The International Conference on Recent Advances in Information Systems (ICRAIS) places Information Systems at the core of its mission, highlighting their transformative role in addressing today’s complex global challenges. A central focus of the conference is how Information Systems research and innovation contribute to sustainability in its broadest sense — encompassing environmental responsibility, social equity, economic resilience, and ethical governance. We particularly welcome contributions that explore how Artificial Intelligence, data analytics, digital infrastructures, and education technologies within Information Systems can drive sustainable development and responsible digital transformation. By advancing sustainable Information Systems, ICRAIS aims to foster solutions that create long-term value for society, industry, and the planet.
The conference topics are:
- Cybersecurity, Data Privacy and Protection
- Cloud Computing
- Blockchain and Distributed Ledger Technologies
- Internet of Things (IoT)
- Quantum Computing
- Human-Computer Interaction
- Sustainable IT Practices
- Digital Transformation and Innovation
- AI in Education
- ICT4D & ICT
- Ethics in Information Systems
- Sustainable Development
Special Track
AI & Machine Learning
This track focuses on the latest developments and applications in Computer Vision, Natural Language Processing, and Generative AI. We welcome contributions presenting both fundamental research and practical implementations across various domains.
Focus Areas
- Computer Vision: From image processing to object detection, instance segmentation or multiple object tracking
- Natural Language Processing: Text analysis, language processing, and multimodal systems
- Generative AI: Text-to-image, text-to-text, and multimodal generative models
While these are our primary focus areas, submissions are not required to strictly align with any specific theme mentioned above. We encourage innovative approaches that may span multiple areas or introduce novel perspectives.
Application Domains
The track covers various fields of application such as:
- Agriculture: Precision farming, harvest prediction, plant disease detection
- Education: Personalized learning, automated assessment systems, intelligent tutoring systems
- Industry: Quality control, predictive maintenance, process optimization
- Fundamental Research: Novel architectures, optimization methods, modeling approaches
Methodological Focus
We particularly welcome contributions addressing cross-cutting methodological approaches such as:
- Federated Learning and Distributed Training
- Prompt Engineering and LLM Optimization
- Multi-task and Transfer Learning
- Explainable AI and Interpretability
- Green AI and Resource Efficiency
This track is aimed at researchers, developers, and practitioners who wish to share their insights and experiences from both academic research and industrial applications. We encourage exchange between different domains and welcome presentations of cross-domain solution approaches.