eISSN: 2978-3704 editor@irjernet.com
All Journals
Submit your article

Publication Ethics

Publication Ethics

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

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

1. Introduction & Core Principles

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is committed to upholding the highest standards of publication ethics, intellectual rigor, editorial independence, and scientific integrity. Maintaining public trust and academic credibility across foundational AI, deep learning, computer vision, natural language processing, reinforcement learning, robotics, and cross-disciplinary machine learning research requires that authors, editors, peer reviewers, and publishers strictly adhere to standardized ethical conduct.

FEAIML operates in strict accordance with the core practices, guidelines, and flowcharts established by the Committee on Publication Ethics (COPE), the Directory of Open Access Journals (DOAJ), and the standards prescribed for indexing in Scopus and other global abstracting and indexing databases.

2. Duties & Ethical Responsibilities of Authors

Authors submitting their manuscripts to FEAIML must ensure total transparency, compliance, and adherence to the following scientific standards:

  • Originality & Novelty: Manuscripts submitted to FEAIML must represent entirely original artificial intelligence and machine learning research that has not been previously published elsewhere (in full or in part) and is not currently under consideration by any other journal, book chapter, or conference proceedings platform.

  • Data Transparency & Model Reproducibility: Authors must present accurate experimental data, benchmark testing results, algorithmic formulations, hyperparameter configurations, and objective interpretations of their research findings. Fabricating, falsifying, or selectively omitting benchmark runs or dataset metrics is strictly prohibited. Raw training logs, dataset preprocessing steps, model weights, and custom analysis scripts must be retained and made available upon reasonable request from editors or reviewers.

  • Proper Attribution & Referencing: Credit must be explicitly given to all source material, foundational algorithms, open-source code repositories, benchmark datasets, and technical instruments used. Third-party work must be cited accurately according to standard technical referencing styles (e.g., IEEE/APA style).

  • Disclosure of Conflicts of Interest: Authors must disclose all potential financial, commercial, personal, or institutional conflicts of interest that might bias or appear to influence the model architecture design, experimental evaluations, or conclusions.

  • Funding Statements: All financial support, research grants, compute resource sponsorships, or institutional funding received for conducting the AI research must be explicitly acknowledged in the manuscript.

  • Use of Generative AI Tools: The use of artificial intelligence (AI) tools, such as Large Language Models (LLMs) or automated code generation assistants, must be clearly disclosed in the Methods or Acknowledgments section. AI tools do not meet authorship criteria and cannot be listed as authors or co-authors.

  • Post-Publication Obligations: If an author discovers a significant error or inaccuracy in their published work, they are obligated to promptly notify the Handling Editor or Editor-in-Chief and cooperate fully to publish an erratum, corrigendum, or retraction notice.

Submit Article Now

3. Originality, Plagiarism & Research Misconduct

FEAIML maintains a strict zero-tolerance policy regarding academic dishonesty, unethical practices, and research misconduct. Every manuscript undergoes automated plagiarism detection software (e.g., Turnitin / iThenticate) prior to initial editorial evaluation.

The journal strictly prohibits:

  • Direct Plagiarism: Copying text, mathematical equations, neural network architectures, or algorithmic code verbatim from another source without quotation marks and appropriate citation.

  • Mosaic & Paraphrased Plagiarism: Rephrasing or restructuring computational frameworks, machine learning models, or theoretical proofs from third-party sources without clear attribution.

  • Self-Plagiarism & Text Recycling: Reusing substantial sections of the author’s own previously published technical papers without disclosure or citation.

  • Data Fabrication & Falsification: Inventing false empirical metrics, altering accuracy scores, fabricating loss curves, or manipulating benchmark comparisons to support false technical claims.

  • Visual & Graph Manipulation: Modifying visual data representations (such as feature maps, confusion matrices, ROC curves, or architecture block diagrams) in a way that alters the underlying scientific truth.

Consequence of Misconduct: Confirmed instances of deliberate research misconduct, code piracy, or plagiarism exceeding the allowable threshold (typically <15% overall similarity, excluding references and standard technical terminology) will result in immediate desk rejection, formal notification to the authors' affiliated institutions, and a temporary or permanent ban on future submissions.

4. Authorship Criteria & Acknowledgment

Authorship credit must accurately reflect meaningful intellectual and scholarly contributions to the manuscript. FEAIML adheres strictly to the ICMJE/COPE authorship standards.

Authorship Eligibility

To be listed as an author, an individual must meet all four of the following criteria:

  1. Substantial contributions to the conceptualization, algorithm design, model training, dataset curation, code implementation, empirical testing, or mathematical modeling of the study.

  2. Drafting the manuscript or revising it critically for important intellectual content.

  3. Approval of the final version of the manuscript to be published.

  4. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Acknowledgments & Contributors

Individuals who contributed to the study (e.g., lab technicians, compute cluster support staff, language editors, formatting personnel, or administrative supervisors) but do not meet all four authorship criteria must be acknowledged in the Acknowledgments section, rather than named as co-authors.

5. Duties & Responsibilities of Editors

The Editor-in-Chief and the Editorial Board of FEAIML maintain absolute independence and responsibility for all publication decisions:

  • Fair & Impartial Evaluation: Manuscripts are evaluated solely on their scientific quality, computational rigor, originality, technical clarity, and relevance to emerging artificial intelligence and machine learning technologies. Evaluations are completely free from bias regarding the authors' race, gender, sexual orientation, geographic origin, religious beliefs, political views, or institutional affiliation.

