| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Engineering and Technology | 824 | 19 | 21 | 10 |
| Medicine | 2754 | 18 | 20 | 8 |
Biomedical Engineering Online primarily tackles Biomedical engineering, Artificial intelligence, Computer vision, Internal medicine and Pattern recognition. In addition to Artificial intelligence research, it aims to explore topics under Machine learning and Signal. The Internal medicine study featured in Biomedical Engineering Online draws connections with the study of Cardiology.
Biomedical Engineering Online features studies on Cardiology, including topics such as Hemodynamics.
The journal papers investigate areas of study like Biomedical engineering, Artificial intelligence, Pattern recognition, Internal medicine and Cardiology. The most cited papers facilitate discussions on Biomedical engineering that incorporate concepts from other fields like Ablation, Electroporation, Finite element method and In vivo. The published papers address concerns in Artificial intelligence which are intertwined with other disciplines, such as Computer vision and Signal processing.
The scientific interests tackled in the journal are Artificial intelligence, Biomedical engineering, Pattern recognition, Internal medicine and Deep learning. The journal explores issues in Artificial intelligence which can be linked to other research areas like Machine learning, Computer vision and Sensitivity (control systems). Biomedical Engineering Online focuses on Biomedical engineering but the discussions also offer insight into other areas such as Biocompatibility, Ex vivo, Hemodynamics and Ultrasound.
While Pattern recognition is the focus of it, it also provided insights into the studies of Transfer of learning, F1 score and Inertial measurement unit. While work presented in Biomedical Engineering Online provided substantial information on Internal medicine, it also covered topics in Gastroenterology, Oncology and Cardiology. The Convolutional neural network works featured in Biomedical Engineering Online incorporate elements from Artificial neural network and Segmentation.
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 Biomedical Engineering Online (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 Biomedical Engineering Online (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, 2.80% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.15% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.77% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.38% of all publications and 57.69% 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.
Bojan Milosevic;Alberto Leardini;Elisabetta Farella
(2020)Jie Zhong;Jie Zhong;Ying Wang;Jie Li;Xuetong Xue
(2020)Romana Perinajová;Joe F. Juffermans;Jonhatan Lorenzo Mercado;Jean Paul Aben
(2021)Michael Neidlin;Sam Liao;Zhiyong Li;Benjamin Simpson
(2021)Esmail Nourmohammadi;Esmail Nourmohammadi;Saman Hosseinkhani;Reza Nedaeinia;Hoda Khoshdel-Sarkarizi
(2020)Christian Giang;Elvira Pirondini;Elvira Pirondini;Nawal Kinany;Nawal Kinany;Camilla Pierella
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