Aim & Scope
Frontiers in Applied AI & Machine Learning (FAAIML) is a peer-reviewed, open-access journal published by Inkwise Press. It publishes research on how AI and machine learning methods are built, tested and put to work on real problems, from new algorithms to careful studies of deployed systems.
We value work that is honest about data, evaluation and limitations. A well-designed negative result or a reproducible benchmark study can be as useful to readers as a new state-of-the-art model.
Scope
FAAIML publishes original research articles, review articles, case studies and short communications in areas including, but not limited to:
- Machine learning algorithms and optimisation
- Deep learning and neural networks
- Natural language processing and generative AI
- Computer vision and image analysis
- Reinforcement learning and autonomous systems
- Data science, analytics and big data applications
- AI in healthcare, finance and education
- Human–AI interaction and explainable AI
- Ethical, legal and societal implications of AI
- AI for sustainable development and smart systems
Peer Review
Every submission goes through double-blind peer review by at least two independent reviewers with expertise in the subject. The usual time from submission to first editorial decision is 12–14 weeks. See the Peer Review Policy for details.
Originality
All manuscripts are screened for similarity with iThenticate before they go to reviewers, as set out in the Plagiarism Policy.
Open Access and Licensing
FAAIML is fully open access. Authors keep the copyright in their work, which is published under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Article Processing Charges
There is no Article Processing Charge. Frontiers in Applied AI & Machine Learning charges authors nothing at any stage of submission, peer review or publication.
