Journal of Mathematical Modelling and Artificial Intelligence (JMMAI)

01 Editorial Policy

JMMAI considers manuscripts that meet the journal's standards for ethical authorship, transparent reporting, and responsible scholarly communication. The journal applies recognized publication ethics principles, including COPE guidance, and adapts relevant international authorship and reporting norms to research in mathematical modelling, computational science, artificial intelligence, machine learning, optimization, statistics, and related interdisciplinary areas.

Authors should review these guidelines before submission. A manuscript that is carefully prepared, ethically documented, and clearly aligned with the journal's scope can move through editorial screening and peer review more efficiently.

02 General Requirements for Manuscript Preparation

Every submission to JMMAI must include the files and declarations needed for double-blind review and responsible editorial assessment.

• Conflict of interest declaration: Authors must submit a completed conflict of interest statement with the manuscript.
• Anonymized main manuscript: The manuscript file must not contain author names, affiliations, email addresses, acknowledgments, or any other identifying details.
• Separate title page: Author names, affiliations, email addresses, ORCID identifiers, declarations, acknowledgments, and the cover letter should be uploaded separately from the blinded manuscript.
• Complete online metadata: All author details entered in the submission system must be accurate and consistent with the title page.
• Supplementary files: Where relevant, authors should upload authorship declarations, consent forms, ethics approval letters, permissions for reused material, datasets, appendices, code notes, or other supporting evidence.

Submissions that do not include the required information may be returned to the corresponding author for correction before peer review begins.

03 Reporting Standards

Authors should use the reporting framework that best matches their study design. Systematic reviews and meta-analyses should follow PRISMA or an equivalent recognized checklist. Empirical modelling studies, simulation research, algorithm-benchmarking work, data-driven studies, and computational experiments should follow an appropriate structured reporting checklist or provide enough methodological detail to support transparency and reproducibility.

The completed checklist, when applicable, should be uploaded as a supplementary file. Clear reporting improves the reliability, discoverability, and reusability of published work in mathematical modelling and artificial intelligence.

04 Preparing the Manuscript

All manuscripts must be prepared in clear academic English and organized according to the article type. The main manuscript should be blinded for review, while author-identifying information must appear only on the separate title page.

Title Page

The title page is a separate file that records the full identifying and administrative information for the submission. It should include:

• Article title: A concise and informative title that accurately reflects the manuscript's contribution.
• Author information: Full name, department, institution, city/country, email address, and ORCID identifier where available. Mark the corresponding author with an asterisk and provide a complete correspondence address.
• Disclaimers: Any statement clarifying that the views expressed are those of the authors and not necessarily those of their institutions, where relevant.
• Funding and support: Details of grants, institutional support, computing resources, software access, equipment, or other assistance that enabled the research.
• Acknowledgments: Recognition of individuals or organizations that contributed but do not meet authorship criteria.
• Consent to publish: Required if the manuscript includes identifiable material, third-party content, or data requiring permission.
• Ethics approval: Required for studies involving human participants, surveys, interviews, personal data, or sensitive datasets.
• Registration or pre-registration number: Required where the study was registered or pre-registered.
• Word count: Main-text word count excluding abstract, references, figures, and tables.
• Tables and figures: Number of tables and figures.
• Cover letter: A brief statement to the Editor-in-Chief explaining originality, relevance, scope fit, and confirming that the work is not under consideration elsewhere.

Abstract and Keywords

The abstract should summarize the work accurately and should be followed by 3-5 keywords that support indexing and discoverability. Authors should use technical keywords that reflect the core mathematical, computational, or AI contribution of the manuscript.

• Original Research Articles: Structured under Background, Methods, Results, and Conclusion.
• Systematic Reviews and Meta-Analyses: Structured under Background, Methods, Results, and Conclusion, with registration details where available. If not registered, state that clearly.
• Review Articles: May use a structured or unstructured format depending on the nature of the review, but the purpose, coverage, and main conclusions must be clear.
• Applied Case Studies / Technical Case Reports: Usually structured under Background, Case Description or Technical Context, Results or Outcome, and Conclusion.
• Other manuscript types: May use an unstructured abstract, provided it gives a concise overview of the content and contribution.

