| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 237 | 120 | 190 | 23 |
The journal is organized to address concerns in the fields of Artificial intelligence, Computational intelligence, Machine learning, Evolutionary computation and Data science. The work on Artificial intelligence addressed in IEEE Computational Intelligence Magazine expands to the thematically related Pattern recognition. Computational intelligence research presented in IEEE Computational Intelligence Magazine encompasses a variety of subjects, including Field (computer science), Management science, Engineering management and Operations research.
The studies in Evolutionary computation featured incorporate elements of Evolutionary algorithm and Theoretical computer science. Most of the Data science studies addressed also intersect with Big data.
The most cited papers mainly tackle studies in Artificial intelligence, Machine learning, Computational intelligence, Fuzzy logic and Evolutionary computation. The most cited papers focus on Artificial intelligence research which is adjacent to topics in Natural language processing. The most cited papers with studies in Machine learning featured incorporate elements of Field (computer science) and Data mining.
The journal focuses largely on the fields of Artificial intelligence, Machine learning, Evolutionary algorithm, Artificial neural network and Computational intelligence. IEEE Computational Intelligence Magazine aims to address concerns in Artificial intelligence, specifically in the areas of Deep learning, Domain knowledge, Genetic programming, Convolutional neural network and Feature (machine learning). The work on Machine learning tackled in it brings together disciplines like Ubiquitous computing, Object detection, Wearable computer and Personality.
The studies on Evolutionary algorithm discussed can also contribute to research in the domains of Human multitasking and Crossover. The journal focuses on Artificial neural network but the discussions also offer insight into other areas such as Probabilistic logic and Data mining. The presented Computational intelligence research focuses mostly on Evolutionary computation and, on occasion, topics in Optimization problem, Linear programming and Quadratic programming.
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 IEEE Computational Intelligence Magazine (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 IEEE Computational Intelligence Magazine (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, 20.51% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.13% were posted by at least one author from the top 10 institutions publishing in the journal. Another 32.26% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 29.03% of all publications and 22.58% 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 essential aspect that often gets overlooked in analyzing research trends and contributions is the educational pathways of the contributing authors. This adds another dimension to the discussion, as we consider the diverse paths these professionals took to contribute to the field. A part of this journey is of course defined by the educational requirements of the specific profession or research area. For example, the path to becoming a preschool teacher in Hawaii would differ greatly from that of a researcher in computational intelligence. For detailed information about the educational and licensure requirements for becoming a preschool teacher in Hawaii, you can follow this link: preschool teacher education requirements in Hawaii. Having this broad perspective not only enriches our understanding of research trends but also raises intriguing questions about the correlations between various academic pathways and contribution areas. Are there certain educational backgrounds that gravitate more towards certain research topics in computational intelligence, machine learning, or artificial intelligence? These are the questions that can be explored and potentially answered by delving deeper into the educational journeys of the contributing authors. Understanding these aspects contributes to a holistic and comprehensive analysis of the research trends, author contributions, and the future trajectory of these scientific fields.
Kay Chen Tan;Liang Feng;Min Jiang
(2021)Shad Akhtar;Asif Ekbal;Erik Cambria
(2020)Yu Xue;Yankang Wang;Jiayu Liang;Adam Slowik
(2021)Wonjun Ko;Eunjin Jeon;Seungwoo Jeong;Heung-Il Suk
(2021)Maoguo Gong;Yu Xie;Ke Pan;Kaiyuan Feng
(2020)Handing Wang;Liang Feng;Yaochu Jin;John Doherty
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