Aim & Scope

Aim

Machine Learning and Applied Artificial Intelligence provides a peer-reviewed venue for applied machine learning. The journal is intended for machine-learning researchers, applied AI engineers, and domain scientists using predictive systems. Its editorial goal is to publish articles that make the research question, method, evidence, and limitations visible enough for readers to evaluate and reuse.

Core Scope

The journal considers manuscripts in the following areas:

  • Original studies in applied machine learning with transparent assumptions and evaluable evidence.
  • Research on AI deployment that explains methods, data, and interpretation limits.
  • Applied work involving model evaluation where practical relevance is supported by analysis rather than assertion.
  • Interdisciplinary work connecting domain adaptation to adjacent scientific, engineering, health, environmental, social, or policy questions.

Article Types Considered

The journal may consider application studies, deployment reports, benchmark papers, methods papers, negative results, and reviews. Article type should be selected according to the main contribution, not according to desired length or perceived prestige.

Method and Evidence Expectations

For this field, manuscripts should pay particular attention to:

  • problem formulation
  • training data suitability
  • baseline and deployment context
  • model monitoring
  • failure modes

Out of Scope

The journal does not consider manuscripts that are purely promotional, lack a research question, duplicate previously published work, make unsupported clinical or policy claims, present unverifiable results, or fall outside applied machine learning. Manuscripts that are technically sound but do not fit the journal's subject identity may be returned before peer review.

Editorial Standard

The journal does not require spectacular novelty. It requires a clear contribution, appropriate citations, transparent methods, relevant ethical approvals where needed, and a limitations section. Reviewers and editors should ask whether the work is trustworthy and useful for its intended readership.