International journal of machine learning and computing Acceptance Rate

International journal of machine learning and computing Acceptance Rate

Nil


Acceptance Rate is not available for International journal of machine learning and computing, or not provided by International journal of machine learning and computing


The acceptance rate is a way to ascertain the level of prestige a journal carries. An article when submitted for publishing is vehemently scrutinized and goes through very heavy testing such as peer-reviewing. Many prestigious journals such as International journal of machine learning and computing make sure that their reputation is maintained by any means necessary. They try very hard to keep their acceptance rate very low and only publish extremely well-done scientific articles and manuscripts. The Acceptance rate can measure the selectivity or prestige of a journal, though, like many journal metrics, the raw number is not the whole story, there is more to it.

In simple words, the percentage of all articles submitted to a journal in a whole year over the number of articles that were accepted to be printed in the same year can be defined as the acceptance rate. Both the number of submitted articles and the number of accepted articles are shown, to aid in giving a comprehensive understanding of the size of the journal. Articles that were withdrawn are not included in this data, they fall under the rejection rate. The acceptance rate is calculated as the ratio of the number of articles submitted to the number of articles published. The exact method of calculating the acceptance varies depending on journal to journal, each has its own.

Difference between Acceptance Rate and Rejection Rate

Rates of acceptance and rejection are projected in percentages and reflect and review the relationship between the number of articles submitted to a particular journal and those that were finally accepted after choosing from them. These rates are a measurement that must be gauged and taken into account when selecting the journal in which to publish your work, especially if your contribution is of high quality and has the capacity to compete with the best studies and challenge the best research. The rejection rate is the exact opposite of the Acceptance rate. Low Acceptance rate and High Rejection rate mean a prestigious journal such as International journal of machine learning and computing whereas high acceptance rate and low rejection rate means the journal doesn’t go through rigorous checks such as International journal of machine learning and computing.

Calculating Acceptance Rate

Acceptance rates of journals are capable of influencing faculty tenure and promotion decisions as well as how the quality of journals is seen and the intention to submit manuscripts to a given journal. Several variations exist in how acceptance rates are calculated across journals and editors, different methods are used by everyone which in turn results in potentially misleading comparisons and different results. The different results caused by different methods used can affect how powerful and reputed a journal is. Journals such as International journal of machine learning and computing have different methods than others.

Relationship between Impact factor and Acceptance rate

The impact factor plays a very important role as many researchers—together with members of institutional committees, who make decisions about funding, awards, and promotions—usually have the belief that studies published in high-impact-factor journals, which also have very high rejection rates and low acceptance rates, have a major impact in the subject area that is being researched. Also, having a paper published in a high impact factor journal like International journal of machine learning and computing is considered to be a challenge, very few authors are able to accomplish this and thus those authors get more funding and collaborations.

Journal Acceptance Rate Feedback System

Journal Acceptance Rate Feedback System is a platform on which you can access relevant data and make informed decisions with the help of a qualified crowd. It helps in improving the transparency of the peer-reviewed process. Occasionally, societies or other groups will issue acceptance rates for journals within a particular field. Again, a Google search may help find these but a dedicated software or platform particularly designed for this process is much more convenient


International journal of machine learning and computing

International Journal of Machine Learning and Computing - IJMLC is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of machine learning and computing. The focus is to publish papers on state-of-the-art machine learning and computing. Submitted papers will be reviewed by technical committees of the Journal and Association. The audience includes researchers, managers and operators for machine learning and computing as well as designers and developers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing. IJMLC is an open access journal which focus on publishing original and peer reviewed research papers on all aspects of machine learning and computing. And the topics include but not limited to: Adaptive systems Business intelligence Biometrics Bioinformatics Data and web mining Intelligent agent Financial engineering Inductive learning Geo-informatics Pattern Recognition Logistics Intelligent control Media computing   Neural net and support vector machine Hybrid and nonlinear system Fuzzy set theory, fuzzy control and system Knowledge management Information retrieval Intelligent and knowledge based system Rough and fuzzy rough set Networking and information security Evolutionary computation Ensemble more...







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