Editorial Policies

Focus and Scope

The main aim of the International Journal of Artificial Intelligence (ISSN 0974-0635) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Artificial Intelligence (IJAI) is a peer-reviewed online journal and is published in Spring and Autumn i.e. two times in a year.


Section Policies


Checked Open Submissions Checked Indexed Checked Peer Reviewed

Peer Review Process

The practice of peer review is to ensure that high quality scientific material is published, therefore the peer review is one of the most objective processes of the International Journal of Artificial Intelligence (IJAI). Our referees play a vital role in maintaining the high standards of IJAI.  At present 12 weeks’ time given to the reviewer for reviewing. 

The Editors-in-Chief first evaluate all manuscripts. Although it is an extremely rare occurrence, the Editors-in-Chief may accept an exceptional manuscript at this first stage. The Editors-in-Chief may also reject a manuscript at this stage because it is insufficiently original, it has serious scientific flaws, it is ungrammatical, it is written in poor English, or it falls outside the aims and scope of the journal. Those that meet the minimum criteria are passed on to an Editor or to an Associate Editor to manage the review process. The manuscripts are reviewed by minimum two reviewers who are experts in the area of Artificial Intelligence.



Publication Ethics and Malpractice Statement:

All journals of Centre for Environment & Socio-Economic Research Publications™ support and adopted “Publication ethics and malpractice” which are based on COPE’s Best Practice (http://publicationethics.org/) and Elsevier PERK (http://www.elsevier.com/editors/perk).

The publication of an article in a peer-reviewed journal is an essential building block in the development of a coherent and respected network of knowledge. It is a direct reflection of the quality of the work of the authors and the institutions that support them. Peer-reviewed articles support and embody the scientific method. It is therefore important to agree upon standards of expected ethical behaviour for all parties involved in the act of publishing: the author, the journal editor, the peer reviewer, the publisher and the society.

Any complaints regarding any material published in the journal should be directly sent to the Editor-in-Chief.


Reporting standards

Authors of reports of original research should present an accurate account of the work performed as well as an objective discussion of its significance. Underlying data should be represented accurately in the paper. A paper should contain all the references to permit others to locate and consult the sources on which the work is based. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable.

Originality and plagiarism

The authors should ensure that they have written entirely original works, and if the authors have used the work and/or words of others that this has been appropriately cited or quoted. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable.

Multiple or concurrent publication

An author should not in general publish manuscripts describing essentially the same research in more than one journal or primary publication. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Publication of some kinds of articles (e.g. translations) in more than one journal is sometimes justifiable, provided certain conditions are met. The authors and editors of the journals concerned must agree to the secondary publication, which must reflect the same data and interpretation of the primary document. The primary reference must be cited in the secondary publication.

Acknowledgement of sources

Proper acknowledgment of the work of others must always be given by means of notes written according to bibliographical standards. Information obtained privately, as in conversation, correspondence, or discussion with third parties, must not be used or reported without explicit permission from the source, and the acknowledgement should be made clearly. Information obtained in the course of confidential services, such as refereeing manuscripts or grant applications, must not be used without the explicit written permission of the author of the work involved in these services.

Authorship of the paper

All those who have made significant contributions to the paper should be listed as co-authors.

Appeal against the editorial decision

The authors have the right to appeal against any editorial decision. A statement with rebuttal should be sent directly to the Editor-in-Chief.


Editors should take all reasonable steps to ensure the quality of the edition. Editors’ decisions to accept or reject a paper for publication should be based only on the paper’s importance, originality, clarity, and the relevance to the artificial intelligence theory and applications area.

Publication decisions

The editors of concern journal are responsible for deciding which of the articles submitted to the journal should be published. In doing so, they follow the policy established by the Centre for Environment & Socio-Economic Research Publications™. The validation of the work in question and its importance to researchers and readers must always drive such decisions.

Peer review

All submitted papers are subject to strict peer-review process. The practice of peer review is to ensure that high quality scientific material is published, therefore the peer review is one of the most objective processes of the our Journal. Our referees play a vital role in maintaining the high standards of our Journal.

