Thank you for agreeing to review a manuscript for Neutrosophic Optimization and Intelligent Systems (NOIS).

Your assessment must be independent, confidential, constructive, evidence-based, and focused on the scientific quality of the manuscript.

Before Accepting the Invitation

1. Confirm that your expertise is appropriate to the manuscript and identify any significant limitations in your ability to assess its methods or conclusions.
2. Check the response and review deadlines. Decline promptly if you cannot complete the review within the requested period.
3. Disclose any actual, potential, or perceived conflict of interest to the handling editor. Do not accept a review involving a close personal relationship, current shared institution, or recent close collaboration, supervisory relationship, or joint grant with an author.
4. Do not accept an invitation merely to gain access to confidential research.
5. If the authors' identity becomes apparent and creates a conflict of interest, inform the editor immediately.

Confidentiality and Ethical Conduct

Treat the manuscript, supplementary files, research data, correspondence, and review process as strictly confidential.

Do not share review materials, delegate the assignment, contact the authors directly, or use unpublished findings for another purpose.

Co-reviewing with a junior researcher or colleague requires advance written permission from the handling editor. All approved contributors must be identified to the editorial office and comply with confidentiality and conflict-of-interest requirements.

Do not upload manuscript material or confidential information to generative AI services or use generative AI to prepare the substantive peer-review report.

Do not make coercive citation requests, use derogatory language, or attempt to identify the authors unnecessarily.

Scientific Review Criteria

Assess the following aspects as applicable to the manuscript.

Scope and Contribution: Is the research relevant to NOIS? Does it contribute genuine new scientific knowledge rather than merely rename or superficially modify established methods?

Originality and Literature: Are existing results acknowledged correctly? Are claims of novelty justified by an appropriate comparison with previous work?

Mathematical Validity: Are definitions, assumptions, theorems, mathematical derivations, proofs, and numerical examples correct and internally consistent?

Neutrosophic and Uncertainty Models: Are the uncertainty components and operators well defined? Are the asserted properties proved under the relevant conditions?

Optimization Methods: Are the objective functions, constraints, algorithmic steps, parameters, stopping rules, computational budgets, and convergence or performance claims adequately justified?

Artificial Intelligence and Machine Learning: Are the data, preprocessing, training and test partitions, baselines, evaluation metrics, and generalization claims appropriate? Have material risks of data leakage been addressed?

Statistical and Computational Evidence: Are the analyses, experimental repetitions, uncertainty estimates, and numerical results sufficient to support the conclusions?

Reproducibility: Are relevant proofs, data, code, software settings, and supporting materials described adequately? Are any restrictions on access justified?

Ethics and Integrity: Are declarations and evidence consistent with the journal's publication ethics, competing interests, data availability, and AI policies?

Presentation: Are the abstract, introduction, methods, results, figures, tables, conclusions, and references clear, accurate, and appropriately organized?

Not every criterion applies to every manuscript. Evaluate the work according to its actual scientific design.

Preparing Your Report

Provide a concise summary of the manuscript and its claimed contribution.

Identify major scientific strengths and concerns. Explain material errors precisely, referring to equation numbers, proof steps, figures, tables, experimental settings, or supporting material where appropriate.

Distinguish issues that undermine scientific validity from correctable deficiencies and optional improvements.

Number your comments so the authors can respond point by point.

Avoid recommending additional experiments or theoretical developments unless they are scientifically necessary to assess the submitted claims.

If you identify a possible ethical violation or manipulation of research or peer review, report the concern confidentially to the handling editor with relevant evidence. Do not contact third parties independently.

Use the designated author-facing field for constructive scientific comments. Reserve the confidential editor field for matters that legitimately require private handling.

Recommendation

Select the recommendation that best reflects the scientific evidence and explain its rationale.

A recommendation is advisory. The final editorial decision rests with the appropriately authorized independent editor.

Submit your report through the assigned OJS review task, not through an unrelated account or third-party platform.

If you upload an annotated file, remove your name and other identifying metadata before submitting it.

Notify the editor promptly if you require an extension or cannot complete the review.

Thank you for supporting independent scientific assessment and the integrity of the scholarly record.