05 Manuscript Sections

Methods May Include

• Study setting, such as laboratory, simulation environment, cloud platform, benchmark suite, institutional setting, or industry context.
• Timeframe for data collection, experiments, simulations, model training, or evaluation.
• Study design, including simulation study, computational experiment, comparative algorithm analysis, empirical investigation, or applied modelling study.
• Data sources, including datasets, repositories, benchmark corpora, observational records, surveys, generated data, or synthetic data.
• Eligibility criteria for datasets, samples, records, participants, or input cases.
• Objectives and metrics, including primary and secondary objectives, target outputs, performance measures, or evaluation indicators.
• Technical environment, including programming languages, packages, libraries, solvers, frameworks, version numbers, and source details.
• Model details, including assumptions, parameters, hyperparameters, architecture, solver settings, numerical constants, convergence criteria, and training or validation procedures.
• Ethics approval details where the study includes human participants, survey responses, identifiable information, or sensitive datasets.

Established methods should be cited properly. New or modified methods must be explained clearly, with justification, assumptions, and limitations. Statistical or computational evaluation should include measures of uncertainty, robustness, or error where relevant.

Results

The Results section should present findings in a logical order and should be directly connected to the objectives and methods. Begin with the most important results, followed by secondary or supporting findings.

• State primary and secondary findings clearly.
• Use text to summarize key results and use tables or figures for detailed values, comparisons, model outputs, or visual explanations.
• Report absolute values together with percentages, normalized measures, accuracy scores, or other derived statistics where appropriate.
• Do not repeat the same data in both a table and a figure unless there is a strong reason.
• Use subheadings such as Baseline Data, Core Findings, Model Performance, Comparative Analysis, Robustness Evaluation, or Practical Implications where helpful.
• Submit extended datasets, supplementary tables, code notes, or technical appendices as supporting material when they are too detailed for the main text.

Technical terms such as significant, correlated, randomized, convergent, efficient, robust, or generalized should be used only when supported by the study design and analysis. When relevant, results should be stratified by dataset, model architecture, parameter setting, experimental condition, or subgroup.

Discussion and Conclusion

The Discussion should begin with a concise interpretation of the main findings and explain how they contribute to mathematical modelling, artificial intelligence, computational methods, or the manuscript's specific application area.

• Explain what is new about the work and how the findings compare with prior studies, established theories, known benchmarks, or existing models.
• Discuss methodological, theoretical, computational, or practical implications without overstating the findings.
• Identify limitations clearly, including dataset size, data quality, assumptions, computational constraints, model generalizability, reproducibility issues, or restricted access to resources.
• Suggest realistic directions for future research, such as testing on new datasets, expanding to additional problem classes, improving model robustness, or conducting larger-scale evaluations.

The Conclusion should provide a focused statement of the study's overall contribution in relation to its aims. Authors should not claim real-world impact, performance superiority, or practical benefit unless these claims are supported by formal and reproducible evidence.

06 References

General Principles for Citing Sources

• Cite original and reliable sources wherever possible.
• References should support the manuscript's claims and should not be used for unnecessary self-promotion or citation manipulation.
• Avoid predatory, non-credible, or irrelevant sources.
• Keep the reference list focused and relevant rather than unnecessarily long.
• Accepted but unpublished work may be cited as in press or forthcoming.
• Submitted but unaccepted work should be cited only when essential and only with written permission, normally as unpublished observations.
• Personal communications should be used sparingly and only when the information cannot be obtained from public sources.
• Datasets, software, repositories, and code archives should be cited using persistent identifiers such as DOI, accession number, or stable repository link whenever available.

Citation Style and Formatting

JMMAI uses a numbered Vancouver-style reference format adapted for mathematics, computation, and artificial intelligence scholarship.

• Number references consecutively in the order in which they first appear in the manuscript.
• Place citation numbers in square brackets after the relevant punctuation.
• List up to six authors; for more than six authors, list the first six followed by et al.
• Use consistent journal title abbreviations where applicable.
• Provide complete bibliographic information so that readers can locate every source.
• Use persistent identifiers for datasets, software, and repositories where available.

