Submissions

Author Guidelines

Guideline Policy

The International Journal of Computational Intelligence and Emerging Technologies considers manuscripts that align with ethical publication practices as outlined by the International Committee of Medical Journal Editors (ICMJE). Authors are strongly encouraged to adhere to these standards to uphold scholarly integrity, transparency, and academic rigor.

These standards cover recommendations for manuscript structure, authorship eligibility, disclosure of conflicts of interest, and ethical considerations for all academic publications.

General Guidelines of Manuscript Preparation

All manuscripts submitted to International Journal of Computational Intelligence and Emerging Technologies must comply with the following guidelines to ensure ethical conduct and a smooth peer-review process:

Conflict of Interest Form: Each submission must include a completed ICMJE Conflict of Interest Form.

Blinded Manuscript: The main manuscript must be anonymized by removing all author identifiers to maintain double-blind peer review integrity.

Title Page: Submit a separate title page containing all author details, institutional affiliations, email addresses, and acknowledgments, along with a covering letter.

Author Metadata: Accurate metadata for all authors (name, ORCID ID, affiliations, and email) must be submitted through the online system.

Supplementary Documents: May include:

Authorship Declaration Form

Consent to Publication (if applicable)

IRB Approval Certificate (if applicable)

Copyright Permissions (if applicable)

Non-compliant submissions may be returned for correction prior to the review.

Downloads

Instructions to the Authors

Authorship Declaration Form

Consent to Publish Form

Title Page Template

Reporting Guidelines

International Journal of Computational Intelligence and Emerging Technologies promotes adherence to established reporting standards based on research type. Authors must submit the appropriate checklist as supplementary material:

CONSORT – for randomized controlled trials

STROBE – for observational studies

PRISMA – for systematic reviews and meta-analyses

STARD – for diagnostic accuracy studies

CARE – for case reports

Manuscript Preparation

Title Page

Should be submitted separately and must include:

Article Title: Concise and informative.

Author Information: Names, departments, affiliations, emails, and ORCID IDs. Mark the corresponding author with an asterisk (*).

Disclaimers (if any)

Sources of Support: Grants, institutional funding, etc.

Acknowledgments

Consent to Publication Statement (if applicable)

IRB Approval Status and Number (if required)

Trial Registration Number (for RCTs)

Word Count

Number of Figures and Tables

Covering Letter: Addressed to the Editor-in-Chief

Abstract

Structured abstracts are mandatory for the following article types:

Original Research Article: Background, Methods, Results, Conclusion + 3–5 Keywords

Systematic Review / Meta-analysis: Same as above, with Trial Registration Number (if applicable)

Case Series: Background, Results, Conclusion + Keywords

Case Reports: Background, Case Presentation, Conclusion + Keywords

Other Types: Unstructured abstract with keywords

Introduction

Clearly state the research background, relevance to computational intelligence or emerging technologies, theoretical gaps, and objectives. Avoid presenting results or conclusions here. Use 3–5 core references to justify context.

Methods

Detail research design, tools, settings, timeframe, data sources, instruments (with make and model), and inclusion/exclusion criteria. Address:

Software used (e.g., MATLAB, Python, TensorFlow)

Algorithmic frameworks or simulations

Dataset access and processing

Validation or benchmarking procedures

IRB approval (for human/animal studies)

Include statistical tools, significance levels, and performance metrics such as precision, recall, accuracy, F1-score, AUC, etc., depending on the study.

Justify lesser-known or novel techniques and mention any limitations.

Results

The Results section should present findings in a logical and clear sequence, supported by tables, figures, and textual summaries. Begin with the most significant outcomes and follow with secondary or supporting results. The information must align with the objectives and outcomes described in the Methods section.

Present primary and secondary findings explicitly.

Avoid redundancy by summarizing data in the text while presenting full details in well-organized tables and figures.

Use absolute numbers alongside derived statistics (e.g., percentages or ratios) to provide clarity and allow for verification.

Do not repeat data across tables and figures—choose the format that most effectively conveys the findings.

Where applicable, results may be organized under subheadings, such as:

Dataset Description and Characteristics: Outline key features of the datasets used, including sample sizes, data distributions, and relevant technical properties.

Model Performance Metrics: Present outcomes such as accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, etc., for the algorithms applied.

Comparative Analysis: Compare performance across multiple algorithms, architectures, or feature sets.

Ablation Studies: Report on the influence of different components or hyperparameters on system performance.

