1. Title: AI-powered Vision and Language Models (AVLM)
Special Session Organizers:
- Dr. Manh-Hung Ha, Faculty of Applied Science, International School, Vietnam National University, Hanoi
- Prof. Oscal Tzyh-Chiang Chen, Department of Electrical Engineering, National Chung Cheng University, Taiwan and International School, Vietnam National University, Hanoi
- Assoc. Prof. Tran Thi Ngan, Faculty of Applied Science, International School, Vietnam National University, Hanoi
Objectives and topics:
We invite the researchers and practitioners to present and discuss advances in the research and development of topics on AI-powerd vision and language for real-world applications and simulations. As the field expands rapidly, discussions will also address new datasets, metrics for multimodal learning, pressing privacy concerns. AI has revolutionized the way we approach computer vision and natural language processing, driving breakthroughs in image recognition, object detection, and language understanding. As these technologies continue to mature, researchers and practitioners are increasingly seeking methods to integrate vision and language models into practical, real-world applications such as autonomous systems, smart healthcare to immersive multimedia, and interactive AI assistants. This special session AVLM aims to bring together experts, scholars, and industry professionals to share the latest advances, discuss emerging challenges, and explore future directions in the field. This special session also aims to unite leading researchers, practitioners, and industry experts to accelerate progress in deep learning for vision and language across real-world applications. By spotlighting state-of-the-art architectures, discussing the transition from theory to deployment, and fostering interdisciplinary collaboration, we seek to inspire innovative approaches and practical solutions. Additionally, the session emphasizes the importance of tackling challenges such as scalability, data diversity, interpretability, and ethical considerations, ultimately guiding future research and enabling robust, impactful AI systems. Through this collective effort, we strive to create a global network that drives transformative breakthroughs and catalyzes the widespread adoption of advanced vision-language models.
The AVLM is anticipated to emphasize data-centric approaches with a strong focus on vision and language model. This special session seeks to:
- Identify real-world contexts in which vision and language can offer significant benefits
- Promote dialogue and collaboration on vision-language solutions for practical applications
- Create a forum for the ICTA community to explore this exciting and rapidly expanding field of multimodal representations
We welcome paper submissions on all topics related to neural fields, including but not limited to:
- Vision and language for autonomous driving and robotics
- Neural networks and deep neuronal networks for image processing
- Language-driven perception
- Language-driven sensor and traffic simulation
- Vision and language representation learning
- New datasets and metrics for multimodal learning
- Multi-modal fusion for end-to-end application
- Large-Language-Models (LLMs) as task planner
- Other applications of LLMs to driving and robotics
- Assistive technologies based on image processing
- Image processing optimization
- Medical diagnosis systems based on complex image processing
- Image processing for real-time control
- AI-Powered Image & Video Processing
- Image Processing for Public Health & Safety
- Computational Imaging
Important dates
- Submission of papers: 30 June, 2025
- Notification of acceptance: 20 August, 2025
- Camera-ready papers: 30 August, 2025
- Registration & payment: 30 August, 2025
- Conference date: November 27-28, 2025
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2. Title: AI-Driven Decision Support Systems for Biomedical Applications
Special Session Organizers:
- Prof. Dr. Vassilis C. Gerogiannis, Department of Digital Systems, University of Thessaly, Larissa, Greece, vgerogian@uth.gr
- Prof. Dr. Andreas Kanavos, Department of Informatics, Ionian University, Corfu, Greece, akanavos@ionio.gr
- Assist. Prof. Dr. Biswaranjan Acharya, Department of Computer Engineering – Artificial Intelligence and Big Data Analytics, Marwadi University, Rajkot, Gujarat, India, biswaranjan.acharya@marwadieducation.edu.in
- Assist. Prof. Dr. Debabrata Swain, Computer Science and Engineering Department, Pandit Deendayal Energy University, Gandhinagar, India, Debabrata.Swain@sot.pdpu.ac.in
- Dr. Alaa Mohasseb, Senior Lecturer, School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK, alaa.mohasseb@port.ac.uk
Objectives and topics:
Biomedical data presents a unique set of challenges, including high dimensionality, class imbalance, small sample sizes, and uncertainty in decision-making. The increasing availability of multimodal biomedical data—ranging from numerical measurements and textual reports to medical images, signals, and genomic sequences—necessitates advanced AI-driven methodologies that can effectively process, integrate, and analyze such heterogeneous data sources.
This special session aims to explore cutting-edge AI methodologies, including fuzzy systems, machine learning, deep learning, and hybrid computational intelligence techniques, that address the inherent complexity, uncertainty, and variability of biomedical data. The session will also emphasize the need for interpretable and trustworthy AI in biomedical applications, ensuring that predictive models contribute not only to improved accuracy but also to enhanced transparency, fairness, and reliability in clinical decision-making.
