0010-4825
Published by: Elsevier
https://www.journals.elsevier.com/computers-in-biology-and-medicine
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 21 | 534 | 891 | 87 |
The scientific interests tackled in Computers in Biology and Medicine are Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Machine learning. Presentations on Artificial intelligence include those discussing Deep learning, Support vector machine, Convolutional neural network, Artificial neural network and Feature extraction. Pattern recognition research featured in the journal incorporates concerns from various other topics such as Speech recognition, Feature (computer vision) and Electroencephalography.
The majority of Computer vision studies presented zero in on Image processing. More specifically, the research on Segmentation in Computers in Biology and Medicine is related to Image segmentation.
The journal publications mainly tackle studies in Artificial intelligence, Pattern recognition, Computer vision, Support vector machine and Segmentation. Most of the works presented in the most cited publications deal with Artificial intelligence but they intersect with the subject of Machine learning. The most cited articles explore research in Pattern recognition alongside concepts in Speech recognition and other areas of study in Signal processing.
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 Computers in Biology and 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 Computers in Biology and 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, 2.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 9.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.99% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.29% of all publications and 76.46% 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.
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