Advanced Computational Intelligence: An International Journal (ACII)

Scope & 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

Important Dates

  • Submission Deadline : February 01, 2026
  • Notification                   : February 28, 2026
  • Final Manuscript Due : March 07, 2026
  • Publication Date          : Determined by the Editor-in-Chief


Call for Papers