Supplementary information, extended data tables, or technical appendices may be submitted as supporting documents, particularly when too detailed for inclusion in the main text but essential for transparency.

Use graphs or charts when they offer a clearer visual representation than tables, and avoid duplication of data between visual formats. Refrain from non-technical use of statistical terms. Use terms such as "statistically significant," "correlation," or "randomized" only where appropriately supported by methodology. If applicable, stratify results by relevant technical or contextual variables (e.g., dataset type, network architecture, feature subset), unless a clear rationale is provided for not doing so.

Discussion & Conclusion

The Discussion section should begin with a concise summary of the key findings of the study, highlighting the most significant results and how they contribute to the existing body of knowledge in computational intelligence or emerging technologies. Authors are encouraged to explore potential mechanisms or algorithmic decisions that may explain these findings.

Emphasize the novel contributions and how the results compare with or diverge from existing literature.

Interpret findings in light of relevant computational theories, frameworks, or experimental benchmarks.

Critically analyze practical, technological, or scientific implications.

Authors should clearly state the limitations of their study—be it data limitations, algorithmic constraints, computation time, or generalizability—and how these might influence the interpretation of the findings. Where applicable, discuss the role and potential influence of system architecture, parameter tuning, dataset diversity, or external hardware/software constraints.

Avoid repeating detailed data already presented in the Results section. Instead, use this space to offer a meaningful interpretation, link findings to the study objectives, and explore directions for future research, such as:

Application to different domains (e.g., healthcare, finance, smart cities)

Scalability to real-world systems

Enhancements using deep learning, reinforcement learning, or hybrid models

Use of federated learning or edge computing frameworks

In the Conclusion, provide a concise and clear statement that reflects the overall significance of the findings, aligned with the study’s aims. Avoid overstating results or drawing conclusions not directly supported by the data. Differentiate between statistical significance and practical technological relevance. Avoid making claims about cost-efficiency or commercial viability unless supported by a formal analysis.

Lastly, refrain from implying publication precedence or referencing unpublished or incomplete work. Any new hypotheses should be proposed explicitly and identified as exploratory to encourage further investigation.

References
General Considerations Related to References

Authors are expected to provide accurate and verifiable references to original research sources to maintain the scientific integrity and traceability of scholarly contributions. All cited literature must directly relate to the study and support the claims or findings presented in the manuscript.

References must not be used for promotional purposes or to disproportionately cite the authors' own prior work unless clearly justified by relevance. The journal discourages self-citation beyond a reasonable extent and prohibits citation of articles from predatory or non-peer-reviewed sources.

Review articles may be cited for contextual understanding; however, primary research articles should be referenced whenever possible to ensure that interpretations are based on original findings.

To uphold citation quality, authors should:

Avoid overly exhaustive bibliographies.

Prioritize key studies and foundational work relevant to the research.

Include additional references as supplementary material, if necessary.

Ensure proper credit and permissions for cited unpublished work, datasets, or personal communications.

For unpublished or informal sources:

Articles accepted but not yet published should be cited as “in press” or “forthcoming.”

Manuscripts submitted but not accepted must be referenced as “unpublished observations,” with explicit author permission.

Personal communications should be cited only when absolutely necessary and must include date and consent of the communicator.

Persistent identifiers (e.g., DOI) should be cited for datasets and software used in the study, where available.

References must be cited in sequential order of appearance in the manuscript using Arabic numerals in square brackets (e.g., [1], [2–4]) and formatted according to the journal’s reference style.

Reference Style and Format

The International Journal of Computational Intelligence and Emerging Technologies adheres to the Vancouver referencing style, in line with ICMJE recommendations and the National Library of Medicine’s Citing Medicine, 2nd edition.

General Guidelines

Number references consecutively in the order of their first appearance in the manuscript.

Reference numbers should be placed in square brackets after punctuation. Example: Machine learning improves pattern recognition in biomedical imaging. [1]

Use "et al." after the sixth author when listing contributors in citations.

Avoid citing non-peer-reviewed or predatory sources.

Cite datasets and software tools using persistent identifiers where applicable.

Clearly distinguish between “in press” and “unpublished observations,” with proper permissions.

Reference Examples

Article in a journal:
Smith TJ, Kumar R, Nguyen T, Li M, Rao S, Tanaka K, et al. Deep learning-based cancer diagnosis using histopathological images. IEEE Trans Med Imaging. 2021;40(5):1231–42.