  • Editorial Independence & APC Separation: Decisions to accept or reject a manuscript are entirely independent of commercial interests, publisher influences, or Article Processing Charge (APC) payments. An APC becomes applicable only after formal double-blind peer review and editorial acceptance to cover open-access publishing services. Acceptance cannot be purchased or guaranteed under any circumstances.

  • Confidentiality: Editors and editorial staff must safeguard the confidentiality of all submitted manuscripts, proprietary algorithms, model weights, and code repositories, and must not disclose details to anyone other than the corresponding author, assigned reviewers, and editorial advisers.

  • Handling Conflicts of Interest: Editors must recuse themselves from evaluating manuscripts in which they have competing interests arising from direct collaborative, commercial, financial, or personal relationships with any of the authors. Contact the Editorial Office at editor@irjernet.com for ethical inquiries.

6. Duties & Responsibilities of Peer Reviewers

Peer review is the foundation of scholarly publishing integrity. Reviewers invited by FEAIML must fulfill the following duties:

  • Objective & Constructive Critique: Reviews should be conducted objectively, impartially, and constructively. Reviewers should clearly express their technical views with supporting arguments, evaluating algorithm soundness, baseline comparisons, and statistical validity, while avoiding personal or derogatory comments against authors.

  • Confidentiality: Reviewers must treat all assigned manuscripts, algorithms, model architectures, datasets, and execution logs as strictly confidential documents. Un-published manuscript details or code must not be shared, discussed, or utilized for personal research gain.

  • Promptness: Invited reviewers must respond promptly to review requests and complete evaluations within the specified timeframe. If a reviewer feels unqualified or unable to review within the deadline, they should notify the editor immediately.

  • Identification of Ethical Issues: Reviewers must alert the Handling Editor if they detect substantial overlaps with previously published works, suspected data/metric fabrication, uncredited dataset usage, or undisclosed conflicts of interest.

7. Double-Blind Peer Review Integrity

To eliminate potential unconscious biases, FEAIML strictly enforces a Double-Blind Peer Review process.

  • The identities of the authors are withheld from the peer reviewers throughout the evaluation process.

  • The identities of the peer reviewers are concealed from the authors.

Peer Review Outcomes

Following external review, the handling editor renders one of four standard decisions:

  1. Accept without Revision

  2. Minor Revisions Required

  3. Major Revisions Required (Subject to mandatory re-evaluation)

  4. Reject

The Editor-in-Chief retains final authority and responsibility for all editorial decisions.

8. Article Processing Charges (APC) & Publishing Sustainability

FEAIML is an Open Access journal dedicated to global knowledge sharing. To maintain publishing operations, portal infrastructure, typesetting, Crossref DOI registration, and long-term digital archiving:

  • No Submission Charges: Authors can submit manuscripts and undergo double-blind peer review without any upfront submission or evaluation fees.

  • APC Application: An Article Processing Charge (APC) applies only upon official acceptance of the manuscript following peer review to sustain Open Access production.

  • Waivers: APC waiver policies are available for deserving researchers, early-career AI engineers, and authors from low-income nations to ensure financial barriers do not impede publication.

  • Editorial Insulation: APC handling is entirely separated from editorial evaluations. Peer reviewers and editors have no visibility into payment details.

9. Corrections, Retractions & Expressions of Concern

FEAIML is dedicated to maintaining the permanent accuracy, completeness, and integrity of the scholarly record. Post-publication issues are handled in strict compliance with COPE guidelines:

  • Errata / Corrigenda: Published when minor honest errors, typos, or inadvertently omitted disclosures occur that do not alter the main scientific or algorithmic findings of the published paper.

  • Expressions of Concern: Issued by the Editor-in-Chief if a formal investigation into potential research misconduct, benchmark manipulation, or dataset licensing breaches is ongoing, but definitive evidence is not yet available.

  • Retractions: Executed in severe cases of confirmed plagiarism, duplicate publication, data/metric fabrication, unauthorized use of proprietary code/data, or flawed technical conclusions that render the work unreliable. A formal Retraction Notice will be attached to the original article detailing the precise grounds for retraction.

10. Appeals, Complaints & Editorial Governance

  • Appeals: Authors who believe their manuscript was rejected due to a factual misunderstanding or procedural error may submit a formal appeal to the Editorial Office at editor@irjernet.com within 14 days of the decision. Appeals must provide detailed, evidence-based responses addressing the specific points raised by reviewers and editors.

  • Complaints: Allegations regarding editorial misconduct, reviewer bias, or breaches of ethical standards should be addressed directly to the Editor-in-Chief at editor@irjernet.com. Complaints are thoroughly, confidentially, and impartially investigated following established COPE flowcharts.

11. Digital Archiving & Open Access Policy

FEAIML is a fully Open Access journal operating under the Creative Commons Attribution (CC BY 4.0) license. All articles are published and immediately made freely available to the global academic, AI research, and technical community.

To ensure long-term preservation and permanent digital availability, FEAIML's published content is archived across digital repository systems, institutional archives, and permanent Crossref DOI metadata registers, ensuring accessibility even if the journal ceases operation.

12. Contact Information

For questions regarding publication ethics, reports of scientific misconduct, or inquiries about editorial policies, please contact the Editorial Office: