Journal of AI, Automation & Intelligent Systems

About this journal

1.0 Aims and Scope

The Journal of AI, Automation & Intelligent Systems (JAAIS) publishes rigorous theoretical, computational, experimental and applied scholarship concerned with the development, evaluation and responsible deployment of artificial intelligence, automation and intelligent systems. The journal provides a forum for research that advances the scientific foundations of intelligent technologies while also examining how those technologies behave when transferred from controlled experimental environments into complex operational settings.

JAAIS is particularly interested in research that connects algorithmic innovation with system-level performance. Contributions should therefore move beyond demonstrations of isolated predictive accuracy and explain how intelligent systems perform under realistic constraints including incomplete or changing data, limited computational resources, uncertainty, latency, sensor degradation, human interaction, adversarial conditions, safety requirements and distribution shift.

The journal welcomes work spanning artificial intelligence, machine learning, deep learning, reinforcement learning, computer vision, intelligent control, robotics, autonomous systems, embodied intelligence, human–robot interaction, edge intelligence, Internet of Things systems, cyber-physical systems, digital twins and industrial automation. Research addressing intersections among these fields is particularly encouraged where the integration creates a substantive scientific or engineering contribution.

JAAIS also provides a venue for research on trustworthy intelligent systems. Relevant work includes robustness, reliability, explainability, uncertainty estimation, verification, validation, privacy-preserving learning, adversarial resilience, human oversight, algorithmic accountability, model monitoring and safety-critical AI.

The journal recognises that the practical significance of intelligent systems increasingly depends on more than model performance. It therefore welcomes research addressing the complete lifecycle of intelligent technologies, including data acquisition, system design, training, validation, deployment, monitoring, maintenance and adaptation.

Research may be theoretical, simulation-based, laboratory-based, field-deployed or mixed in design. However, claims must remain proportional to the evidence presented. Results obtained exclusively from simulation or benchmark datasets should not be described as evidence of successful real-world deployment unless such deployment has actually been evaluated.

1.1 Core Research Areas

The journal's core areas include machine learning and deep learning; reinforcement and adaptive learning; computer vision and multimodal perception; robotics and autonomous machines; human–robot and human–AI interaction; intelligent control; industrial automation; edge and embedded intelligence; IoT-enabled intelligence; cyber-physical systems; digital twins; sensor fusion; predictive and condition-based maintenance; autonomous decision systems; trustworthy and responsible AI; explainable AI; model robustness; uncertainty quantification; AI safety; privacy-preserving intelligent systems; distributed intelligence; intelligent manufacturing; and real-world evaluation of AI-enabled technologies.

JAAIS also considers emerging research that falls between conventional disciplinary boundaries where the manuscript demonstrates clear relevance to intelligent systems.

1.2 Interdisciplinary and Emerging Areas

Interdisciplinary work may connect artificial intelligence with engineering, manufacturing, healthcare technology, environmental monitoring, mobility, agriculture, infrastructure, energy systems, public services or other application domains. Such articles must nevertheless make a genuine contribution to AI, automation or intelligent-systems scholarship rather than merely applying an existing algorithm to a new dataset.

The journal particularly welcomes research investigating how intelligent systems interact with people, institutions, physical infrastructure and changing environments.

2.0 Contributions We Welcome

JAAIS considers Original Research, Review Articles, Systematic Reviews where appropriate, Methods and Technical Articles, Short Communications, Perspectives, Case Studies, Editorials and other article types approved by the journal.

Original Research should present a clearly defined research problem, appropriate methodology, sufficient comparison with relevant baselines and conclusions supported by the reported evidence.

Review articles should provide analytical synthesis rather than descriptive catalogues of prior studies. Methods and Technical Articles should demonstrate why the proposed technique matters and provide sufficient detail for independent evaluation or replication.

2.1 Evidence and Methodological Expectations

Authors should report datasets, sampling or data-generation procedures, preprocessing, training and validation strategies, evaluation metrics, baselines, statistical procedures and computational resources sufficiently clearly to permit expert assessment.

Claims of superior performance should be supported by appropriate comparisons. Where stochastic models are used, authors should report variability rather than a single favourable run where technically feasible.

Authors should distinguish training, validation and test data clearly and address potential leakage or inappropriate reuse of benchmark information.

3.0 Disciplinary Standards

Reproducibility is a central expectation of JAAIS. Authors should provide sufficient information concerning algorithms, hyperparameters, software environments, hardware, model architecture, experimental conditions and evaluation procedures.

Where source code, trained models or datasets can legitimately be shared, authors are encouraged to provide stable repository links. Where they cannot be shared, the limitation should be explained.

Research involving physical robots, autonomous platforms or cyber-physical systems should describe hardware configuration, sensing, control architecture, experimental environment and safety considerations.

3.1 Research Transparency

Authors should distinguish clearly between measured results, simulated results, inferred results and projected performance. Computational cost, latency, memory requirements and energy consumption should be reported where they materially affect claims about practical deployment.

Bias, privacy, cybersecurity, robustness and safety implications should be addressed when relevant to the system being studied.

4.0 Topics Outside Scope

JAAIS does not normally consider manuscripts consisting primarily of routine application of an established algorithm to a new dataset without methodological or substantive insight; promotional descriptions of commercial products; unsupported claims about artificial intelligence capabilities; manuscripts lacking sufficient methodological information for meaningful evaluation; or speculative commentary presented as empirical evidence.