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Plagiarism Policy

Plagiarism Policy

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML)

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

1. Introduction & Overview

Frontiers in Emerging Artificial Intelligence and Machine Learning (FEAIML) is committed to maintaining the highest standards of academic integrity, originality, and ethical scholarly publishing. The journal expects all submitted manuscripts to represent the authors' own original work and to appropriately acknowledge the intellectual contributions of others through accurate citation and referencing across all artificial intelligence, computer vision, natural language processing, reinforcement learning, robotics, and machine learning domains.

The journal follows internationally recognized principles of publication ethics and research integrity, including the guidelines of the Committee on Publication Ethics (COPE), the Directory of Open Access Journals (DOAJ), and Scopus selection criteria. Plagiarism, in any form, is considered a severe violation of scholarly ethics and will result in immediate editorial action before or after publication, irrespective of Article Processing Charge (APC) considerations.

2. Definition of Plagiarism

Plagiarism is the presentation of another person’s ideas, words, code, data, figures, architecture diagrams, benchmark tables, images, or intellectual work as one’s own without appropriate acknowledgment, permission, or citation. FEAIML considers both intentional and unintentional plagiarism to be completely unacceptable.

Plagiarism includes, but is not limited to:

  • Direct Copying: Copying text verbatim from published or unpublished sources without proper attribution or quotation marks.

  • Substantial Paraphrasing: Paraphrasing another author's concepts, theoretical frameworks, mathematical derivations, loss function formulations, or empirical methodologies without clear, appropriate citation.

  • Code & Algorithm Plagiarism: Reusing proprietary neural network code, training scripts, pseudo-code, numerical optimization algorithms, or hardware acceleration designs without authorization or proper source credit.

  • Data & Visual Plagiarism: Utilizing system architecture diagrams, model flowcharts, benchmark tables, empirical datasets, loss/accuracy graphs, or visual feature maps without authorization or explicit source attribution.

  • Translation Plagiarism: Translating published technical AI content from another language and presenting it as original research without proper citation.

  • Uncredited Text Recycling: Reusing substantial portions of previously published text, model training setups, or evaluation protocols without full disclosure.

3. Originality Requirement

By submitting a manuscript to FEAIML, authors confirm that their submission complies with the journal's strict originality requirements and ethical standards:

  • The manuscript represents original, unpublished scholarly work in artificial intelligence and machine learning disciplines.

  • The manuscript has not been published previously in whole or in part (except as an academic thesis, dissertation, conference abstract, or preliminary preprint, which must be formally declared upon submission).

  • The manuscript is not under simultaneous evaluation or consideration by any other journal, book chapter, or conference proceedings.

  • All external data sources, secondary code, software libraries, pre-trained model weights, and theoretical AI frameworks have been accurately cited in standard technical reference format (e.g., IEEE/APA style).

  • Written copyright permissions have been obtained for any third-party materials, baseline tables, model schematics, or figures.

  • All listed authors have reviewed, contributed to, and approved the final submitted version.

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4. Similarity Screening

All submitted manuscripts undergo mandatory automated similarity screening using advanced plagiarism detection software (e.g., Turnitin / iThenticate) during initial technical screening prior to peer review.

  • Threshold Guidelines: FEAIML adheres to an overall similarity index threshold of less than 15%, with no single source exceeding 3–5%, excluding references, standard technical terminology, governing equations/loss functions, and direct method descriptions.

  • Contextual Editorial Evaluation: Editorial decisions are based on the nature, context, source, and severity of any detected similarity rather than relying strictly on a single numerical score.

  • Legitimate Similarities: Common AI definitions, standard mathematical formulations, standard hardware/GPU setup descriptions, formal institutional terminology, and properly quoted text are evaluated contextually by the handling editor.

5. Editorial Assessment of Suspected Plagiarism

If potential plagiarism or originality issues are identified during initial screening, peer review, or production, the Editorial Office will conduct an investigation in accordance with COPE flowcharts.

Depending on the severity of the issue, the Editorial Office may:

  • Request formal clarification or a point-by-point explanation from the corresponding author.

  • Request technical revisions and proper attribution of affected sections.

