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Published by: Institute of Electronics, Information and Communication Engineers
https://search.ieice.org/bin/index.php?category=D&lang=E&curr=1
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 445 | 82 | 158 | 13 |
IEICE Transactions on Information and Systems explores disciplines such as Artificial intelligence, Computer vision, Pattern recognition, Speech recognition and Algorithm. The Artificial intelligence works featured in it incorporate elements from Machine learning, Computer graphics (images) and Natural language processing.
The most cited publications aim to foster the development of research in Artificial intelligence, Computer vision, Speech recognition, Pattern recognition and Algorithm. The works on Artificial intelligence tackled in the journal articles bring together disciplines like Machine learning and Natural language processing. The Computer vision study tackled in the published articles is a key component of adjacent topics in the area of Computer graphics (images).
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 IEICE Transactions on Information and Systems (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 IEICE Transactions on Information and Systems (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, 15.02% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 24.86% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.92% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.02% of all publications and 44.20% 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.
While the IEICE Transactions on Information and Systems journal is an integral source of high-impact research in artificial intelligence, computer vision, and other related disciplines, it is important to also consider the potential professional applications and career pathways stemming from this knowledge. For example, educators skilled in artificial intelligence and pattern recognition can be instrumental in designing inclusive learning environments. This is particularly relevant in special education, where teachers employ technologies to personalise learning experiences for students with diverse needs. One potential career path for professionals inspired by the IEICE Transactions on Information and Systems journal findings is to become a special education teacher. These educators work with children who have a wide range of learning, mental, emotional, and physical disabilities. They employ specialised teaching strategies and effective technologies, which can include applications of artificial intelligence (AI) and computer vision technologies, to cater to a diverse learning environment. You can learn more about how to translate your knowledge into a career in this field by exploring how to become a special education teacher in Kansas. Here, you will discover the necessary qualifications and requirements for a fulfilling pathway in special education. By connecting high-quality research with practical application, the link between the IEICE Transactions on Information and Systems journal studies and the professional world becomes more tangible.
Vasileios Kouliaridis;Konstantia Barmpatsalou;Georgios Kambourakis;Shuhong Chen
(2020)Haichuan Yang;Shangce Gao;Rong-Long Wang;Yuki Todo
(2021)Yudi Zhang;Debiao He;Xinyi Huang;Ding Wang
(2020)Jiateng Liu;Wenming Zheng;Yuan Zong;Cheng Lu
(2020)Pursuing a degree in Computer Science opens doors to diverse industries, but exploring related fields can broaden your career options even further. For instance, an artificial intelligence degree salary often reflects the high demand for specialized skills in AI, machine learning, and data science. This makes AI a compelling complement to traditional computer science studies.
Alternatively, if you're passionate about sustainability, combining computer science with environmental expertise is a growing trend. Understanding roles available in environment-related fields can be facilitated by exploring what jobs can you get with an environmental science degree. This insight is valuable for developing software and systems that tackle ecological challenges.
For those looking to accelerate their learning, many institutions now offer an accelerated computer science degree online. Such programs cater to fast-paced learners and working professionals aiming to upskill quickly without compromising quality.
Moreover, pursuing an environmental engineering degree online provides a solid foundation for those interested in applying engineering principles to ecological problems. This path benefits from the synergy between computing expertise and environmental engineering, encouraging innovative career prospects.
