Aims and scope
Advanced AI Methods and Applications (AAIMA) is a broad-based, peer-reviewed journal that publishes original research on artificial intelligence methods, technologies, and applications across a wide range of scientific, engineering, medical, social, behavioral, economic, educational, managerial, and humanistic disciplines. The journal provides an interdisciplinary platform for research in which artificial intelligence contributes substantially to methodological development, scientific analysis, prediction, optimization, decision-making, automation, or the solution of real-world problems.
The Journal welcomes submissions from a wide range of topics including, but not limited to:
- Artificial Intelligence and Intelligent Systems
- Machine Learning and Deep Learning
- Neural Networks and Representation Learning
- Generative Artificial Intelligence
- Large Language Models and Foundation Models
- Multimodal Artificial Intelligence
- Reinforcement Learning
- Transfer Learning and Federated Learning
- Explainable and Interpretable Artificial Intelligence
- Trustworthy, Responsible, and Ethical AI
- Natural Language Processing and Computational Linguistics
- Computer Vision and Image Processing
- Pattern Recognition
- Speech and Signal Processing
- Robotics and Autonomous Systems
- Intelligent Agents and Multi-Agent Systems
- Knowledge Representation and Knowledge Graphs
- Expert Systems and Decision Support Systems
- Data Mining and Predictive Analytics
- Time-Series Forecasting and Spatio-Temporal Modeling
- Recommender Systems and Intelligent Information Retrieval
- Computational Intelligence and Soft Computing
- Fuzzy Systems and Fuzzy Logic
- Evolutionary Computation and Swarm Intelligence
- Optimization and AI-Based Optimization
- AI for Cybersecurity and Privacy
- Adversarial Machine Learning and Secure AI
- AI for Internet of Things (IoT) and AIoT
- Edge AI, Cloud AI, and Distributed AI
- Cyber-Physical Systems and Digital Twins
- High-Performance and Parallel AI Computing
- Quantum Artificial Intelligence and Quantum Machine Learning
- AI for Software Engineering and Intelligent Computing
- AI for Manufacturing and Industry 4.0
- AI for Mechanical and Industrial Engineering
- AI for Electrical, Electronics, and Communication Engineering
- AI for Civil, Structural, and Environmental Engineering
- AI for Chemical Engineering and Process Systems
- AI for Materials Science and Engineering
- AI for Aerospace and Automotive Engineering
- AI for Biomedical Engineering
- AI for Medicine and Healthcare
- AI for Medical Imaging and Biomedical Signal Processing
- AI for Clinical Decision Support and Digital Health
- AI for Bioinformatics, Computational Biology, and Biotechnology
- AI for Neuroscience and Brain-Computer Interfaces
- AI for Chemistry and Computational Chemistry
- AI for Physics and Scientific Computing
- AI for Mathematics and Statistical Modeling
- AI for Energy Systems and Renewable Energy
- AI for Agriculture and Food Systems
- AI for Environmental Science and Sustainability
- AI for Smart Cities and Intelligent Infrastructure
- AI for Transportation and Intelligent Mobility
- AI for Management and Business Administration
- AI for Marketing and Consumer Analytics
- AI for Finance, Economics, and Business Analytics
- AI for Accounting, Risk Management, and Decision Sciences
- AI for Supply Chain and Logistics
- AI for Operations and Project Management
- AI for Human Resource Management and Organizational Studies
- AI for Entrepreneurship, Innovation, and Technology Management
- AI for Education and Learning Technologies
- AI for Personalized Learning and Educational Assessment
- AI for Psychology and Behavioral Sciences
- AI for Sociology and Computational Social Science
- AI for Communication, Media, and Social Network Analysis
- AI for Humanities and Digital Humanities
- AI for Law and Legal Analytics
- AI for Public Policy, Governance, and Public Administration
- Human-AI Interaction and Human-AI Collaboration
- AI-Assisted Scientific Discovery and Research
- AI-Based Modeling, Simulation, and Decision Intelligence
- Interdisciplinary and Cross-Domain AI Applications
Advanced AI Methods and Applications (AAIMA) publishes Original Research Articles that make a substantive contribution to artificial intelligence methodology or its application within a specific scientific or professional domain. The journal particularly welcomes interdisciplinary research that integrates AI with established theories, methods, technologies, or practical problems from different fields. All manuscripts are evaluated on the basis of originality, scientific quality, methodological rigor, significance, and relevance to the journal's scope. Submissions are screened for research and publication ethics prior to peer review.
Research in which artificial intelligence plays a meaningful role in the development of methods, analysis, prediction, optimization, decision-making, or application is welcomed, regardless of the disciplinary field in which the research is conducted.
