eISSN: 2978-3704 editor@irjernet.com
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Instruction For Authors

Instruction For Authors

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

eISSN: 2978-3704 | International Peer-Reviewed Journal | Open Access

1. About the Journal

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is an international, double-blind peer-reviewed, Open Access scholarly journal dedicated to publishing high-quality, impactful research that advances knowledge in foundational AI, deep learning algorithms, computer vision, natural language processing, reinforcement learning, robotics, and cross-disciplinary machine learning applications.

FEAIML provides an inclusive global platform for researchers, computer scientists, AI engineers, data specialists, and technology policy experts to disseminate original empirical research, critical theoretical insights, novel algorithmic architectures, and evidence-based computational solutions addressing complex challenges in modern intelligent ecosystems.

2. Scope and Acceptable Topics

The journal welcomes high-quality submissions covering (but not limited to) the following core focus areas:

  • Foundational AI & Machine Learning Algorithms: Supervised, Unsupervised, Semi-Supervised, Self-Supervised, and Reinforcement Learning, Mathematical Foundations of Machine Learning, Optimization Techniques, and Statistical Learning Theory.

  • Deep Learning & Neural Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Diffusion Models.

  • Computer Vision & Image Processing: Visual Pattern Recognition, Object Detection and Segmentation, Video Analytics, 3D Computer Vision, Biometrics, and Medical Image Analysis.

  • Natural Language Processing & Speech: Large Language Models (LLMs), Machine Translation, Sentiment Analysis, Dialogue Systems, Text Mining, Knowledge Graphs, and Automatic Speech Recognition (ASR).

  • Robotics, Autonomous Systems & Edge AI: Intelligent Control Systems, Multi-Agent Systems, Swarm Intelligence, Embedded AI, Edge Computing Optimization, and Autonomous Navigation.

  • Trustworthy, Explainable & Ethical AI: Explainable AI (XAI), Fairness, Accountability, and Transparency (FAT), AI Safety, Adversarial Robustness, Privacy-Preserving Machine Learning, and Federated Learning.

  • Interdisciplinary AI Applications: AI in Healthcare, Computational Biology, Smart Grids, Financial Engineering (FinTech), Quantum Machine Learning, Cyber-Physical Systems, and Industrial Automation.

3. Acceptable Article Types

FEAIML publishes several distinct categories of scholarly contributions:

  • Original Research Articles: Empirical, experimental, computational, or theoretical studies presenting novel algorithmic designs, empirical benchmark evaluations, and clear contributions to artificial intelligence and machine learning.

  • Review Papers & Systematic Literature Reviews: Critical, systematic, and comprehensive reviews of existing AI literature, meta-analyses, state-of-the-art model comparisons, and emerging technological frameworks.

  • Case Studies & Technical Reports: In-depth analyses of real-world industrial AI deployments, complex system integration challenges, hardware acceleration testing, or field evaluations.

  • Conceptual Papers & Frameworks: Theoretical computational models, novel optimization paradigms, and groundbreaking architectural concepts.

  • Short Communications & Technical Notes: Concise reports on preliminary experimental findings, novel open-source software tools, benchmark dataset releases, or urgent perspectives on AI developments.

4. Pre-Submission Checklist

Before submitting a manuscript, authors must verify full compliance with the following requirements:

  • Originality: The manuscript represents entirely original work and has not been published previously in any form (except as an academic thesis or preliminary preprint).

  • Exclusivity: The manuscript is not currently under consideration by any other journal, edited book, or conference platform.

  • Author Approvals: All co-authors have reviewed, approved, and explicitly agreed to the submission of the final manuscript version.

  • Ethical Compliance: Necessary institutional ethics board approvals, dataset licensing compliance, and data integrity requirements have been strictly met.

  • Plagiarism & Integrity: The manuscript adheres to the journal's Publication Ethics and maintains a similarity index below the acceptable journal threshold (<15% overall, excluding references and standard technical/mathematical terms).

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5. Manuscript Preparation Guidelines

A. General Formatting

  • File Format: Microsoft Word (.doc or .docx) format only.

  • Language: Academic English (clear, concise, and grammatically correct).

  • Font & Size: Times New Roman, 12 pt size.

  • Line Spacing: 1.5 line spacing throughout the entire document.

  • Margins: Standard 2.5 cm (1 inch) on all sides.

