Computational Intelligence and Machine Learning is a web-based Computational Intelligence and Machine Learning that is committed to publishing and offering the Global community access to the latest research work, exploratory findings and rare case reports, from all over the planet.
In its persistent effort to be a premier Computational Intelligence journal, Computational Intelligence and Machine Learning exercises a strict and rigorous reviewing process to verify the authenticity, up-to-dateness, and novelty of all the research that it publishes.The dedicated editorial and reviewing committee at Computational Intelligence and Machine Learning are tasked with the sole responsibility of maintaining the top quality standards that Computational Intelligence and Machine Learning is known for, globally.
Computational Intelligence and Machine Learning is the go-to journal for a wide variety of research, exploratory, analytical works, as well as the rarest of case studies, being carried out by researchers from all corners of the world within the different specializations of Artificial Intelligence & Machine Learning and closely associated disciplines.
Computational Intelligence and Machine Learning prides itself as a completely merit-based journal where anyone can get their research work published as long as it exhibits ingenuity, resourcefulness, talent, and originality. The fact that Computational Intelligence and Machine Learning is a double-blind peer-reviewed journal attests to this reality.
Usually, all judgments and decisions of the editorial committee at Computational Intelligence and Machine Learning are abiding and cannot be repealed. Nevertheless, a circumstance wherein an author feels like the contents, data or intentions of their manuscript was misjudged or misunderstood by the editorial committee. In such cases, an author may seek an explanation for the dismissal of their manuscript.
Subsequently, any appeals that an author wishes to make, have to have rational and logical reasoning as well as a counter explanation to the letter of rejection that they had received.
Any differences of opinion with regards to the significance, originality, and relevance of the manuscript in general, will and cannot be account for as an appeal.
Any decision that the editorial committee of Computational Intelligence and Machine Learning takes upon due reviewing and evaluation of the merits of the appeal, will be deemed final.
Even if the appeal is taken into consideration and deemed worthy of acceptance by the editorial committee, this does not guarantee the acceptance, reconsideration or publication of the manuscript. This is owing to the fact that the reconsideration process for a rejected manuscript often involves former reviewers/editors and new reviewers/editors, as well as a considerable amount of revision to be done.
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.
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.
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 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 -
materials and methods utilized,
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.