| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 45 | 739 | 1469 | 62 |
The journal focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Image (mathematics). The study on Artificial intelligence presented in Multimedia Tools and Applications intersects with the topics under Machine learning. The journal focuses on Pattern recognition but the discussions also offer insight into other areas such as Histogram and Deep learning.
It connects the study in Computer vision with the closely related area of Robustness (computer science). Algorithm research featured in it incorporates concerns from various other topics such as Information hiding, Embedding and Encryption. Encryption research discussed connects with the study of Chaotic.
The study on Segmentation featured in it expounds on the topic of Image segmentation in particular.
The most cited publications focus largely on the fields of Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Multimedia. The Artificial intelligence study tackled in the published papers is a key component of adjacent topics in the area of Machine learning. The journal articles explore issues in Algorithm which can be linked to other research areas like Theoretical computer science and Encryption.
The objective of the journal is to combine knowledge in the areas of Artificial intelligence, Pattern recognition, Image (mathematics), Computer vision and Algorithm. The journal focused on Artificial intelligence research but expanded to cover Machine learning. The studies in Pattern recognition featured incorporate elements of Artificial neural network, Pixel and Robustness (computer science).
Multimedia Tools and Applications dives deep in exploring the relationship between the study of Image (mathematics) and Process (computing). Most of the works presented in Multimedia Tools and Applications deals with Algorithm but it intersects with the subject of Encryption. In addition to Encryption research, Multimedia Tools and Applications aims to explore topics under Chaotic and Key (cryptography).
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 Multimedia Tools 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 Multimedia Tools 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, 8.80% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 6.66% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.51% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.09% of all publications and 76.74% 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 fast-paced advancements in fields such as Artificial intelligence, Pattern recognition, Computer vision, and Algorithm, there has been a steady demand for expert professionals in these areas. Individuals with a keen interest in these fields often choose a research or academic career path.
Becoming a teacher or a leader in research, particularly in a specific field like Artificial Intelligence or Computer Vision, involves dedicated time committed to gaining expertise and knowledge. However, the journey to becoming an expert in these fields can be both enriching and rewarding.
For example, if you are interested in becoming a history teacher in South Dakota, understanding the expectations and requirements can help plan your career trajectory better. With regards to the time investment, you might wonder how long does it take to become a teacher in South Dakota. Such information can guide potential researchers or teachers in setting realistic expectations and goals for their career.
Overall, whether it is teaching or conducting research, professionals in fields like Artificial intelligence, Pattern recognition, Computer vision, and Algorithm, have many opportunities to influence the future of these areas through their work.
Sourabh Katoch;Sumit Singh Chauhan;Vijay Kumar
(2021)Christian Garbin;Xingquan Zhu;Oge Marques
(2020)Jian Wang;Siyuan Lu;Shui-Hua Wang;Yu-Dong Zhang;Yu-Dong Zhang
(2021)Djamila Romaissa Beddiar;Brahim Nini;Mohammad Sabokrou;Abdenour Hadid
(2020)Waseem Ullah;Amin Ullah;Ijaz Ul Haq;Khan Muhammad
(2021)Sunil Singh;Umang Ahuja;Munish Kumar;Krishan Kumar
(2021)Monika Bansal;Munish Kumar;Manish Kumar
(2021)Mohamed Abdel-Basset;Abduallah Gamal;Gunasekaran Manogaran;Le Hoang Son
(2020)Jianming Zhang;Xiaokang Jin;Juan Sun;Jin Wang
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