Reference Examples

07 Tables, Figures, Units and Symbols

Units of Measurement

• Use SI units or standard decimal multiples for measurable physical quantities in applied modelling studies.
• Define computational and performance metrics clearly, including runtime, memory use, accuracy, precision, recall, F1-score, error rate, convergence tolerance, or loss values.
• Report statistical values with suitable measures of precision, such as confidence intervals, standard errors, uncertainty intervals, or error margins where applicable.
• When domain-specific or non-SI units are necessary, provide SI equivalents in parentheses where useful.
• Maintain consistent notation and units throughout the manuscript.

Abbreviations and Symbols

• Use only standard and widely understood abbreviations.
• Avoid abbreviations in the title and abstract unless they are universally recognized.
• Spell out each abbreviation at first use in the main text, followed by the abbreviation in parentheses.
• Common units such as kg, ms, GB, or MB do not need expansion.
• Mathematical notation and symbols should be defined at first use and applied consistently throughout the manuscript, tables, and figures.

Consistent terminology and notation help readers evaluate the manuscript accurately and support uniform presentation across JMMAI publications.

08 Types of Manuscripts

JMMAI welcomes a range of manuscript categories that support scholarly exchange across mathematical modelling, artificial intelligence, machine learning, optimization, computational mathematics, applied statistics, data science, and related interdisciplinary fields.

• Editorials: Normally invited by the editorial board and focused on emerging methods, debates, standards, or directions in the journal's field.
• Original Research Articles: Full-length reports of original theoretical, computational, empirical, or applied findings. These require a structured abstract, 3-5 keywords, and clearly labelled Introduction, Methods, Results, Discussion, and Conclusion sections.
• Review Articles: Systematic reviews, meta-analyses, scoping reviews, and narrative reviews. Systematic reviews should follow a recognized framework such as PRISMA where applicable.
• Brief Reports / Short Communications: Concise original studies or early findings, generally limited to approximately 1500 words, up to three figures or tables, and 15-20 references.
• Applied Case Studies / Technical Case Reports: Reports of unusual, instructive, or practically important modelling, AI, algorithmic, or computational scenarios, generally limited to 1000-1200 words with an abstract, keywords, introduction, case or technical description, discussion, and conclusion.
• Letters to the Editor: Brief comments on recently published JMMAI articles or important issues within the journal's scope, usually limited to 500 words and five references.
• Images, Data Visualizations, or Quiz-style Submissions: Annotated diagrams, visual workflows, model outputs, or algorithmic illustrations with explanatory notes, usually limited to 500 words and five references.
• Innovation and Technique Articles: Descriptions of novel methods, models, tools, algorithms, workflows, or computational techniques with evidence of relevance and effectiveness.
• Quality Improvement or Applied Practice Reports: Structured reports on improving research workflows, computational processes, modelling pipelines, or AI system development practices.

All submissions must follow the journal's formatting, ethical, and review requirements. Manuscripts will undergo peer review unless the editorial policy for a specific category states otherwise.

09 Manuscript Submission and Authorship

Manuscript Submission

Manuscripts should be submitted through the JMMAI online submission system. Authors should ensure that all required files are uploaded, metadata is complete, and declarations are included before final submission.

Recommended submission link: https://jmmaijournal.com/index.php/jmmai/submission

Files should be prepared in an editable format unless the submission system requests otherwise. Figures, tables, supplementary files, datasets, or appendices should be uploaded in the appropriate file category. Authors should also comply with any file-size limits shown in the submission portal.

Authorship Criteria

JMMAI expects authorship to reflect genuine intellectual contribution and accountability. Each manuscript should include a clear author contribution statement.

• An author must have made a substantial contribution to the conception or design of the study, or to the acquisition, analysis, modelling, computation, interpretation, or validation of data or results.
• An author must have participated in drafting the manuscript or revising it critically for important intellectual content.
• An author must approve the final version submitted for publication and accept responsibility for the integrity of the work.

Funding acquisition, general supervision, administrative support, data collection alone, or suggesting a topic does not automatically justify authorship. Contributors who do not meet the authorship criteria should be acknowledged with their consent.

Honorary, guest, and ghost authorship are not acceptable. Any authorship dispute will be handled in accordance with recognized publication ethics guidance, including COPE principles.

