About AIX2027

AIX2027 is an international scientific conference focused on Artificial Intelligence and its applications across computer science, mathematics, physics, chemistry, and bioinformatics. The conference provides a multidisciplinary platform for researchers, academics, scientists, and practitioners to present recent research, exchange ideas, and discuss emerging AI-based methods and applications.

The conference welcomes research addressing theoretical developments, computational methods, modeling and simulation, data analysis, machine learning, intelligent systems, and practical applications of artificial intelligence across the represented scientific disciplines.

Submission Guidelines

All papers must be original and must not be simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full Papers describing original research, novel methodologies, computational approaches, experimental results, or practical applications of artificial intelligence in the conference areas.
• Posters describing ongoing research, preliminary findings, innovative ideas, emerging applications, or focused scientific studies relevant to the conference themes.

All submissions should demonstrate a clear scientific contribution and relevance to the scope of AIX2027.

List of Topics

• 1. Artificial Intelligence and Computer Science

• Machine Learning and Deep Learning
• Generative AI and Natural Language Processing
• Computer Vision and Image Processing
• Cybersecurity and Intelligent Systems
• Data Analytics and Intelligent Computing

• 2. Artificial Intelligence and Mathematics

• Mathematical Modeling Using AI
• Optimization and Intelligent Algorithms
• Statistics and AI-Based Data Analysis
• Differential Equations and Predictive Modeling
• AI in Applied Mathematics and Scientific Computing

• 3. Artificial Intelligence and Physics

• Machine Learning for Physical Modeling and Simulation
• AI in Materials Physics and Energy Systems
• AI-Based Analysis of Physical Experiments and Data
• AI in Medical Physics and Imaging
• Intelligent Physical and Dynamical Systems

• 4. Artificial Intelligence and Chemistry

• AI for Chemical Compound Discovery and Design
• Machine Learning for Chemical Property Prediction
• Computational Modeling and Simulation in Chemistry
• AI in Analytical Chemistry and Materials Science
• AI in Drug Design and Chemical Reactions

• 5. Artificial Intelligence and Bioinformatics

• Machine Learning in Bioinformatics
• AI for Genomic and Biological Sequence Analysis
• AI-Based Drug Discovery and Design
• AI for Protein Analysis and Molecular Structures
• AI in Biomedical Informatics and Digital Health