  • Request raw empirical data, training logs, source code, model weights, or primary dataset files for verification.

  • Reject the manuscript outright prior to peer review if substantial plagiarism, uncredited text recycling, or code duplication is detected.

  • Issue a formal warning or notify the authors' affiliated institutions/funding bodies in cases of severe academic misconduct.

6. Plagiarism Detected After Publication

If plagiarism, data fabrication, or major originality violations are identified after an article has been published, FEAIML will conduct an objective and confidential investigation following COPE protocols.

Depending on the investigation outcome, the journal may:

  • Publish a Correction / Corrigendum if minor attribution errors occurred without unethical intent.

  • Issue an Editorial Expression of Concern while a formal institutional investigation is ongoing.

  • Formally Retract the published article in cases of confirmed serious plagiarism, duplicate submission, or uncredited text recycling.

  • Formally notify the authors' affiliated institution(s), research councils, or academic bodies.

7. Self-Plagiarism & Redundant Publication

Authors must avoid excessive reuse of their own previously published work without explicit disclosure, citation, or editorial authorization.

Unacceptable practices include:

  • Duplicate Submission: Submitting substantially identical manuscripts to multiple journals or conferences concurrently.

  • Redundant / Duplicate Publication: Publishing identical research findings, baseline evaluations, or datasets across multiple outlets without transparency.

  • Text Recycling: Reusing large passages of verbatim text from the authors' previous publications without attribution.

  • Salami Slicing: Fragmenting a single comprehensive AI/ML study into multiple overlapping, minimal publishable units without scientific justification.

8. AI-Assisted Writing & Originality

The use of Artificial Intelligence (AI) and Large Language Models (LLMs) for language polishing, grammar improvement, code formatting, or translation assistance is permitted, provided it does not compromise research integrity.

  • Authorship Restrictions: AI tools cannot be listed as authors or co-authors under any circumstances.

  • Author Responsibility: Authors remain 100% accountable for the originality, factual accuracy, mathematical correctness, and integrity of all content produced or refined using AI tools.

  • AI-Generated Plagiarism: Content produced by AI that replicates existing literature without proper citation, generates hallucinated datasets, or produces fabricated references will be treated as plagiarism and ethical misconduct.

9. Image and Data Integrity

Authors must ensure that all figures, model architecture flowcharts, visual output maps, tables, graphs, and empirical datasets accurately reflect the study's original findings.

Inappropriate manipulation of visual data (e.g., splicing, selective enhancement, or selective cropping in visual outputs), statistical falsification, metric/simulation data fabrication, or misleading data representations constitute grave scientific misconduct and will lead to immediate manuscript rejection or formal post-publication retraction.

10. Article Processing Charge (APC) Independence

FEAIML maintains a strict separation between editorial integrity and publication fees:

  • No Fee Dependency: Editorial evaluations, similarity checks, and plagiarism investigations are conducted completely independently of Article Processing Charge (APC) status.

  • Non-Refundable Misconduct Policy: In cases where plagiarism or academic dishonesty is identified post-acceptance, APC payment does not protect an article from retraction, nor does it guarantee publication. Acceptance or immunity from ethics enforcement cannot be purchased under any circumstances.

11. Authors' Responsibilities

  • Ensure absolute originality, authenticity, and academic rigor in the manuscript.

  • Accurately credit all secondary data, software tools, prior studies, baseline models, literature, and mathematical/loss formulations.

  • Secure written permission for any copyrighted third-party tables, architecture schematics, or images.

  • Cooperate fully with editorial inquiries regarding data validation, raw file checks, code/script validation, or similarity reports.

  • Immediately notify the Editorial Office if a significant error or oversight is discovered post-submission or post-publication.

12. Commitment to Research Integrity

FEAIML is dedicated to fostering academic honesty, open transparency, and scholarly trust. The journal treats all allegations of plagiarism, text recycling, code duplication, and research misconduct with utmost seriousness, conducting evidence-based, impartial, and confidential investigations to safeguard the scientific record.

13. Contact Information

For questions regarding this Plagiarism Policy, similarity screening reports, or research integrity standards, please contact the Editorial Office: