| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 614 | 7 | 8 | 4 |
Computational and Mathematical Methods in Medicine explores disciplines such as Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Algorithm. Computational and Mathematical Methods in Medicine concentrated on Artificial intelligence research, specifically Segmentation, Image processing, Support vector machine, Artificial neural network and Deep learning. Image segmentation is a major topic of Segmentation research.
Feature (computer vision) and Electroencephalography are some topics wherein Pattern recognition research discussed in it have an impact.
The published articles investigate studies in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Support vector machine. While the primary focus in the most cited articles is Machine learning, they also dissect topics surrounding Feature extraction and Signal processing as a whole. The Pattern recognition research presented in the most cited articles focuses mostly on Electroencephalography and, on occasion, topics in Signal and Speech recognition.
Computational and Mathematical Methods in Medicine investigates studies in Artificial intelligence, Pattern recognition, Internal medicine, Cancer research and Deep learning. Most of the Artificial intelligence studies addressed also intersect with Machine learning. The research on Pattern recognition tackled can also make contributions to studies in the areas of Image (mathematics), Residual, Feature (computer vision) and Convolution.
The research on Internal medicine featured in Computational and Mathematical Methods in Medicine combines topics in other fields like Gene, Oncology and Cardiology. In addition to Cancer research research, it aims to explore topics under Carcinogenesis, Cancer, Cell, Cell growth and Downregulation and upregulation. The study on Segmentation featured in Computational and Mathematical Methods in Medicine expounds on the topic of Image segmentation in particular.
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 Computational and Mathematical Methods in Medicine (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 Computational and Mathematical Methods in Medicine (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, 13.21% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.09% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.30% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.22% of all publications and 57.39% 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.
Junaid Asghar;Saima Akbar;Muhammad Zubair Asghar;Bashir Ahmad
(2021)Fang He;Rachel Ka Man Chun;Zicheng Qiu;Shijie Yu
(2021)Muhammad Aslam;G Srinivasa Rao;Muhammad Saleem;Rehan Ahmad Khan Sherwani
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