1947-8534
Published by: IGI Global
https://www.igi-global.com/journal/international-journal-multimedia-data-engineering/1118
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1136 | 4 | 4 | 1 |
The journal primarily tackles Artificial intelligence, Computer vision, Pattern recognition, Multimedia and Information retrieval. Some problems in Artificial intelligence that were presented in it overlapped with concepts under Machine learning, Data mining and Natural language processing. Deep learning is a focus of the Machine learning works in it.
Data mining research discussed connects with the study of Classifier (UML). Pixel, Video tracking, Motion (physics), Segmentation and Shot (filmmaking) are all aspects of Computer vision research featured in it. It links adjacent topics like Video tracking with Video processing.
The studies in Pattern recognition featured incorporate elements of Visual Word and Image retrieval. The research on Multimedia tackled can also make contributions to studies in the areas of Metadata, World Wide Web, Mobile device and Adaptation (computer science). The research on Information retrieval featured in International Journal of Multimedia Data Engineering and Management combines topics in other fields like The Internet, Set (abstract data type) and TRECVID.
The most cited articles investigate areas of study like Artificial intelligence, Machine learning, Multimedia, Pattern recognition and Contextual image classification. The studies tackled in the published articles, which mainly focus on Artificial intelligence, apply to Reduction (complexity) as well. The studies on Pattern recognition discussed at the most cited publications can also contribute to research in the domains of Hamming distance and Computer vision.
International Journal of Multimedia Data Engineering and Management investigates areas of study like Artificial intelligence, Pattern recognition, Autonomic nervous system, Potential mechanism and Dance. Graph (abstract data type) and Vertex (graph theory) are some topics wherein Artificial intelligence research discussed in International Journal of Multimedia Data Engineering and Management have an impact. In International Journal of Multimedia Data Engineering and Management, Clustering coefficient and Selection (genetic algorithm) are investigated in conjunction with one another to address concerns in Pattern recognition research.
The journal facilitates the exploration of Dance in relation to the fields of Multimedia and Audio visual.
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 International Journal of Multimedia Data Engineering and Management (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 International Journal of Multimedia Data Engineering and Management (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 25.00% 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.
While embarking on the fascinating journey of exploring various intriguing research topics presented in the International Journal of Multimedia Data Engineering and Management, readers might also be interested to learn about the potential career paths in the field of Multimedia Data Engineering and Management. The knowledge and expertise acquired in areas such as artificial intelligence, computer vision, pattern recognition, multimedia, and information retrieval can open doors to a variety of job opportunities. For instance, one can consider becoming a multimedia data engineer, who specializes in designing and managing systems for storing, analyzing, and retrieving complex multimedia data. Similarly, a career as a multimedia analyst who applies machine learning and artificial intelligence techniques to unlock insights from multimedia data can also be a rewarding choice. Moreover, the skills learned can also contribute to professions in related areas of education. For instance, you may consider exploring a career as a teacher assistant in early childhood education, especially in a technology-enhanced learning environment. To understand the prerequisites and potential salary for such a position, you can refer to this reliable source about becoming a preschool teacher assistant in Missouri. In conclusion, the knowledge gained from articles and research published in the International Journal of Multimedia Data Engineering and Management can help readers not only contribute to the scientific community but also advance their careers in various domains.
Shengzhou Yi;Koshiro Mochitomi;Isao Suzuki;Xueting Wang
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