1347-7986
Published by: Japan Society for Fuzzy Theory and Intelligent Informatics
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1078 | 6 | 12 | 2 |
The main points discussed in the journal deals with Artificial intelligence, Fuzzy logic, Pattern recognition, Computer vision and Human–computer interaction. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Machine learning and Natural language processing.
The most cited articles generally zeroe in on subjects such as Artificial intelligence, Speech recognition, Machine learning, Natural language processing and Discrete mathematics. The studies on Artificial intelligence discussed at the journal papers can also contribute to research in the domains of Microprocessor and Volume (computing). The most cited papers about Association rule learning and Soft computing are all disciplines of Machine learning that connect with topics in Genetic network and Acceleration.
The journal mostly deals with topics like Artificial intelligence, Pattern recognition, Machine learning, Human–computer interaction and Data mining. While the journal focused on Artificial intelligence, it was also able to explore topics like Class (set theory) and Computer vision. The research on Pattern recognition featured in the journal combines topics in other fields like Time series, Artificial neural network, Adaptive resonance theory, Template matching and Cluster analysis.
Machine learning research featured in the journal incorporates concerns from various other topics such as Aerial photography, Land use and Fuzzy logic. It addresses concerns in Human–computer interaction which are intertwined with other disciplines, such as Robot, Human–robot interaction and Social robot. Data mining research in the journal involves the investigation of Distributed database studies, all of which are linked to disciplines such as Fuzzy clustering.
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 Japan Society for Fuzzy Theory and Intelligent Informatics (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 Japan Society for Fuzzy Theory and Intelligent Informatics (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, 13.89% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 41.94% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.90% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 38.71% of all publications and 6.45% 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.
In an evolving technological landscape, artificial intelligence (AI) plays a significant role not only in advanced computing disciplines but has also started penetrating various other sectors. One such field is education, where AI could lend a helping hand in revolutionizing the traditional teaching methodologies. Particularly for subjects like History which rely heavily on factual recall and critical thinking, AI technologies can create an engaging and personalized learning environment. AI could predict student performance, streamline administrative tasks, offer personalized learning paths and even become a utility for teachers in secondary education. For example, a high school history teacher could utilize AI to automate mundane tasks such as grading, allowing them to concentrate more on designing comprehensive lessons and assisting students individually. AI-powered tutoring systems can also provide personalized attention to students by customizing the teaching based on student’s comprehension level and learning pace. Another growing field is the use of Natural Language Processing (NLP) in education. NLP can transform the way feedback is given in class by providing immediate, personalized and actionable feedback to students. This could be particularly effective during teaching history, where students' written assignments could be evaluated by AI, identifying both their understanding and areas needing improvement. In addition, Machine Learning, a subset of AI, could be crucial in predicting students’ learning outcomes by analyzing the patterns in their learning behavior and performance. This analysis can help to identify struggling students early, allowing for timely intervention. To incorporate AI in education, the teachers themselves need to have a certain level of understanding about this technology. For information on becoming a history teacher in Michigan and the potential salary, visit this link. As AI and its subsets continue to evolve, they will undoubtedly offer both increased efficiency and novel approaches to teaching history. By embracing these advancements, educators can significantly enhance the learning experience, making it more interactive, personalized, and efficient.
Hiroshi Sakai;Michinori Nakata;Junzo Watada
(2020)Akihiro Nishihara;Naoki Masuyama;Yusuke Nojima;Hisao Ishibuchi
(2021)Yuichi Omozaki;Naoki Masuyama;Yusuke Nojima;Hisao Ishibuchi
(2021)Naoki Masuyama;Itsuki Tsubota;Yusuke Nojima;Hisao Ishibuchi
(2021)Yuto Fujii;Naoki Masuyama;Yusuke Nojima;Hisao Ishibuchi
(2021)Taichi Sono;Michita Imai
(2021)Yuto Irie;Naoki Masuyama;Yusuke Nojima;Hisao Ishibuchi
(2020)Ryuichi Hashimoto;Naoki Masuyama;Yusuke Nojima;Hisao Ishibuchi
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