Journal supplement:
Chen Y, Park JH, Goldstein R, et al. Advancements in neuro-symbolic integration. J Artif Intell Res. 2020;45(Suppl 2):88–101.

In press article:
Zhao L, Patel D. Intelligent control in autonomous robotics. Robotics Today. In press.

Published abstract:
Al-Rashid F, Coleman M, Jha R. Quantum-inspired networks for large-scale optimization [abstract]. Proc IEEE Conf Neural Netw. 2022;59:208.

Conference proceedings article:
Gupta S. Generative models for edge AI. In: Proceedings of the 8th International Conference on Smart Systems; 2022 Mar 18–20; Tokyo. Edited by Nair R. New York: Springer; 2022. p. 94–103.

Book chapter:
Tan J, Martinez L. Adaptive fuzzy systems in energy grid management. In: Ghosh D, editor. Computational Intelligence in Infrastructure Systems. 2nd ed. London: Elsevier; 2020. p. 155–78.

Whole journal issue:
Zhang X, Morris D, editors. Advances in computational cognition. Cognit Syst Res. 2019;61:1–98.

Entire conference proceedings:
White C, editor. Proceedings of the 10th International Symposium on Artificial Neural Networks; 2021 Jul 10–13; Berlin. Heidelberg: Springer-Verlag; 2021.

Book:
Rojas R. Neural Networks: A Systematic Introduction. Berlin: Springer; 1996.

Monograph in series:
Jensen ML, Crandall C. Optimization in swarm-based learning. In: Evans B, series editor. Computational Theory Series. Vol 3. Oxford: Academic Press; 2021. p. 77–89.

Institutional publication:
National AI Research Council. Annual Research Review 2022. Washington (DC): NAIRC; 2022.

Thesis:
Hwang J. Reinforcement Learning Algorithms in Multi-Agent Systems [Ph.D. thesis]. Cambridge (UK): University of Cambridge, Department of Engineering; 2021.

Illustrations (Figures)

Figures and illustrations significantly enhance the scientific communication and comprehension of computational models, algorithms, and emerging technologies. Authors are encouraged to submit high-quality visual content that supports and clarifies the manuscript’s key findings.

Image Format & Quality: All figures must be submitted in high-resolution TIFF or JPEG formats. Images should be sharp, clear, and publication-ready. Low-resolution or pixelated graphics will not be accepted. Screenshots or compressed images are discouraged unless absolutely necessary.

Computational Visualizations: For algorithm flowcharts, neural network architectures, simulation outputs, and other computational models, high-resolution images with appropriate labeling and contrast are essential. Diagrams must be annotated accurately and concisely.

Graphical Plots and Charts: Authors should include clear axes, legends, and labels. Ensure that data plots maintain appropriate resolution and do not suffer from compression artifacts.

Figure Numbering: Figures should be numbered sequentially (e.g., Figure 1, Figure 2, etc.) in the order they are cited in the manuscript.

Previously Published Figures: Authors must cite the source and obtain written permission from the copyright holder for any figure reused from a prior publication unless the figure is public domain or under a compatible open license.

Legends for Illustrations: All figure legends should be placed on a separate page in the manuscript file. Legends must be detailed enough to understand the figure without referring to the main text and must explain all symbols, abbreviations, or highlights.

Note: Figures should not be embedded within the manuscript body. Instead, indicate their placement in-text using notes such as [Insert Figure 1 here].

Units of Measurement

All measurements and numerical values must follow international standards for consistency, reproducibility, and clarity.

Metric System: All dimensional values should be reported in the metric system (e.g., meters, kilograms, seconds).

Temperature: Express all temperature values in degrees Celsius (°C).

Numerical Precision: Use consistent decimal precision across similar measurements. For example, do not mix values like 0.75 and 0.752 unless contextually significant.

Computational Benchmarks: Include processing time, hardware specifications (e.g., CPU, GPU, RAM), and software environment (version and platform) for reproducibility.

Data Units: Use standardized units (e.g., MB, GB, FLOPS) for memory, data transfer, and computational throughput. When appropriate, provide conversions in parentheses.

Authors must apply the same unit conventions throughout the manuscript and avoid switching between systems (e.g., mixing imperial and metric units).

Abbreviations and Symbols

To ensure broad comprehension among interdisciplinary readers, authors must apply the following rules regarding abbreviations and symbols:

Standard Abbreviations Only: Use only commonly accepted and well-documented abbreviations. Avoid the use of nonstandard or domain-specific acronyms without definitions.

