Call for Papers
Artificial Intelligence (AI) systems are computational frameworks designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. These systems integrate algorithms, data, and computing power to enable machines to adapt and improve their performance over time. AI systems encompass a wide range of technologies, including machine learning, natural language processing, computer vision, and autonomous robotics, and they are increasingly applied in domains such as healthcare, finance, cybersecurity, transportation, and smart cities. As they continue to evolve, AI systems are not only transforming industries and enhancing human capabilities but also raising important considerations around ethics, transparency, and trustworthiness.
The International Conference on Artificial Intelligence Systems (AIS 2026) aims to bring together leading researchers, academics, and practitioners from around the world to share their latest findings, innovations, and applications in Artificial Intelligence and intelligent systems. AIS 2026 provides a premier interdisciplinary platform for presenting advances and exchanging insights on the theoretical foundations, cutting-edge technologies, and real-world implementations of AI. AIS 2026 will feature keynote talks, technical paper sessions, workshops, and tutorials covering a wide range of AI topics. We invite submissions of high-quality research papers describing original and unpublished results in all areas of Artificial Intelligence Systems. addresses the use of advanced intelligent systems in providing cybersecurity solutions in many fields, and the challenges, approaches, and future directions. We invite the submission of original papers on all topics related to Intelligent Systems with special interest in but not limited to:
- Machine Learning and Data-Driven Systems
- Deep Learning, Representation Learning, and Transfer Learning
- Reinforcement Learning and Adaptive Systems
- Federated, Distributed, and Edge AI
- Reinforcement Learning and Autonomous Agents
- Federated Learning and Distributed AI Systems
- AI Applications and Intelligent Systems
- Scalable and High-Performance AI Architectures
- Edge AI, Cloud AI, and Embedded Intelligence
- Optimization and Resource-Aware AI Systems
- Natural Language and Multimodal AI
- Natural Language Processing and Understanding
- Speech Recognition and Conversational Agents
- Multimodal Learning and Vision-Language Systems
- Knowledge Representation and Reasoning
- Data Mining, Knowledge Discovery, and Information Retrieval
- Automated Planning and Scheduling
- Generative AI, Foundation Models, and Large Language Models
- Generative AI and Large Language Models (LLMs)
- Explainable and Responsible AI
- Federated, Distributed, and Edge AI
- Reinforcement Learning and Autonomous Agents
- AI for Decision Support Systems
- Natural Language Processing and Computational Linguistics
- Multilingual and Low-Resource Language Models
- Human-Computer Interaction (HCI) and Assistive Technologies
- Conversational AI and Dialogue Systems
- Computational Language and Human-Centered Systems
- Prompt Engineering and Fine-tuning
- Multimodal LLMs
- Agentic AI
- Computer Vision, Image and Video Processing
- Image and Video Understanding
- Object Detection, Recognition, and Tracking
- Multimodal Learning and Vision-Language Systems
- 3D Vision and Scene Understanding
- Affective Computing and Emotion Recognition
- Artificial Intelligence Applications
- Robotics and Autonomous Systems
- AI for Healthcare, Smart Cities, and Transportation
- AI in Finance, Education, and Industry 4.0
- AI for Cybersecurity, Privacy, and Digital Trust
- AI Infrastructure and Engineering
- Recommender Systems and Personalization
- AI for Social Good and Humanitarian Applications
- Emerging Trends and Future Directions in AI Systems
- Artificial Intelligence Security
- Responsible and Trustworthy AI
- Explainable AI and Transparency
- Ethical and Societal Implications of AI
- AI Safety, Robustness, and Security
- AI Policy, Regulation, and Governance
Submission Guidelines:
Submission System:
Paper format
Submitted papers (.pdf format) must use the A4 IEEE Manuscript Templates for Conference Proceedings. Please remember to add Keywords to your submission.
Length
Submitted papers may be up to 8 pages with two additional pages may be added for references.
Originality
Papers submitted to AIS must be the original work of the authors. The may not be simultaneously under review elsewhere. Publications that have been peer-reviewed and have appeared at other conferences or workshops may not be submitted to AIS. Authors should be aware that IEEE has a strict policy with regard to plagiarism https://www.ieee.org/publications/rights/plagiarism/plagiarism-faq.html The authors' prior work must be cited appropriately.
Proofreading:
Please proofread your submission carefully. It is essential that the language use in the paper is clear and correct so that it is easily understandable. (Either US English or UK English spelling conventions are acceptable.)
IMPORTANT: No-Show Policy
At least one author of each accepted paper is required to register to the conference and present the paper. Only registered and presented papers will be published in the conference proceedings. A paper not presented or presented by a non-author without prior written approval by the TPC Chair will be removed from the final conference proceedings and no registration refund can be made to the authors of these papers.
Publication:
All papers that are accepted, registered, and presented in AIS 2026 and the workshops co-located with it will be submitted to IEEEXplore for publication.