Any complaints that authors wish to lodge should be directed to the Editor-in-Chief of Computational Intelligence and Machine Learning Journal at editor@technoarete.org

Every one of the guidelines detailed below is part of the Authorship Principles put forth by the editorial committee of Computational Intelligence and Machine Learning , that all authors who intend to have work published have to adhere to, in order to ensure stable and secure authorship practices.

The Computational Intelligence and Machine Learning Journal assumes that all authors -

declare complete ownership of the contents of their research work,

offer explicit consent to the publication of their work,

have obtained all due permissions and approvals from their institutions, organizations, and facilities, where they carried out their research work, for the purposes of publication, before actually proceeding to submit their work to Computational Intelligence and Machine Learning Journal.

The Computational Intelligence and Machine Learning Journal refrains from dictating what sort of research, analytical work, case studies, reports, etc, that justify authorship, which is why it is advised that authors pay close attention to and work according to the standards of authorship currently followed in every stream of research within Computational Intelligence and Machine Learning Journal. In the absence of such standards for authorship, it is advised that one follows the guidelines listed below.

It is understood that all authors who list their names on their manuscripts stating that they have authored the contents therein upon submitting them to Computational Intelligence and Machine Learning -

have contributed significantly in -

the conceptualization and designing of the research output,

procurement, investigation and explanation of the data therein, or

the production of the new and unique software application, code, or algorithm utilized in conducting the research.

have composed, edited and refined the research output in a manner that is fit for intellectual consumption.

have consented to the fact that the version of the submitted manuscript is ready for publication.

have consented to be held responsible for every element of the work carried out, offering assurance of its reliability, authenticity, and up-to-dateness.

Authors submitting their work for reviewing, with the intention of getting them published in the Computational Intelligence and Machine Learning Journal, are implored to ensure that any and all forms of data, reference materials, citations, software, coding, applications, algorithms, programs, etc, that support the assertions within their research work, are in total compliance with all relevant regulatory requirements and guidelines. It is highly advised that you go through all of our guidelines for the sharing of research data, in order to secure complete compliance.

The Computational Intelligence and Machine Learning Journal devoted to sustaining its standards of quality by ensuring that the integrity of all the scientific output it publishes, is nothing short of being exemplary and edifying for readers.

In accordance with these standards, it is highly advised that authors abstain from falsifying research outcomes that could prove to be damaging to the reputability of the Computational Intelligence and Machine Learning Journal as well as the overall effort of our personal, by upholding all the values of being a thorough professional and scientific author.

Ensuring the all-round integrity of one's research work, its outcomes, and their portrayal can be secured by adhering to certain guidelines for exemplary scientific practice, such as -

submitting a certain manuscript to not more than one journal at a time.

submitting work that has been thoroughly self-vetted for its originality, meaning that it hasn't been published in any other form, journal or language, (either in part or completely), except if the work is a continuation of research that has been already published and has entirely brand new/fresh insights to offer. (in cases where material has been reused in some form or another, authors are urged to provide further clarity and shed more light on such issues).

submitting work such that the outcomes and conclusions of the research are presented plainly and genuinely, without being manipulated or falsified (this also includes the manipulation of images). All authors are implored to follow specialization-specific guidelines in compiling, demarcating, and preparing data.

submitting work that is completely original where no part of the work (data, text, concepts, ideas, and outcomes) is plagiarized from someone else. Computational Intelligence and Machine Learningreserves the right to make use of its plagiarism tool to verify the authenticity and originality of a manuscript.

submitting work where the names and list of all authors, corresponding authors as well as the order in which they are mentioned are all exact and correct. This is owing to the fact that appending to a list of authors and editing names/lists of authors during the revision process is conventionally not allowed, but might be deemed as necessary only in a few specific cases. The reasons for any appending/editing of the names/lists of authors that need to be made should be clearly stated before a request is made. It is important to note that any requests for appending/editing the names/lists of authors, will not be entertained once a manuscript has been accepted for publication.

The Computational Intelligence and Machine Learning Journal utilizes the Turnitin software application for the purposes of identifying any occurrences of repeated and plagiarized text within manuscripts that have been submitted for review. The Turnitin software performs comprehensive checks of the text of manuscripts, comparing the text against a broad reference base consisting of all existing publications, every popular article and journal database, as well as the entire Web in general. Upon successfully comparing the text of a manuscript with all these references, it produced a report which highlights the percentage of text that has been plagiarized and the percentage of text that is completely genuine. In cases where the Turnitin software detects plagiarism, it also provides the exact source from which the text was copied. This helps the editorial committee further examine the report and the suspected plagiarism to verify if the claims of the application are indeed factual.

It is a well-known fact that all academic and scholarly manuscripts are composed only after the careful analysis and reviewing of previously published work. As a result, it might be hard to define the borders between definite and distinct plagiarism and permissible referrals. This is why the below-listed guidelines have been formulated to offer a clear idea of how text can be identified and categorized as being plagiarized.

The following can be categorized as being plagiarized text -

words, sentences, concepts, and research outcomes that have been used without adequate citations, and that are completely identical to someone else's work.

● recycling text (also referred to in the industry as self-plagiarism) which involves the use of an author's own work from another publication elsewhere, without any adequate citations and recognition of the original source.

● inadequate and scant paraphrasing which involves the lifting of entire paragraphs from another source, without altering the structure of the sentences comprehensively or altering the structure but not replacing words.

● lifting entire sentences without placing them in quotation marks, and failing to cite the original sources.

● offering adequate citations and recognition of original sources, but failing to paraphrase sentences appropriately or placing them within quotation marks is also categorized as involuntary plagiarism. Likewise, text, where sentences are paraphrased in part and quoted in part, are also categorized as involuntary plagiarism. The norm is to either paraphrase such text completely or to place the entire text within quotation marks, while also making offer citations of the original sources.

● stark similarities in the text within different parts of a manuscript, such as the -

abstract,

introduction,

materials and methods utilized,

discussion, and

conclusion,

are categorized as plagiarized text. This can easily be avoided by paying careful attention to the text and paraphrasing frequently used sentences and using synonyms for frequently used words. Albeit, some technical terms and jargon cannot be altered. The editorial committee of Computational Intelligence and Machine Learning is well aware of such complications and will make concessions in these cases.

Manuscripts that have been found to contain plagiarized text post-publication, will be removed from the Computational Intelligence and Machine Learning Journal website after due investigation by the editorial committee and confirmation from the journal's Editor-in-chief.
Subsequently, a 'Notice of Retraction' along with a link to the original source will be appended to the retraction notification in the issue in which the work was originally published.

All manuscripts that have been deemed as acceptable by the editorial committee will be published on the Computational Intelligence and Machine Learning Journal website and will be made available to all. This is owing to the fact that Computational Intelligence and Machine Learning is an open-access journal, which ensures that wide-spread propagation of all published work. Authors will be required to clearly specify if they prefer to pay to have their published work made available to readers for free, or not.