| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 378 | 95 | 97 | 15 |
The topics of Multimedia, World Wide Web, Artificial intelligence, The Internet and Human–computer interaction are the focal point of discussions in the journal. The research on Multimedia featured in it combines topics in other fields like User interface, Graphics, Visualization, Computer network and Virtual reality. IEEE MultiMedia is mostly focused on World Wide Web, specifically Web modeling.
IEEE MultiMedia facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Natural language processing, Machine learning, Computer vision and Pattern recognition.
The most cited articles investigate areas of study like Multimedia, World Wide Web, Artificial intelligence, Human–computer interaction and Information retrieval. The journal publications facilitate discussions on Multimedia that incorporate concepts from other fields like User interface, The Internet, Graphics, Collaborative software and Virtual reality. The most cited papers address concerns in Artificial intelligence which are intertwined with other disciplines, such as Computer vision and Natural language processing.
IEEE MultiMedia generally zeroes in on subjects such as Artificial intelligence, Feature extraction, Task analysis, Pattern recognition and Deep learning. Artificial intelligence research presented in it encompasses a variety of subjects, including Machine learning, Computer vision and Natural language processing. It explores topics in Feature extraction which can be helpful for research in disciplines like Artificial neural network, Multimodal learning, Facial recognition system, Visualization and Reinforcement learning.
While Pattern recognition is the focus of the journal, it also provided insights into the studies of Redundancy (engineering), Facial expression, Reuse and Encoding (memory). Topics in Deep learning were tackled in line with various other fields like Quality (business), Theoretical computer science, Decoding methods, Compression (functional analysis) and Benchmark (computing). In addition to Affective computing research, the journal aims to explore topics under Multimedia and Focus (computing).
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 MultiMedia (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 MultiMedia (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, 13.04% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.50% of all publications and 62.50% 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.
Considering the range of complex and advanced subjects discussed at IEEE Multimedia, it is beneficial for those pursuing related careers to gain a deep understanding of these issues through structured educational paths. For instance, someone interested in a career as a history teacher might need to understand how virtual reality, a topic often discussed within IEEE Multimedia, can be used to enhance students' learning experiences, offering a more immersive journey through time. To learn more about this career path, you can refer to our guide on how to become a history teacher in Utah. However, the need for understanding topics such as Artificial Intelligence, Multimedia, and World Wide Web is not limited to teaching professions. They are interconnected with various other fields like User Interface, Graphics, Computer Network, Visualization, and Virtual Reality. Professionals in software development, web designing, digital art, network administration, and more also extensively need to understand these subjects. Therefore, it is crucial to explore appropriate educational and training pathways for careers that involve mastery of these subjects. Future entries in this blog will delve into such pathways for various professions, providing readers with comprehensive guides on their journey to achieving their career goals.
Kiyoharu Aizawa;Azuma Fujimoto;Atsushi Otsubo;Toru Ogawa
(2020)Sheng Liu;Chengqing Li;Qiao Hu
(2021)Ashima Anand;Amit Kumar Singh;Zhihan Lv;Guarav Bhatnagar
(2020)Yang Wu;Yanyan Zhao;Xin Lu;Bing Qin
(2021)Trisha Mittal;Aniket Bera;Dinesh Manocha
(2021)Weiyao Lin;Xiaoyi He;Wenrui Dai;John See
(2020)Ioan-Sorin Comsa;Estevao Bissoli Saleme;Alexandra Covaci;Gebremariam Mesfin Assres
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