  • Page Numbers: Consecutive numbering centered at the bottom of each page.

B. Recommended Manuscript Structure (IMRaD)

Title Page

  • Concise and informative manuscript title.

  • Full names, institutional affiliations, city, and country of all authors.

  • Active ORCID iDs for all authors.

  • Corresponding author’s official email address (editor@irjernet.com / author email) and contact details.

Abstract & Keywords

  • Abstract: A structured or unstructured summary between 150–250 words covering background, AI research objective, model architecture/methodology, key benchmark results, and practical implications.

  • Keywords: 4 to 6 relevant technical keywords reflecting the primary subjects of the research (e.g., Deep Learning, Transformer Networks, Computer Vision, Model Optimization).

Main Body Structure

  • Introduction: Research problem statement, theoretical/practical context, research gaps in current state-of-the-art models, research questions, and specific study objectives.

  • Literature Review / Theoretical Framework: Critical evaluation of prior work, baseline algorithms, and theoretical/mathematical foundation.

  • Methodology: Algorithm formulations, neural network architectures, hyperparameter configurations, dataset descriptions, pre-processing protocols, evaluation metrics, and software/hardware specs used.

  • Results / Benchmark Analysis: Clear presentation of quantitative empirical metrics, loss/accuracy curves, comparison tables against baselines, visual feature maps, or performance speedups.

  • Discussion: Interpretation of technical findings, analysis of model failure cases, computational trade-offs, engineering contributions, practical implications, and study limitations.

  • Conclusion: Concise summary of key algorithmic outcomes, practical recommendations, and future research directions.

Declarations & Back Matter

  • Funding Statement: Details of research grants, compute resource sponsorships, or university funding received.

  • Conflict of Interest Declaration: Disclosure of any personal, commercial, financial, or institutional competing interests.

  • Data & Code Availability Statement: Clear explanation of where raw datasets, pre-trained model weights, and custom analysis/code repositories (e.g., GitHub, Zenodo) can be accessed.

  • Acknowledgements: (If applicable) Recognition of technical assistance, compute cluster support, language editing, or institutional backing.

  • References: Formatted strictly in standard technical referencing style (e.g., IEEE or APA style). Active Digital Object Identifiers (DOIs) must be provided for all references wherever available.

  • Tables & Figures: Embedded directly within the text close to their first mention with clear, descriptive captions and high-resolution quality.

6. Peer Review & Editorial Process

FEAIML enforces a rigorous Double-Blind Peer Review process to safeguard scientific quality and objectiveness:

  1. Initial Desk Review: The Editorial Office evaluates submissions for scope alignment, formatting compliance, language clarity, and similarity analysis (Turnitin/iThenticate).

  2. Double-Blind Peer Review: Manuscripts meeting desk standards are evaluated by at least two independent domain experts without revealing author or reviewer identities.

  3. Editorial Decision: Based on reviewer recommendations, the Handling Editor / Editor-in-Chief issues one of five formal decisions:

    • Accept without Revision

    • Minor Revisions Required

    • Major Revisions Required (Subject to mandatory re-evaluation)

    • Reject with Opportunity for Resubmission

    • Reject

7. Artificial Intelligence (AI) Policy

  • Authors may use Artificial Intelligence (AI) or Large Language Model (LLM) tools solely for language translation, basic grammar improvement, software code optimization, or readability refinement.

  • AI tools cannot be listed as an author or co-author under any circumstances, as they do not meet authorship eligibility criteria.

  • Authors must explicitly declare the use of AI tools in the Methods or Acknowledgments section and remain 100% accountable for the originality, factual accuracy, mathematical correctness, and integrity of the content.

8. Open Access, Copyright & APC

  • Open Access: All published articles are made freely and permanently accessible online to readers, researchers, and engineers worldwide immediately upon publication without restriction.

  • Copyright Retention: Authors retain 100% full copyright of their published work licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

  • Submission Fee: $0 (No charges for submitting or reviewing manuscripts).

  • Article Processing Charge (APC): To cover digital infrastructure, Crossref DOI registration, typesetting, and archiving, an Article Processing Charge (APC) is payable only after formal double-blind peer review and final editorial acceptance. There are no submission or hidden charges.

9. Manuscript Submission & Editorial Contact

Manuscripts must be submitted online through the journal's official publishing portal: