| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 384 | 80 | 90 | 15 |
The discussions in Pattern Analysis and Applications mainly cover the fields of Artificial intelligence, Pattern recognition (psychology), Pattern recognition, Computer vision and Machine learning. In the Artificial intelligence research discussed, Segmentation, Classifier (UML), Support vector machine, Feature extraction and Artificial neural network are all tackled. Scale-space segmentation is a focus of the Segmentation works in Pattern Analysis and Applications.
The journal explores topics in Pattern recognition (psychology) which can be helpful for research in disciplines like Data mining, Speech recognition, Face (geometry), Image (mathematics) and Algorithm. It explores issues in Pattern recognition which can be linked to other research areas like Pixel, Feature (computer vision) and Cluster analysis. The work on Cluster analysis presented in it focuses on Correlation clustering in particular.
It links adjacent topics like Computer vision with Robustness (computer science).
The published papers primarily tackle Artificial intelligence, Pattern recognition (psychology), Pattern recognition, Computer vision and Machine learning. The journal papers hold forums on Pattern recognition (psychology) that merge themes from other disciplines such as Data mining, Feature (computer vision), Biometrics, Natural language processing and Benchmark (computing). The Pattern recognition research tackled in the journal publications is interrelated with Cluster analysis which concerns subjects like Algorithm.
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 Pattern Analysis 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 Pattern Analysis 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, 8.63% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 7.87% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.15% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.02% of all publications and 77.95% 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.
As a prospective career, Pattern Analysis and Applications offer immense possibilities across domains. The specialized knowledge of Artificial Intelligence, Pattern Recognition, Computer Vision, and Machine Learning equips individuals to take on complex roles in top organizations. For those aspiring towards academia, there's ample opportunity to contribute to this ever-expanding field. For instance, becoming an English teacher in Maine might be an easier path, but the level of intellectual challenge and potential impact in Pattern Analysis is considerably higher. To better understand a traditional education career prospect, you can check out this article on how to be an english teacher in maine. On the other hand, Pattern Analysis and Applications provide a high-tech, innovative pathway, with impacts on a unexpectedly wide range of sectors. Furthermore, the interdisciplinary nature of this field allows for greater adaptability in an ever-evolving work environment. Therefore, considering a career in Pattern Analysis and Applications demonstrates an active engagement with the most cutting-edge aspects of technology and research.
Tomas Arias-Vergara;Tomas Arias-Vergara;Tomas Arias-Vergara;Philipp Klumpp;Juan Camilo Vásquez-Correa;Juan Camilo Vásquez-Correa;Elmar Nöth
(2021)Tallha Akram;Muhammad Attique;Salma Gul;Aamir Shahzad
(2021)Ayan Kumar Bhunia;Avirup Bhattacharyya;Prithaj Banerjee;Partha Pratim Roy
(2020)Muhammad Sharif;Muhammad Attique Khan;Farooq Zahid;Jamal Hussain Shah
(2020)Wei Wei;Edmond S. L. Ho;Kevin D. McCay;Robertas Damaševičius
(2021)Srishti Gupta;Partha Pratim Roy;Debi Prosad Dogra;Byung-Gyu Kim
(2020)Jinsong Liu;Isak Worre Foged;Thomas B. Moeslund
(2021)Shehroz S. Khan;Jacob Nogas;Alex Mihailidis
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