1346-0714
Published by: Japanese Society for Artificial Intelligence
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 966 | 15 | 32 | 3 |
Transactions of The Japanese Society for Artificial Intelligence focuses on Artificial intelligence, Information retrieval, World Wide Web, Natural language processing and Machine learning. Transactions of The Japanese Society for Artificial Intelligence facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Data mining, Computer vision and Pattern recognition.
Artificial intelligence, Natural language processing, Algorithm, Theoretical computer science and Mathematical optimization are the main subjects of interest in the published papers. The most cited articles aim to investigate interdisciplinary topics such as Artificial intelligence and Causality (physics). The published papers address concerns in Natural language processing which are intertwined with other disciplines, such as Lightness (philosophy), Speech recognition, Dialog system, Dialog box and Human–computer interaction.
The concepts of Human–computer interaction, Artificial intelligence, Algorithm, Reinforcement learning and Structure (mathematical logic) are tackled in Transactions of The Japanese Society for Artificial Intelligence. Topics in Human–computer interaction explored in the journal were investigated in conjunction with research in Action recognition, Context (language use), Task oriented and Active listening. Some problems in Artificial intelligence that were presented in Transactions of The Japanese Society for Artificial Intelligence overlapped with concepts under Coordinate descent method and Natural language processing.
The journal explores issues in Algorithm which can be linked to other research areas like Block (telecommunications), Complex data type and Group lasso, Feature selection. Response generation, Multimedia, Dialogue acts and Service (business) are some topics wherein Reinforcement learning research discussed in Transactions of The Japanese Society for Artificial Intelligence have an impact. The research on Structure (mathematical logic) featured in Transactions of The Japanese Society for Artificial Intelligence combines topics in other fields like Theoretical computer science and Software design pattern.
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 Transactions of The Japanese Society for Artificial Intelligence (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 Transactions of The Japanese Society for Artificial Intelligence (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, 5.13% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.43% were posted by at least one author from the top 10 institutions publishing in the journal. Another 32.43% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.32% of all publications and 10.81% 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.
One of the missing aspects that could offer added depth to the article is a section dedicated to potential career opportunities for those interested in the fields discussed. Here is a sample section: As advancements in artificial intelligence continue, prospective individuals looking to chart a career in these fields might be curious about the professional routes they can pursue. Engaging in studies like those published in Transactions Of The Japanese Society For Artificial Intelligence could serve as a launching pad for various career paths.
Some of these career options include becoming an AI Engineer, a Business Intelligence Developer, a Data Scientist, and many others. Specializing in particular aspects of artificial intelligence can unlock unique opportunities. For example, if you have underlying command of English language and want to explore a teaching career within the AI space, discovering how to become an English teacher in Colorado could present an attractive pathway for bridging language proficiency and technical acumen.
Incorporating knowledge acquired from essential research fields like machine learning, data mining, and natural language processing can enable significant contributions in both academia and a commercial setting. Consequently, engaging with cutting-edge development in artificial intelligence could prove invaluable in the road towards career progression.
Shogo Hayashi;Akira Tanimoto;Hisashi Kashima
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(2021)Asuka Sakai;Hiroaki Maruhashi;Yukinobu Hamuro;Munehiko Sasajima
(2021)Koichiro Tamura;Yutaka Matsuo
(2020)Maya Okawa;Tomoharu Iwata;Takeshi Kurashima;Yusuke Tanaka
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