irigoyen . University of the Basque Country, Spain.
- Matilde Santos. Complutense University of Madrid, Spain.
- Javier Sanchis. Universitat Politècnica de València, Spain.
- Mikel Larrea Sukia. University of the Basque Country, Spain.
- Borja Fernández Adiego. European Organization for Nuclear Research (CERN).
Scope:
This Special Session aims to bring together researchers and practitioners to present and discuss recent theoretical developments and real-world applications of soft computing methodologies for industrial and environmental domains, with emphasis on deployable solutions. Contributions are encouraged on learning- and model-based approaches, including hybrid and physics-guided modeling, data-driven and robust control, and intelligent optimization strategies. Emerging frameworks such as digital twins for monitoring, prediction, optimization, and control, as well as reinforcement learning and Bayesian optimization for complex decision spaces, are especially welcome when supported by industrially relevant validation.
The session also promotes trustworthy and resilient intelligent systems, including explainable AI methods for fault detection/diagnosis and decision support, and approaches that explicitly address uncertainty, safety constraints, and reliability in industrial operation. Overall, this SS provides a forum to exchange ideas and results that advance the state of the art in soft computing-based optimization, modeling, and control, aligned with Industry 4.0/5.0 needs and sustainability-driven applications. Suggested topics:
- Energy efficiency, decarbonization, and emission-aware optimization.
- Multi-objective and constrained optimization in industrial/environmental systems.
- Advanced control: data-driven control, learning-based MPC, adaptive/robust control.
- Reinforcement learning and safe RL for process control and operations.
- Bayesian optimization and surrogate-assisted optimization for expensive/complex processes.
- Modeling of complex systems: hybrid models, grey-box identification, system-of-systems.
- Physics-informed learning (e.g., PINNs) and physics-guided ML for industrial processes.
- Digital twins for monitoring, prediction, predictive maintenance, and closed-loop optimization/control.
- Fault detection, diagnosis, prognostics, and predictive maintenance (including XAI).
- Industrial anomaly detection and condition monitoring using deep/soft computing methods.
- Explainable, trustworthy, and uncertainty-aware AI for industrial decision support.
- Resilience and robustness: fault-tolerant control and graceful degradation strategies.
- Edge/real-time AI, resource-aware deployment, and human-in-the-loop industrial intelligence.