Aims and Scope

Aims

The Journal of Advanced Intelligent Computing and Informatics is an international, peer-reviewed, open-access scholarly journal that publishes original research, systematic and methodological reviews, and short communications in computational intelligence and information science. The Journal serves as a platform for rigorous, applied, and theoretically grounded scholarship that addresses the design, evaluation, and governance of intelligent computing systems and the methods, data, and infrastructure that support them.

The Journal aims to publish work that demonstrates originality, methodological rigor, reproducibility, and clearly articulated contributions to theory, method, or practice. It welcomes both single-discipline studies of high methodological quality and interdisciplinary work that draws on computer science, data science, mathematics, and engineering together.

Scope

The Journal's scope is organized around three subject domains. The areas listed below are indicative rather than exhaustive; submissions outside these examples but within the broad subject domains are welcome.

Intelligent Computing and Learning Systems

The Journal welcomes research on the methods and architectures of artificial intelligence and machine learning, including:

  • Deep learning and neural network architectures
  • Large language models, foundation models, and generative AI
  • Reinforcement learning and sequential decision-making
  • Representation, self-supervised, and transfer learning
  • Computer vision, speech, and multimodal learning
  • Natural language processing and language understanding
  • Quantum and quantum-inspired machine learning
  • Neuromorphic, spiking, and brain-inspired computing
  • Automated machine learning, meta-learning, and neural architecture search
  • Optimization, search, and metaheuristics for learning

Informatics, Data, and Distributed Systems

The Journal welcomes research on data, knowledge, and the systems that deliver intelligence at scale, including:

  • Data science, data engineering, and large-scale analytics
  • Knowledge representation, reasoning, and knowledge graphs
  • Information retrieval, recommendation, and ranking
  • Distributed, parallel, and federated learning
  • Edge computing, embedded intelligence, and the Internet of Things
  • High-performance and cloud computing for AI
  • Graph learning and network science
  • Database systems, data integration, and data quality
  • Multi-agent systems and intelligent automation
  • Human-computer interaction and human-centered AI

Foundations, Trustworthiness, and Governance

The Journal welcomes research on the theoretical and ethical foundations of intelligent computing, including:

  • Theoretical foundations of learning and computation
  • Explainability, interpretability, and transparency
  • Fairness, accountability, and bias mitigation
  • Robustness, safety, and adversarial machine learning
  • Privacy-preserving computation and security of intelligent systems
  • Causal inference and uncertainty quantification
  • Reproducibility, benchmarking, and evaluation methodology
  • Ethics, governance, regulation, and policy for artificial intelligence

Examples of in-scope work

The following examples illustrate the range of work the Journal publishes:

  • A self-supervised pretraining method for low-resource language understanding
  • An explainable graph neural network for anomaly detection in sensor networks
  • A federated learning framework for privacy-preserving training across edge devices
  • A neural architecture search method for efficient on-device inference
  • A benchmark and evaluation protocol for retrieval-augmented generation
  • A causal framework for assessing fairness in automated decision systems
  • A quantum-inspired optimization algorithm for large combinatorial problems
  • A systematic review of interpretability methods for deep neural networks

Out-of-scope and editorial discrimination

The Journal applies rigorous initial screening for scope-fit, methodological adequacy, and originality before sending manuscripts to peer review. The following categories of work are typically declined at desk-screen:

  • Pure mathematics or theory with no computational or intelligent-systems dimension
  • Domain-specific application papers whose primary contribution belongs to finance, healthcare, business, or another applied field, which are better suited to the relevant sibling journals published by World Research Union
  • Engineering or product reports and tool announcements without a research contribution
  • Method papers without adequate evaluation, baselines, or reproducibility
  • Survey-style summaries without methodological synthesis or original analysis
  • Speculative commentary on artificial intelligence without an empirical or methodological component
  • Manuscripts reapplying standard models to standard datasets without a clear, generalizable contribution

Authors uncertain about scope-fit are encouraged to send a short pre-submission inquiry through the Contact page before preparing a full manuscript.

Article types

The Journal publishes the following article types. Detailed length, structure, and review expectations are described on the Author Guidelines page.

  • Original Research Article: full-length empirical, methodological, or theoretical contribution with original findings
  • Systematic or Methodological Review: structured synthesis following PRISMA, scientometric, bibliometric, or comparable established methodology
  • Short Communication: concise report of a focused finding, including thematic syntheses, brief methodological notes, and timely results of immediate interest
  • Editorial: by invitation only, authored by members of the Editorial Team or invited contributors

Particularly welcomed contributions

While the Journal accepts submissions across the full breadth of its subject domains, contributions in the following named sub-disciplines are particularly welcomed and align closely with the Journal's editorial expertise: deep learning and foundation models, generative AI, explainable and trustworthy AI, federated and edge intelligence, graph and knowledge-based learning, reinforcement learning, and the governance of intelligent systems.