Accepted Special Sessions

Conditions

Organizers 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 due time and 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: Machine Learning and Computer Vision in Industry 4.0 (MLCVI)

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

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Special Session 2: Time Series Forecasting in Industrial and Environmental Applications (TSF)

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: Applications of Multi-Agent Systems in AI and Industry (AMS-AII)

María M. Martínez Ballesteros – University of Seville, Spain.
Manuel Jesús Jiménez Navarro. University of Seville, Spain.
José Enrique Sánchez López. University of Seville, Spain.
David Gutiérrez Avilés. University of Seville, Spain.
Belén Vega Márquez. University of Seville, Spain.
Manuel Carranza García. University of Seville, Spain..

Scope:
Multi-Agent Systems (MAS) are at the forefront of Artificial Intelligence (AI), enabling robust, adaptive, and autonomous decision-making in dynamic and distributed environments. Recent advancements in deep reinforcement learning, distributed AI, explainability, and real-time decision-making have significantly expanded the scope of MAS applications in fields such as robotics, smart grids, autonomous systems, and industrial AI.
This special session aims to bring together researchers and practitioners to discuss the latest advancements, theoretical innovations, and practical applications of MAS. This session aims to explore novel algorithms, architectures, and real-world implementations that enhance the intelligence, cooperation, scalability, and ethical considerations of MAS in various domains.

We encourage submissions on (but not limited to) the following topics:

Theoretical Advances in MAS: Coordination, cooperation, negotiation, game theory, emergent behavior.
Reinforcement Learning for MAS: Multi-agent deep RL, reward shaping, policy learning, decentralized RL.
Scalability and Complexity: Distributed computing, swarm intelligence, hierarchical MAS architectures.
Trust, Fairness, and Ethics: Explainability, interpretability, privacy-preserving MAS, and fairness in decision-making.
Applications of MAS in AI: Smart cities, autonomous vehicles, cybersecurity, IoT, supply chain optimization, and healthcare.
Human-Agent Interaction: Hybrid AI-human collaboration, multi-agent human-in-the-loop systems.

Special Session 4: Efficiency and Explainability in Machine Learning and Soft Computing (EEML-SF)

María M. Martínez Ballesteros – University of Seville, Spain.
Manuel Jesús Jiménez Navarro. University of Seville, Spain.
José Enrique Sánchez López. University of Seville, Spain.
Javier Hiruelo Pérez. University of Seville, Spain.
Pablo Reina Jiménez. University of Seville, Spain.
Ángela R. Troncoso García. Pablo de Olavide University, Spain.ille, 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, and Tabular data.

Special Session 5: Soft Computing and Hard Computing for a Data Science Process Model (SCHC-PM), 2nd edition

Antonio J. Tallón-Ballesteros – University of Huelva, Spain.
Ireneusz Czarnowski – Gdynia Maritime University, Poland.

Scope:
Data science projects may be faced with a few different methodologies. Going back to the roots, KDD is the first approach, moving ahead newer names have been coined like CRISP-DM, DMME, etc. and more recently DASC-PM (Data Science Process Model). Data generation speed is continuously being increased, and their storage is an important matter while the data processing is done. Different terms have been mentioned starting from the initial byte crossing through gigabyte, terabyte, and so on; yottabyte I,s up to now the highest magnitude defined to store the information. A machine learning task is required to process the data, which may include different kinds of pre-processing. The support of soft computing and hard computing is crucial for any stage of the DASC-PM methodology. This special session welcomes works concerning any real-world application or part of it where soft computing or hard computing are tools to achieve the final prediction or the last product of the data provision phase. The scope may be concerning supervised, unsupervised or semi-supervised learning. The topics of interest for this thematic session comprise but are not limited to:

Data provision
Data preparation
Data management
Exploratory analysis
Analysis phase
Deployment phase
Utilization phase
Data integration
Data transformation
Data storage
Feature selection
Outlier detection
Outlier removal
Noise smoothing
Instance selection
Data rebalancing
Missing values imputation
Random projection
Informative projection
Data normalization
Text-based user interfaces to pre-process big data
Medical data analysis
Finance
Social sciences
Education
Science applications
Communication
Instrumentation
Electronic technology
Signal processing
Audio processing
Video processing
Automation
Networks of any type

Special Session 6: Intelligent Techniques Applied to Modelling and Control in Engineering focused on marine energy systems and robotics (MCE-ESR)

J. Enrique Sierra García – University of Burgos, Burgos, Spain.
Matilde Santos Peñas – Complutense University of Madrid, Spain.
Fares M’zoughi – University of Victoria, Canada.
Payam Aboutalebi – University Complutense of Madrid, Spain.
Bowen Zhou – Northeastern University, China.

Scope:
The use of intelligent techniques, or the hybridization of conventional approaches and those derived from artificial intelligence, and in particular those formed by soft computing, is very often the only solution for the modeling and control of complex systems in the field of engineering. This approach allows dividing the objectives of the problem and using different techniques to address each part, and achieving high-level objectives using specific techniques for different objectives. This special session seeks to analyze the potential of intelligent and hybrid techniques in the fields of marine renewable energy and robotics, as two examples of engineering systems that have achieved great development but present challenges in the modeling and design of control systems.
In the field of modeling, parametric techniques and other classical techniques provide simple and fast models, but whose accuracy may not be good enough. However, data-driven methods, which use deep learning, generate models that better fit the real behavior of the system, but can be very demanding in terms of computational requirements. Hybridization of techniques allows to exploit the best of both worlds by providing more accurate models with less computational complexity.
Something similar happens in the control field. Some tasks are performed by conventional controllers with great efficiency, which allows to achieve more ambitious and complex objectives using heuristic and intelligent techniques.
The objective of this special session is to provide a platform for researchers, engineers and industrial professionals from the fields of marine renewable energy and robotics to share and exchange their innovative ideas, research results and experiences in the hybridization of techniques applied to the modeling and control of these systems. Contributions to this special session are welcome to present and discuss novel methods, algorithms, control techniques, frameworks, architectures, platforms and applications.

Session topics include, but are not limited to, the following strategies and approaches applied to modelling and control of marine energy systems and robotics:

Intelligent control: fuzzy control, neuro-control, neuro-fuzzy, intelligent-PID control, …
Learning systems: reinforcement learning, machine learning, and deep learning applications applied on modelling and control
Optimization by heuristic techniques of system engineering and control
Modelling and identification by Soft Computing techniques
Hybrid models and hybrid control of complex systems

Special Session 7: Quantum Computing (QC)

Francesc Rodríguez-Díaz, Pablo de Olavide University, Spain.
David Gutiérrez-Avilés. University of Seville, Spain.
Sebastián Alberto Grillo. Universidad Autónoma de Asunción, Paraguay.
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

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