This Special Session is organized within IEEE International Joint Conference on Neural Networks (IJCNN) as part of the 2026 IEEE World Congress on Computational Intelligence (WCCI 2026).

Abstract: This Special Session continues previous successful editions, focusing on the rapidly evolving field of multimodal deep learning and information fusion in neural networks. It explores the development of advanced architectures and hybrid mechanisms that integrate multiple data modalities, such as visual, textual, sensor, and contextual information. By combining these heterogeneous sources, multimodal neural models enhance context understanding and enable the creation of intelligent systems capable of solving complex real-world problems. Such approaches are particularly relevant to Internet of Things (IoT) environments, where diverse and dynamic data streams must be processed efficiently at the edge. The session highlights key challenges and breakthroughs in fusion strategies, graph and convolutional neural networks, hybrid intelligent systems, multicriteria decision problems, and federated learning.

Aims and scope: The purpose of this Special Session is to promote research and innovation in multimodal deep learning, data fusion, and hybrid intelligent processing. We invite contributions addressing both theoretical foundations and practical applications, especially those involving integration with cutting-edge architectures and IoT-based systems. Submissions that explore hybrid solutions combining neural networks with fuzzy logic, evolutionary computation, or other computational intelligence paradigms are particularly encouraged. Case studies demonstrating real-world implementations of multimodal and hybrid systems are also welcome. Topics of interest include, but are not limited to:

• Multimodal data processing and representation learning,
• Fusion and alignment strategies for heterogeneous data sources,
• Federated and distributed learning for multimodal and IoT systems,
• Explainability and interpretability of multimodal architectures,
• Self-supervised and unsupervised multimodal learning approaches,
• Attention mechanisms and transformers for multimodal fusion,
• Human–AI collaboration enabled by multimodal understanding,
• Applications in healthcare, autonomous systems, and Internet of Things.

Detailed submission instructions and system information will be provided at a later time.

Important Dates

• Paper submission deadline 31 January 2026: (23:59, anywhere on Earth, i.e. UTC-12)
• Notification: 15 March 2026
• Camera-ready: 15 April 2026
• Conference: 21–26 June 2026

Prof. Dawid Połap: (Member, IEEE) received the M.Sc. degree in applied mathematics (with honors) from the Silesian University of Technology, Gliwice, Poland, in 2017, and the Ph.D. degree in 2019 and the D.Sc. (habilitation) degree in 2025, both from the Czestochowa University of Technology, Czestochowa, Poland. He is a Professor and Head of the Department of Artificial Intelligence Modeling at the Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland. He has authored and co-authored more than 150 research papers in international journals and conferences. His main research interests include image processing, intelligent computing, and various aspects of machine learning. Prof. Połap was awarded by the Polish Ministry of Science and Higher Education with a Diamond Grant for the most talented students and a scholarship for exceptional young scientists. He serves as an Editor for Applied Soft Computing, Expert Systems with Applications, Sensors, Evolutionary Intelligence, and several other journals.

Prof. Stefania Tomasiello: Associate professor of computer science (SSD INF/01 in Italy) at the University of Salerno since October 2023. Responsible for the course Fuzzy Logic and Soft Computing (6 ECTS) at the University of Tartu. She was formerly an Associate Professor of Intelligent Systems at the University of Tartu (Institute of Computer Science, Chair of Data Science, Machine Learning Group) and previously, a permanent researcher with CO.RI.SA. (Research Consortium on Agent Systems), University of Salerno. Work-package leader in several funded projects and expert evaluator (ex-ante and ex-post) of applied research projects for the Italian Ministry of Economic Development and, formerly, the Italian Ministry of University and Research. She has been the principal investigator for the University of Tartu in the ERA-NET project “ConnectFarms”. She serves several indexed journals as an associate editor, such as IEEE Transactions on Neural Networks and Learning Systems, a premier journal in the field. She is Co-Editor in Chief of Evolutionary Intelligence (Springer, WoS, Scopus indexed). Senior IEEE member and senior INNS member. INNS Board of Governors 2025-2027. Currently supervising three PhD students. Italian scientific qualification (ASN) as full professor of computer science.

Prof. Gautam Srivastava: (Senior Member, IEEE) received his M.Sc. and Ph.D. in Computer Science from the University of Victoria, Canada, in 2006 and 2012, respectively. He is a Professor of Computer Science at Brandon University, Canada. In his 10-year academic career, he has published a total of 400 papers in high-impact conferences in many countries and high-status journals (SCI, SCIE). He is an Editor of several international scientific research journals, including IEEE Transactions on Industrial Informatics, IEEE Transactions on Computational Social Systems, and IEEE Internet of Things Journal.