We invite submissions on innovative AI-driven approaches to biomedical data analysis, including but not limited to:
- Fuzzy Logic and Uncertainty Management: Applications of fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, and other extensions in biomedical decision-making.
- Interpretable AI and Explainable Models: Methods for improving transparency and trust in AI-driven biomedical applications.
- Hybrid Decision Support Systems: Integration of fuzzy logic with machine learning, deep learning, or knowledge-based systems for robust medical predictions.
- Multimodal Data Fusion: Techniques for combining structured and unstructured biomedical data, including sensor data, imaging, text, and clinical records.
- Biomedical Signal and Image Processing: AI-based enhancement, segmentation, and classification for ultrasound, MRI, CT scans, and electrophysiological signals.
- AI for Personalized Medicine: Predictive models for patient-specific diagnosis, prognosis, and treatment optimization.
- AI-on-the-Edge for Healthcare: Deployment of AI models on low-power, real-time medical devices for remote monitoring and diagnostics.
- Trustworthy AI in Healthcare: Addressing issues of bias, fairness, and regulatory compliance in AI-driven biomedical applications.
This special session aims to:
- Provide a platform for researchers and practitioners to present state-of-the-art AI methodologies in biomedical applications.
- Foster discussions on how advanced AI, fuzzy logic, and hybrid intelligence can tackle key challenges in biomedical decision-making.
- Explore emerging trends in AI-on-the-Edge, federated learning, and uncertainty-aware models that enhance the efficiency, accuracy, and interpretability of biomedical AI solutions.
We invite researchers from academia and industry to contribute original research papers, case studies, and novel applications that push the boundaries of AI in biomedical sciences.
Important dates:
- Submission of papers: 30 June, 2025
- Notification of acceptance: 20 August, 2025
- Camera-ready papers: 30 August, 2025
- Registration & payment: 30 August, 2025
- Conference date: November 27-28, 2025
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3. Title: Big data and AI in life science (BDALS)
Special Session Organizers
- Associate Trien Minh Pham – Faculty of Agricultural Technology, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
- Cuong Nguyen – Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
- Ha Duc Chu – Faculty of Agricultural Technology, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
Objectives and topics:
| The special section titled “Big data and AI in life science” focuses on the integration of bioinformatics, big data analytics, and AI in agriculture and related life science fields. Its primary objective is to explore how advanced computational techniques are transforming agricultural research, production systems, and biological discovery. With the rapid increase in agricultural data, from genomics, phenomics, and climate sensors to remote sensing, AI and machine learning are becoming essential tools to process, interpret, and apply this information in practical and impactful ways. Key topics include the use of machine learning and bioinformatics in crop improvement, soil health monitoring, pest and disease prediction, precision farming, and sustainable resource management. The section also covers AI-driven models for predicting plant growth, yield optimization, and environmental stress response. Furthermore, contributions related to animal health and veterinary applications, especially those employing big data for disease diagnosis and treatment in livestock, are welcomed. The intersection of agriculture and medicine through nutrigenomics and functional foods is also of interest, particularly where AI is used to uncover links between diet, genetics, and health outcomes. By bringing together research that spans genomics, agri-biotechnology, digital agriculture, and data science, this section aims to foster interdisciplinary innovation and offer scalable, data-driven solutions to challenges in food security, sustainability, and agricultural health. |
The BDALS is anticipated to explore the integration of big data and artificial intelligence in life science research. It aims to:
We welcome paper submissions on all topics related to big data, AI, and life science research, including but not limited to:
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Important dates
- Submission of papers: 30 June, 2025
- Notification of acceptance: 20 August, 2025
- Camera-ready papers: 30 August, 2025
- Registration & payment: 30 August, 2025
- Conference date: November 27-28, 2025
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4. Title: Bioinformatics and Computational Biology (BCB)
Special Session Organizers
- Tin Nguyen, Associate Professor, Auburn University, USA, tinn@auburn.edu
- Dang Hung Tran, Associate Professor, School of Information and Communications Technology, Hanoi University of Industry, hungtd@haui.edu.vn
- Nam Sy Vo, Director, Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam, namvs@vinbigdata.org
Objectives and topics:
Together with the ability of generating vast amounts of data, advanced biotechnologies also brought the challenge of translating such data into a better understanding of biological phenomena. BCB2025 is continuing effort of gathering researchers, students, and industrial practitioners for exchange of ideas and research results and in all areas of bioinformatics and computational biology. Topics of interest include but are not limited to:
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Important dates:
- Submission of papers: 30 June, 2025
- Notification of acceptance: 20 August, 2025
- Camera-ready papers: 30 August, 2025
- Registration & payment: 30 August, 2025
- Conference date: November 27-28, 2025