No Abbreviations in Title and Abstract: To maintain clarity in indexing and database searches, avoid abbreviations in titles and abstracts unless they are universally understood (e.g., AI, IoT).

First Mention Rule: Define abbreviations at first use in the manuscript, e.g., Support Vector Machine (SVM). Thereafter, use the abbreviation consistently.

Units of Measurement Exception: Standard units (e.g., cm, kg, ms) do not require expansion.

Symbols: All symbols must conform to standard scientific or mathematical notation. When introducing new symbols (especially in equations or algorithms), provide explicit definitions in the text or within a notation table.

By adhering to consistent abbreviation and symbol usage, authors enhance accessibility and readability for both technical and cross-disciplinary readers.

Types of Manuscripts

The International Journal of Computational Intelligence and Emerging Technologies accepts a wide array of manuscript types to foster research dialogue and innovation in the fields of artificial intelligence, machine learning, robotics, data science, optimization, and emerging computational systems. The journal seeks contributions that bridge theoretical advances and real-world applications, with emphasis on interdisciplinary approaches and next-generation technologies.

Editorials are generally invited by the editorial board and offer expert opinions or highlight emerging trends, innovations, and research challenges within computational intelligence and technology domains.

Original Research Articles must present novel and impactful findings in areas such as neural networks, evolutionary algorithms, fuzzy systems, deep learning, data mining, quantum computing, smart systems, and IoT. Manuscripts must include a structured abstract (with sections: Background, Methods, Results, Conclusion), 3–5 keywords, and standard sections: Introduction, Methods, Results, Discussion, and Conclusion. The Introduction should clearly state the research problem and objectives. The Methods must detail the computational models, datasets, algorithms, evaluation metrics, and experimental procedures used. Results should be presented clearly with proper visualizations and statistical backing. The Discussion should compare results with existing methods and explain implications. The Conclusion must summarize key findings and research limitations.

Review Articles, including narrative reviews, systematic reviews, and meta-analyses, are welcome and should follow established frameworks like PRISMA where applicable. Reviews should synthesize key developments, identify gaps in literature, and propose directions for future research.

Brief Reports or Case Studies are concise descriptions of specific experiments, algorithmic innovations, or technology implementations, limited to 1500 words, three figures/tables, and 15–20 references.

Application Notes or Technical Reports describe the design, deployment, or performance of computational tools, platforms, or emerging technologies. These manuscripts should focus on implementation details, comparative evaluations, and real-world relevance.

Letters to the Editor provide short commentary or critique of recently published work in IJCIET or highlight urgent technical issues, capped at 500 words and five references.

Case Letters present brief accounts of unique computational solutions, edge-case scenarios, or anomalies observed in practical systems. These do not require an abstract and should not exceed 500 words, five references, and two figures.

The journal also supports submission of Visual Demonstrations or Algorithmic Quizzes, which present graphical content such as flowcharts, code visualizations, or model architectures, accompanied by brief explanatory notes or assessments.

Other accepted formats include Short Communications, Innovative Concepts, Simulation Notes, Expert Dialogues, and Emerging Technology Spotlights. All submissions must meet the journal's ethical, formatting, and peer-review standards.

Manuscript Submission

Manuscripts may be submitted online via the journal's official website (https://ijciet.com/
). Figures and tables may be uploaded as separate files or embedded within the Word document. Each file should not exceed 4MB in size.

Authorship Criteria

The International Journal of Computational Intelligence and Emerging Technologies maintains strict authorship guidelines to ensure integrity and proper attribution. All manuscripts must be accompanied by an authorship contribution statement, even if not published.

An individual qualifies as an author only if they meet all of the following:

Significant contributions to the conception/design of the research, algorithm development, or data analysis.

Active participation in drafting or revising the manuscript for scholarly accuracy and depth.

Approval of the final manuscript version and agreement to take responsibility for the content.

General supervision, funding acquisition, or data provision alone does not justify authorship. Proposals or conceptual suggestions without technical or analytical contribution also do not merit authorship.

All listed authors must review and approve the final submission. Individuals who contributed to the research but do not meet authorship criteria (e.g., technical support, language editing) must be acknowledged with their consent in the Acknowledgments section.

The journal adheres to COPE (Committee on Publication Ethics) principles in resolving authorship or contribution disputes.

Copyrights

By submitting a manuscript, the author(s) transfer the rights of first publication to the editorial office of the International Journal of Computational Intelligence and Emerging Technologies.

