0387-6101
Published by: Information Processing Society of Japan
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 747 | 32 | 45 | 6 |
The journal mainly tackles studies in Artificial intelligence, Computer network, Computer security, Programming language and Natural language processing. Topics in Artificial intelligence were tackled in line with various other fields like Machine learning, Speech recognition, Computer vision and Pattern recognition.
The published articles are organized to address concerns in the fields of Artificial intelligence, Computer vision, Algorithm, Computer network and Pattern recognition. While the journal articles focused on Artificial intelligence, they were also able to explore topics like Machine learning, Task (project management) and Natural language processing. The most cited publications focus on Computer network but the discussions also offer insight into other areas such as Overlay network and Slicing.
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 Journal of Information Processing (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 Journal of Information Processing (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 2021 edition, 4.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 41.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.28% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 25.00% of all publications and 18.06% 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.
This journal somewhat lacks real-world applications of the research topics it discusses. Providing this context could significantly attract more readers and impact, drawing those who are interested in the practical implication of the research. For example, educational professionals and enthusiasts who employ artificial intelligence techniques in their educational programs and materials might find this information useful. The specific application of Natural Language Processing (NLP) techniques, for instance, can be considerable in transforming the way of teaching and learning process in various fields such as art. For instance, NLP can be employed to develop complex educational models that can streamline individualized learning for students in different art fields such as elementary art education. These models can analyze and process the linguistic patterns, understanding, and preferences of individual students to customize art lessons in a more engaging, interactive, and productive manner. The sophistication of these learning tools and models can vary significantly depending on the proficiency and experience of the art teacher designing and utilizing them. To get a better understanding of the teaching profession, particularly in the area of art education, you might find insights from articles on how to become an elementary art teacher in New York helpful. Thus, by examining practical applications of these advanced techniques, we could broaden our knowledge and encourage novel approaches to address challenges in the practical field, thereby attracting a wider set of readers who are interested in such knowledge applications.
Shi Qiu;Daniel M. German;Katsuro Inoue
(2021)Dario Alfonso Cuello Mejía;Aoba Saito;Mitsuhiko Kimoto;Takamasa Iio
(2021)Joshua Ani;Sualeh Asif;Erik D. Demaine;Yevhenii Diomidov
(2020)Sangwhan Moon;Naoaki Okazaki
(2021)Nattaon Techasarntikul;Photchara Ratsamee;Jason Orlosky;Tomohiro Mashita
(2020)Neda Gholami;Mohammad Mahdi Dehshibi;Andrew Adamatzky;Antonio Rueda-Toicen
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