| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 609 | 26 | 30 | 9 |
The main research concerns discussed in International Journal of Multimedia Information Retrieval are Artificial intelligence, Pattern recognition, Information retrieval, Image retrieval and Multimedia information systems. It explores issues in Artificial intelligence which can be linked to other research areas like Machine learning and Computer vision. The work on Pattern recognition tackled in International Journal of Multimedia Information Retrieval brings together disciplines like Content-based image retrieval and Data mining.
Many of the studies tackled connect Information retrieval with a similar field of study like Semantic gap. Some problems in Image retrieval that were presented in it overlapped with concepts under Image processing and Image texture. Topics in Multimedia information systems explored in it were investigated in conjunction with research in Multimedia and Data science.
The Multimedia works featured in it incorporate elements from Video retrieval and World Wide Web. Deep learning research featured in International Journal of Multimedia Information Retrieval incorporates concerns from various other topics such as Field (computer science) and Convolutional neural network. The Feature (computer vision) study featured in International Journal of Multimedia Information Retrieval draws connections with the study of Representation (mathematics).
The published articles focus largely on the fields of Artificial intelligence, Pattern recognition, Recommender system, Multimedia and Field (computer science). While the most cited papers focused on Artificial intelligence, they were also able to explore topics like Information retrieval and Computer vision. The journal publications explore Pattern recognition concepts, specifically Vector quantization and Pattern recognition (psychology) but expand to research in Maxima and minima and Image (category theory).
International Journal of Multimedia Information Retrieval facilitates discussions on Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Artificial neural network. International Journal of Multimedia Information Retrieval tackles issues in Artificial intelligence, particularly in the topics of Image (mathematics), Multimedia information systems, Segmentation, Convolutional neural network and Contextual image classification. International Journal of Multimedia Information Retrieval focuses on Convolutional neural network but sometimes tackles the closely related topic of Cosine similarity which is concerned with Recommender system.
The journal explores topics in Deep learning which can be helpful for research in disciplines like Field (computer science), Anomaly detection and Data science. While work presented in the journal provided substantial information on Field (computer science), it also covered topics in Object detection and Human–computer interaction. International Journal of Multimedia Information Retrieval focuses on Pattern recognition but the discussions also offer insight into other areas such as Supervised learning, Content-based image retrieval, MNIST database and Intrusion detection system.
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 Information Retrieval (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 Information Retrieval (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, 10.53% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.41% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.88% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 29.41% of all publications and 35.29% 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 International Journal of Multimedia Information Retrieval's cutting edge focuses on areas like Artificial Intelligence and Pattern Recognition, it's important to acknowledge the broader impact this journal has had on academic and research realms. The journal plays a role in multiple fields, influencing the direction of research and contributing to the body of knowledge in fields like education. For example, principles used from Machine Learning and AI have been employed to develop new pedagogical methods and tools in early childhood education. Understanding the fundamentals of these principles can benefit those in education sector, like aspiring preschool teachers. If you're interested in exploring this avenue, you may find preschool teacher requirements in New Hampshire helpful, as it provides a comprehensive guide to embarking on a career as a preschool teacher, discussions around which have been influenced by the findings in journals like the International Journal of Multimedia Information Retrieval.
Theodoros Georgiou;Yu Liu;Wei Chen;Michael S. Lew
(2020)Unknown
(2022)Ruhul Amin Hazarika;Ajith Abraham;Samarendra Nath Sur;Arnab Kumar Maji
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