About the Journal

About the Journal

Overview

The Frontiers in Applied AI & Machine Learning (FAAIML) is an international, peer-reviewed, open-access journal published by Inkwise Press.
FAAIML provides a global platform for the dissemination of cutting-edge research, methodologies, and applications in artificial intelligence (AI), machine learning (ML), and data-driven systems across multiple disciplines.

The journal is committed to advancing both theoretical foundations and real-world implementations, promoting cross-disciplinary research that bridges computer science, engineering, medicine, economics, and societal development.

Aims and Scope

The journal aims to publish original, high-quality research that advances the understanding and application of AI and ML in solving complex real-world problems.

Topics of interest include, but are not limited to:

  • Machine learning algorithms and optimization

  • 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

FAAIML encourages interdisciplinary collaboration between researchers and practitioners from academia, industry, and government sectors.

Mission

To advance the frontiers of applied artificial intelligence through open, ethical, and impactful research that shapes the intelligent systems of tomorrow.

FAAIML seeks to empower innovation, enhance transparency, and promote responsible AI development that contributes to sustainable global progress.

Editorial and Peer Review Policy

  • All submissions undergo rigorous double-blind peer review by qualified experts in AI and related fields.

  • Each manuscript is evaluated for originality, technical depth, and practical relevance.

  • Submissions are screened using iThenticate/Turnitin for plagiarism and research integrity.

  • The editorial process adheres to COPE, ICMJE, and OASPA ethical guidelines.

Open Access and Licensing

FAAIML operates under a gold open-access policy, providing immediate and unrestricted access to all published content.
All articles are published under the Creative Commons Attribution (CC BY 4.0) license, allowing unrestricted use, distribution, and adaptation with proper credit to the authors.

Indexing and Digital Preservation

To ensure global discoverability and permanence, all FAAIML publications are:

  • Assigned Crossref DOIs for citation and reference tracking.

  • Archived through LOCKSS, CLOCKSS, and the PKP Preservation Network (PKP PN).

  • Structured to comply with metadata and ethical standards required for Scopus, Web of Science (WoS), and DOAJ indexing.

Target Audience

  • AI and machine learning researchers

  • Data scientists and engineers

  • Academics in computer science, robotics, and informatics

  • Healthcare, finance, and industrial technology innovators

  • Policy makers and ethicists exploring responsible AI