Generally speaking, Artificial Intelligence (AI) refers to a broad set of methods, algorithms and technologies that make software 'smart' in a way that may seem human-like to an outside observer.

International Journal of Artificial Intelligence (IJAI) scope encompasses:

☛ Artificial Intelligence

• Brain models, Brain mapping, Cognitive science
• Expert System
• Deep Learning
• Fuzzy systems
• Computer Vision
• Cognitive Computing
• Evolutionary Computation
• Robotic Process Automation (RPA)
• Natural Language and Speech Processing
• Natural language processing
• Fuzzy logic and soft computing
• Software tools for AI
• Expert systems
• Decision support systems
• Automated problem solving
• Knowledge discovery
• Knowledge representation
• Knowledge acquisition
• Knowledge-intensive problem solving techniques
• Knowledge networks and management
• Intelligent information systems
• Intelligent data mining and farming
• Intelligent web-based business
• Intelligent agents
• Intelligent networks
• Intelligent databases
• Intelligent user interface
• AI and evolutionary algorithms
• Intelligent tutoring systems
• Reasoning strategies
• Distributed AI algorithms and techniques
• Distributed AI systems and architectures
• Neural networks and applications
• Heuristic searching methods
• Languages and programming techniques for AI
• Constraint-based reasoning and constraint programming
• Intelligent information fusion
• Learning and adaptive sensor fusion
• Search and meta-heuristics
• Multisensor data fusion using neural and fuzzy techniques
• Integration of AI with other technologies
• Evaluation of AI tools
• Social intelligence (markets and computational societies)
• Social impact of AI
• Emerging technologies
• Applications (including: computer vision, signal processing, military, surveillance, robotics, medicine, pattern recognition, face recognition, finger print recognition, finance and marketing, stock market, education, emerging applications, etc)

 

☛ Machine Learning: Models, Technologies and Applications

• Statistical learning theory
• Unsupervised and Supervised Learning
• Multivariate analysis
• Hierarchical learning models
• Relational learning models
• Bayesian methods
• Meta learning
• Stochastic optimization
• Simulated annealing
• Heuristic optimization techniques
• Neural networks
• Reinforcement learning
• Multi-criteria reinforcement learning
• General Learning models
• Multiple hypothesis testing
• Decision making
• Markov chain Monte Carlo (MCMC) methods
• Non-parametric methods
• Graphical models
• Gaussian graphical models
• Bayesian networks
• Particle filter
• Cross-Entropy method
• Ant colony optimization
• Time series prediction
• Fuzzy logic and learning
• Inductive learning and applications
• Grammatical inference
• Graph kernel and graph distance methods
• Graph-based semi-supervised learning
• Graph clustering
• Graph learning based on graph transformations
• Graph learning based on graph grammars
• Graph learning based on graph matching
• Information-theoretical approaches to graphs
• Motif search
• Network inference
• Aspects of knowledge structures
• Computational Intelligence
• Knowledge acquisition and discovery techniques
• Induction of document grammars
• General Structure-based approaches in information retrieval, web authoring, information extraction, and web content mining
• Latent semantic analysis
• Aspects of natural language processing
• Intelligent linguistic
• Aspects of text technology
• Biostatistics
• High-throughput data analysis
• Computational Neuroscience
• Computational Statistics