10 Copyright, Plagiarism, Archiving and Privacy

Copyright

By submitting a manuscript, authors grant JMMAI the right of first publication. The final copyright and licensing terms applicable to published articles are stated in the journal's copyright notice.

Plagiarism

JMMAI does not accept plagiarism, text recycling without proper disclosure, unattributed use of another person's work, fabricated citations, manipulated data, or any form of research misconduct. If plagiarism or related misconduct is suspected, authors may be asked for an explanation and supporting evidence. Editorial action will be guided by the seriousness of the issue and by established publication ethics procedures.

Archiving Policy

JMMAI supports long-term preservation and continued access to its published content. The journal encourages archiving through recognized digital preservation systems, institutional repositories, library archives, and web-archiving services where applicable.

This preservation approach helps ensure that published articles remain discoverable and accessible even if the journal website, server, or platform changes over time.

Post-Publication Sharing

Authors are encouraged to share the published version of their JMMAI article through personal websites, institutional repositories, academic networking platforms, scholarly profiles, and professional channels, provided that the sharing complies with the journal's license terms and proper citation is given.

Responsible sharing can increase visibility, readership, collaboration, and citation impact within the academic and professional community.

Copyright Notice

Authors publishing in the Journal of Mathematical Modelling and Artificial Intelligence retain copyright over their work while granting the journal the right of first publication. Published articles are made available under the license stated by the journal, such as a Creative Commons license, and authors must ensure that any third-party material included in the manuscript is properly credited and permitted for publication.

Authors are encouraged to share their published work through personal, institutional, and scholarly platforms in a manner consistent with the applicable license, because responsible dissemination supports broader visibility and academic impact.

Privacy Statement

The Journal of Mathematical Modelling and Artificial Intelligence respects the privacy of authors, reviewers, editors, and readers. Names, email addresses, affiliations, and other personal information submitted through the journal website will be used only for the journal's stated purposes, including manuscript processing, peer review, editorial communication, publication, and journal updates.

Personal information will not be disclosed to unrelated third parties for any unrelated purpose. JMMAI maintains confidentiality in editorial and review processes and expects all users of the platform to handle personal information responsibly.

11 Additional Article Category Guidelines

Applied Case Study / Technical Case Report Guidelines

JMMAI considers case-based submissions that describe novel, unusual, or practically valuable applications of mathematical modelling, artificial intelligence, machine learning, computational workflows, optimization, or data-driven methods. When a case-based format is used, the title should clearly indicate the nature of the work, such as a technical case report or an applied case study.

The case description should provide the relevant technical or organizational context, timeline, problem statement, stakeholders or systems involved, methods adopted, tools used, interventions implemented, and outcomes achieved. Supporting material such as model outputs, logs, performance metrics, workflow diagrams, or technical documentation may be included as appendices where appropriate.

The Discussion should critically evaluate the decisions and methods used in the case against relevant theory, standards, benchmarks, best practices, or comparable examples in the literature. Authors should cite recent and relevant literature and should avoid relying mainly on outdated sources unless they are historically or theoretically essential.

Innovation and Technique

JMMAI welcomes manuscripts that introduce new methods, models, tools, algorithms, computational techniques, or applied workflows within mathematical modelling and artificial intelligence. These manuscripts should emphasize originality, methodological clarity, practical relevance, and evidence of effectiveness.

Authors should describe the design and development of the innovation, the theory or logic behind it, the context in which it was tested or applied, and the gap it addresses. Reliability, robustness, limitations, risks, and potential areas for improvement should be discussed. Where available, preliminary results, benchmark comparisons, validation data, or use-case evidence should be included.

Quality Improvement Project

JMMAI also considers Quality Improvement Projects that aim to improve research workflows, modelling processes, AI development pipelines, data-management practices, software reliability, reproducibility, or computational efficiency.

Such manuscripts should define the problem, present baseline information, describe the intervention or improvement strategy, explain the methodology used, and report outcomes using appropriate metrics. Authors should address sustainability, stakeholder involvement, barriers encountered, and the extent to which the improvement can be replicated or adapted elsewhere.

Articles - Section Policy

The standard Articles section applies to full-length original research and other manuscript types accepted within JMMAI's scope. All manuscripts submitted to this section must comply with the journal's ethical, formatting, authorship, reporting, and peer-review requirements.