0888-613X
Published by: Elsevier
https://www.journals.elsevier.com/international-journal-of-approximate-reasoning
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 159 | 85 | 162 | 31 |
| Mathematics | 219 | 23 | 53 | 12 |
International Journal of Approximate Reasoning was organized to reinforce research efforts on Artificial intelligence, Anatomy, Internal medicine, Fuzzy logic and Mathematics education. Most of the Artificial intelligence studies addressed also intersect with Machine learning.
The published articles cover a variety of subjects, including Artificial intelligence, Fuzzy logic, Machine learning, Fuzzy number and Data mining. The most cited papers explore issues in Artificial intelligence which can be linked to other research areas like Set (abstract data type) and Pattern recognition. The works on Fuzzy number tackled in the most cited publications bring together disciplines like Fuzzy set operations and Fuzzy classification.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in International Journal of Approximate Reasoning (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in International Journal of Approximate Reasoning (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2022 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 100.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 0.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Yiyu Yao
(2020)Peide Liu;Yumei Wang;Fan Jia;Hamido Fujita;Hamido Fujita
(2020)Zehua Jiang;Keyu Liu;Xibei Yang;Hualong Yu
(2020)Jifang Pang;Xiaoqiang Guan;Jiye Liang;Baoli Wang
(2020)Unknown
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