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AIA 2023 will be the most comprehensive conference focused on the various aspects of advances in Artificial Intelligence and Application. Our Conference provides a chance for academic and industry professionals to discuss recent progress in the area of Artificial Intelligence and Application. The goal of this conference is to bring together the researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects Artificial Intelligence and Application. All papers accepted and registered will be included in STEM Proceedings and selected papers will be included in SCOPUS and EI journals. Topics of Conference The main topics include but will not be limited to: (Excellent surveying works in these areas are welcome, too.) AI and Evolutionary Algorithms Algebraic Biology Ant colony optimization Applications in Bioinformatics Approximate Reasoning Architectures of Intelligent Systems Aspects of knowledge structures Aspects of natural language processing Aspects of text technology Automated problem solving Bayesian methods Bioinformatics and computational biology Biological network analysis Biostatistics Brain models / cognitive science Combining multiple knowledge sources in an integrated intelligent system Computational Science Constraint-based reasoning and constraint programming Cross-Entropy method Data Mining Decision support systems Constructions technology economy and management Contracting and Legal Issues Data Sensing and Analysis Decision Support Systems Distributed AI algorithms and Techniques Emerging Technologies Evaluation of AI tools Evolutionary Computation Expert systems Fuzzy logic, modeling, control and soft computing Gaussian graphical models General issues in graph and tree mining Grammatical inference Granular Computing Graph learning based on graph grammars Heuristic optimization techniques Hierarchical learning models High-throughput data analysis Hybrid Intelligent Systems Image processing and understanding (interpretation) Inductive learning and applications Information-theoretical approaches to graphs Integration of AI with other technologies Intelligent agents Intelligent databases Intelligent information fusion Knowledge acquisition and discovery techniques Knowledge networks and management Knowledge-intensive problem solving techniques Languages and programming techniques for AI Learning and adaptive sensor fusion Machine learning Markov chain Monte Carlo (MCMC) methods Meta learning Motif search Multi-criteria reinforcement learning Multiple hypothesis testing Multisensor data fusion using neural and fuzzy techniques Natural language processing Nature Inspired Methods Neural networks and applications Real-world applications of Intelligent Systems Particle filter Reasoning strategies Reinforcement learning Robotics Search and meta-heuristics Simulated annealing Social intelligence (markets and computational societies) Statistical learning theory Stochastic optimization Supervised and unsupervised classification of web data Time series prediction Topics on satisfiability Unsupervised and Supervised Learning Web Mining