Plagiarism

Plagiarism, which constitutes unauthorized use or close imitation of the language and thoughts of another author, is treated as a serious ethical violation. The journal strongly discourages plagiarism in any form. Upon detection of such practices, the authors will be contacted for explanation and correction. All cases will be handled in accordance with the guidelines issued by the Committee on Publication Ethics (COPE).

Archiving Policy

The International Journal of Computational Intelligence and Emerging Technologies is committed to the preservation and permanent accessibility of its published research. To this end, the journal employs the LOCKSS (Lots of Copies Keep Stuff Safe) program, enabling libraries to create permanent archives for restoration and access continuity.

This decentralized preservation ensures the journal’s content remains available even in the event of technical or institutional disruptions. The journal actively supports and encourages archiving in institutional repositories and academic libraries to promote enduring academic access.

Post publication Dispersion

Authors are encouraged to disseminate the final published version of their articles widely across academic and professional platforms. This includes depositing articles in institutional repositories, preprint archives, academic networking sites, or sharing via personal websites and professional networks.

The journal publishes under the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided proper citation of the original work. Authors retain full rights to circulate their work, increasing its visibility and scholarly impact.

Case Report

The International Journal of Computational Intelligence and Emerging Technologies accepts case reports that document unique or significant developments in computational intelligence, machine learning applications, or emergent technologies. Titles must include the phrase “A case report” following a colon.

Each case report must provide detailed background, including context, timeline, involved technologies or algorithms, development process, challenges encountered, interventions undertaken, and observed outcomes. Supporting materials such as code repositories, datasets, model diagrams, or performance evaluations should be referenced or provided in supplementary files.

The discussion must evaluate the case’s significance, aligning with current methodologies, frameworks, or technological trends. Authors should cite recent and relevant literature, ideally limiting references published before 2010 to no more than 20%. All visual content must be submitted in high-resolution JPEG or TIFF formats. Pixelated or low-quality images will not be accepted.

Innovation & Technique

The International Journal of Computational Intelligence and Emerging Technologies invites manuscripts that introduce original methods, models, algorithms, frameworks, or technical solutions within the fields of artificial intelligence, machine learning, data science, robotics, cybersecurity, and other emerging computing paradigms. Submissions in this category must emphasize the novelty, practical application, and measurable impact of the innovation.

Manuscripts should offer a comprehensive and detailed description of the innovation, including its conceptual design, development methodology, theoretical underpinnings (if applicable), and real-world deployment context. Authors are expected to clearly explain how the proposed innovation addresses specific computational or technological challenges, and how it enhances performance, accuracy, efficiency, scalability, or user experience compared to existing approaches.

Considerations of computational reliability, ethical implications, safety, and potential risks or constraints should be transparently discussed. Where applicable, preliminary results, benchmarks, case studies, or implementation details supporting the effectiveness and feasibility of the innovation must be included.

This section aims to promote inventive thinking and support the integration of novel solutions in the evolution of intelligent systems and emerging technologies.

Quality Improvement Project

The International Journal of Computational Intelligence and Emerging Technologies supports the submission of Quality Improvement Projects (QIPs) that aim to enhance the development, deployment, or performance of systems in computational and technological environments. These may include enhancements in algorithmic workflows, software development practices, data handling protocols, user experience, or deployment in real-time or embedded systems.

Manuscripts should identify the specific problem addressed, accompanied by contextual background or baseline measurements. Authors must describe the improvement strategy or intervention employed, methodologies adopted (e.g., Agile, DevOps, CI/CD, Lean, Six Sigma), and measurable results achieved, supported by relevant performance indicators.

Submissions should also highlight the sustainability of improvements, involvement of key stakeholders, and obstacles faced during implementation. Emphasis should be placed on replicability, practical utility, and alignment with current industry or academic standards.

The journal values contributions that promote a culture of systematic enhancement, accountability, and evidence-based innovation in the computational sciences.

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Copyright Notice

Authors publishing in the International Journal of Computational Intelligence and Emerging Technologies retain the copyright of their work and grant the journal the right of first publication. All published manuscripts are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This license allows others to copy, distribute, remix, and build upon the work non-commercially, provided appropriate credit is given to the original authors and the journal, and any derivative works are licensed under identical terms.

Authors are encouraged to freely share their published work through institutional repositories, research-sharing platforms, or personal academic websites, thereby increasing visibility and scholarly collaboration.

By submitting a manuscript to this journal, authors confirm that all necessary permissions and consents for the use of copyrighted material, figures, datasets, or software have been obtained prior to submission.