Advanced Computational Intelligence: An International Journal (ACII)

Scope and Topics

Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence.

Topics of interest include but are not limited to, the following

Foundation Models and Large Scale AI Systems

• Large language models (LLMs)
  
• Multimodal foundation models
  
• Retrieval augmented generation (RAG)
  
• Instruction tuning and alignment
  
• Evaluation, safety, and robustness of large models
  
  Deep Learning and Neural Architectures
  
• Transformers and attention based models
  
• Diffusion models and generative deep learning
  
• Self supervised and contrastive learning
  
• Efficient deep learning (compression, distillation, quantization)
  
• Spiking neural networks and neuromorphic computing
  
  Multimodal and Human Centered AI
  
• Vision language and audio language models
  
• Multimodal reasoning and representation learning
  
• Human AI interaction and adaptive interfaces
  
• Affective computing and cognitive modeling
  
  Reinforcement Learning and Autonomous Agents
  
• Deep reinforcement learning
  
• Multi agent systems
  
• Offline and batch RL
  
• Agentic AI and tool using agents
  
• Autonomous decision making and planning
  
  Hybrid and Neuro Symbolic Intelligence
  
• Logic augmented neural networks
  
• Differentiable reasoning
  
• Knowledge infused learning
  
• Hybrid optimization and learning systems
  
  Graph and Relational Machine Learning
  
• Graph neural networks (GNNs)
  
• Knowledge graph construction and reasoning
  
• Relational representation learning
  
• Network science and graph based optimization
  
  Causal and Explainable AI
  
• Causal discovery and causal representation learning
  
• Counterfactual reasoning
  
• Explainable and interpretable machine learning
  
• Trustworthy, fair, and ethical AI
  
  Generative Intelligence
  
• Diffusion based generative models
  
• Generative adversarial networks (GANs)
  
• Synthetic data generation
  
• Generative agents and simulation environments
  
  Edge, Embedded, and Distributed AI
  
• TinyML and on device learning
  
• Federated and privacy preserving learning
  
• Distributed training and scalable AI systems
  
• AI for IoT and cyber physical systems
  
  Classical and Foundational Computational Intelligence
  
• Evolutionary computation and swarm intelligence
  
• Fuzzy systems and soft computing
  
• Optimization and metaheuristics
  
• Machine learning theory
  
• Pattern recognition and statistical modeling
  
  AI for Science, Engineering, and Society
  
• AI for healthcare, medicine, and bioinformatics
  
• AI for climate, sustainability, and environmental modeling
  
• AI for materials science and physics
  
• AI policy, governance, and societal impact
  
  

Paper Submission

Authors are invited to Submit papers for this journal through aciij@aircconline.com Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

Editorial Board Members

• Abdolreza Hatamlou, Islamic Azad University, Iran
• Abe Zeid, Northeastern University, USA
• Ajayeb Abu Daabes, Emirates College Technology, United Arab Emirates
• Amol D Mali, University of Wisconsin, USA
• Amritam Sarcar,Microsoft Corp.,USA
• Anamika Ahirwar, Rajiv Gandhi Technical University, India
• Ankit Thakkar, Nirma University, India
• Antonio Rodriguez M, Universidad Autonoma del Estado de Morelos, Mexico

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