Fair play

The editors should give manuscripts for evaluation with regard to their intellectual content without regard to race, gender, sexual orientation, religious belief, ethnic origin, citizenship, or political philosophy of the authors.

The confidentiality of the peer-review process

All editors should ensure that material submitted to the journal remains confidential while under review.

Conflicts of interest

Editors will make fair and unbiased decisions independent of commercial considerations, and should ensure a fair and appropriate peer-review process. Editors will recuse themselves (i.e. should ask a co-editor, associate editor or other member of the editorial board instead to review and consider) from considering manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors or institutions connected to the papers. When deciding upon the reviewers, editors will take in consideration any risk of conflict of interest.

Unethical publishing

When ethical complaints have been presented concerning a submitted manuscript or published paper, or when they receive notice of the questionable publishing behavior, the editors will discuss and take all the appropriate measures to investigate the claim, even if it is discovered years after publication.


Contribution to editorial decisions

Peer-review assists the editor in making editorial decisions and through the editorial communications with the author may also assist the author in improving the paper. Peer-review is an essential component of scholarly communication, and lies at the heart of the scientific method. Centre for Environment & Socio-Economic Research Publications™ shares the view of many that all scholars who wish to contribute to publications have an obligation to do a fair share of reviewing.


Any selected referee who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the editor and excuse himself from the review process.

Disclosure and conflict of interest

Unpublished materials disclosed in a submitted manuscript must not be used in a reviewerís own research without the express written consent of the author. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.


Any manuscripts received for review must be treated as confidential documents. They must not be shown to or discussed with others except as authorized by the editor.

Standards of objectivity

Reviews should be conducted objectively. Personal criticism of the author is inappropriate. Referees should express their views clearly with supporting arguments.

Acknowledgement of sources

Reviewers should identify relevant published work that has not been cited by the authors. Any statement that an argument had been previously reported should be accompanied by the relevant citation. A reviewer should also call to the editor's attention any substantial similarity or overlap between the manuscript under consideration and any other published paper of which they have personal knowledge.



Inverse Problems can be found in different branches of science. Many of them are related to estimating parameters in statistical models; for instance, a common practice is to tune parameters in partial differential equations or improve forecasts in sequential and variational data assimilation methods, all via noisy observations. Regardless of the context, many issues are raised during the estimation: the impact of sampling noise during the analysis, the non-linear relationship between observations and model variables, the computational cost of running numerical models, and the estimation of prior errors, among others. Machine Learning has become a powerful tool for parameter and state estimation in numerical models. In this context, we can find a pool of models that provide accurate estimates when combined with other information sources, for instance, numerical models. Besides, these methods can be employed to abstract error statistics from data and build statistical models that mimic the actual behavior of natural phenomena. In general, Machine Learning methods can be exploited in any context wherein complex relationships in variables (parameters or states) arise from data.

List of Topics

The main topics of interest of this special issue are (but are not restricted to):

  1. Data Assimilation

  2. Bayesian Inference

  3. Data-Driven Models

  4. Uncertainty Quantification

Important Dates

  • Paper Submission: June 30, 2022.

  • Acceptance/Rejection Notification: July 15, 2022.

  • Camera Ready: July 30, 2022.

  • Special Issue: Autumn, 2022.

Guest Editor

Elias D. Nino-Ruiz, Ph.D.

Associate Professor, Department Chair

Department of Computer Science

Universidad del Norte

Barranquilla, Colombia

Tel: (+57) 5 3509509 Ext. 3261


Website:  https://aml-cs.github.io/

 ORCID : http://orcid.org/0000-0001-7784-8163

Twitter: https://twitter.com/elias_david_84


Ph.D. in Computer Science and Applications, Virginia Tech, Blacksburg, VA 24060, USA

M.Sc. in System Engineering, Universidad del Norte, BAQ, Colombia

M.Sc. in Industrial Engineering, Universidad del Norte, BAQ, Colombia

Director of Applied Math & Computer Science Lab (AML-CS) - https://aml-cs.github.io/





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