| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 97 | 450 | 624 | 43 |
The primary areas of discussion in the journal are Artificial intelligence, Multimedia, Pattern recognition, Computer vision and Computer network. Artificial intelligence study tackled is connected to the field of Machine learning. It explores topics in Multimedia which can be helpful for research in disciplines like The Internet, Mobile device and Human–computer interaction.
Pattern recognition research is concerned with Discriminative model in particular. The Computer vision study featured in the journal draws parallels with the field of Robustness (computer science). Topics in Computer network were tackled in line with various other fields like Scalability, Real-time computing and Distributed computing.
ACM Transactions on Multimedia Computing, Communications, and Applications investigates Real-time computing research which frequently intersects with Video quality.
The journal publications focus on Artificial intelligence, Multimedia, Computer network, Information retrieval and Machine learning. Computer vision and Pattern recognition are some topics wherein Artificial intelligence research discussed in the journal papers has an impact. The most cited publications address concerns in Multimedia which are intertwined with other disciplines, such as Quality of experience, Relevance (information retrieval), Human–computer interaction, Video processing and Event (computing).
The journal investigates areas of study like Artificial intelligence, Pattern recognition, Image (mathematics), Computer vision and Feature (computer vision). The featured Artificial intelligence studies mainly concentrate on Machine learning but also cover areas of interest in Task (project management). The research on Pattern recognition presented in ACM Transactions on Multimedia Computing, Communications, and Applications often intersects with areas of study such as
Topics in Image (mathematics) explored in the journal were investigated in conjunction with research in Graph (abstract data type) and Generative model. In the journal, Construct (python library) and Modality (human–computer interaction) are investigated in conjunction with one another to address concerns in Computer vision research. Research in Contextual image classification and the interrelating topic of Categorization and Discriminative model were among the subjects of interest in the Feature (computer vision) studies discussed in ACM Transactions on Multimedia Computing, Communications, and Applications.
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 ACM Transactions on Multimedia Computing, Communications, and Applications (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 ACM Transactions on Multimedia Computing, Communications, and Applications (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, 2.47% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 25.32% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.13% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.52% of all publications and 43.04% 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.
Despite the detailed content regarding the research areas, citation statistics, and returning authors, the article could be enhanced by providing information about the educational background and qualifications of the contributors to ACM Transactions on Multimedia Computing, Communications, and Applications. Readers may want to know about the expertise and educational qualifications these contributors have to back their research. This information could help readers ascertain the credibility and authority of the contributors and the research published. Here is a draft: A significant number of contributors to ACM Transactions on Multimedia Computing, Communications, and Applications are renowned scholars in their respective fields, and hold advanced degrees from accredited universities. It's crucial to note that several of these contributors have specific educational qualifications in areas closely related to the journal’s primary topics of interest. This includes, but is not limited to, Artificial Intelligence, Multimedia, Pattern recognition, Computer vision, and Computer networks. The blend of expertise forms a robust base of collective knowledge, making the journal an excellent platform for innovation and insightful perspectives in these areas.
For instance, a substantial percentage of contributors specialized in multimedia and pattern recognition have earned Master’s and Ph.D. degrees in Computer Science. They are adept with vital aspects such as machine learning, computer vision, and human-computer interaction. A significant number of researchers contributing to AI-related topics typically have an educational background in software engineering or data science. This strong academic background plays a pivotal role in the depth and value of the research these contributors bring to the journal.
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. By understanding the foundational knowledge base of the journal's contributors, it may offer readers a clearer perspective on the caliber of the articles published and indirectly increase the publication's reputation and authority in the field.
Zhedong Zheng;Liang Zheng;Michael Garrett;Yi Yang
(2020)M. Tanveer;B. Richhariya;R. U. Khan;A. H. Rashid
(2020)Shui-Hua Wang;Yu-Dong Zhang
(2020)Unknown
(2021)Chenggang Yan;Zhisheng Li;Yongbing Zhang;Yutao Liu
(2020)Yuhang Ding;Hehe Fan;Mingliang Xu;Yi Yang
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