Artificial Intelligence, Machine Learning & Data Science

Research on intelligent computational methods that produce a clear scientific, engineering, or interdisciplinary contribution. The journal welcomes methodological advances as well as rigorous applications where AI or data science is central to the research question rather than used only as a routine tool.

Scope Guidance

What We Publish

Submissions in this area should make a clear scientific, engineering, computational, analytical, or interdisciplinary contribution. Suitable research includes:

Machine learning, deep learning, foundation models, generative AI, and multimodal learning with a clear research contribution.

Explainable, trustworthy, robust, privacy-preserving, or responsible AI methods and evaluation.

Scientific machine learning, data-driven modelling, optimization, and intelligent decision-support systems.

Computer vision, pattern recognition, natural language processing, time-series analysis, and multimodal data fusion.

AI for engineering, environment, agriculture, remote sensing, digital health, energy, manufacturing, and other scientific domains.

Benchmarking, datasets, reproducible AI workflows, uncertainty analysis, and rigorous comparative studies.

Illustrative Examples

Example Research Directions

The examples below illustrate the type and level of research that can fit this area. They are examples, not a closed list.

Explainable deep learning for multi-sensor fault diagnosis in industrial machinery

Multimodal AI for satellite-based geohazard susceptibility assessment

Robust machine learning under domain shift for scientific image analysis

Data-driven optimization of energy systems using hybrid physics-informed models

Interpretable time-series learning for predictive maintenance

Benchmarking lightweight neural networks for edge-based scientific sensing

Scope Boundaries

Generally Not Suitable

Simple application of an off-the-shelf model without a clear scientific contribution.

Pure software demos or dashboards with no research question, validation, or methodological insight.

Claims based only on training accuracy without appropriate baselines, testing, or uncertainty analysis.

Important: A manuscript is assessed by its actual research contribution and scope fit, not only by its subject label. Borderline interdisciplinary work may still be suitable when the scientific or technological contribution is clear.

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