| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 28 | 913 | 1632 | 81 |
The topics of Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Machine learning are the focal point of discussions in Pattern Recognition. Artificial intelligence studies presented in Pattern Recognition focus on topics such as Image processing, Pattern recognition (psychology), Segmentation, Feature extraction and Feature (computer vision). The Segmentation works, particularly on Image segmentation are tackled in it.
Scale-space segmentation is a focus of the presented Image segmentation works and it dives deep in Scale-space segmentation. The studies in Pattern recognition featured incorporate elements of Facial recognition system and Cluster analysis. The work on Cluster analysis addressed in it expands to the thematically related Data mining.
The journal tackles issues in Computer vision, particularly in the topics of Image (mathematics), Pixel, Edge detection and Object (computer science).
The journal papers mainly deal with areas of study such as Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Image processing. Most of the Artificial intelligence studies addressed in the most cited articles also intersect with Machine learning. The most cited publications tackle studies in Cluster analysis and the interrelated subject of Data mining to gain insights into Pattern recognition.
The journal mostly deals with topics like Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Feature (computer vision). Topics like Image (mathematics), Convolutional neural network, Segmentation, Benchmark (computing) and Discriminative model are tackled as part of the discussions on Artificial intelligence. The main emphasis of the journal is the subject of Pattern recognition, focusing on Feature vector.
Machine learning research in Pattern Recognition involves the investigation of Generalization studies, all of which are linked to disciplines such as Selection (genetic algorithm) and Classifier (linguistics). The concepts on Deep learning presented in Pattern Recognition can also apply to other research fields, including Artificial neural network, Field (computer science) and Biometrics. While work presented in Pattern Recognition provided substantial information on Feature (computer vision), it also covered topics in Channel (digital image) and Object detection.
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 Recognition (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 Recognition (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 2022 edition, 4.73% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.33% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.45% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.53% of all publications and 49.69% 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.
One section that could greatly enhance the depth of the article is about how to become a contributor to the journal. This section could provide information about the qualifications, submission process, and tips for getting published. If you're looking to contribute to the discussions in the field of Pattern Recognition, it's essential to understand the submission guidelines and requirements of the journal. It's always worth noting that being published in a respected journal such as this not only requires thorough research but also the ability to explain your findings in a clear, concise manner.
You should have a good background in topics such as Artificial Intelligence, Pattern Recognition, Computer Vision, Algorithm, Machine Learning, to thrive in your submission to the journal. A PhD or a Master's degree in the relevant field would be ideally suited to tackle these complex topics.
If you're interested in transitioning from being a reader of the Pattern Recognition Journal to becoming a published contributor, you should consider investing in your education and skills. In particular, becoming a teacher could be one path to take. For instance, in case you might be wondering {how long does it take to become a middle school math teacher in Virginia}, you could read more about it on our site. Teaching such subjects might be one way to gain practical insights that could inspire your research.
Lastly, while having strong knowledge and solid research is crucial, writing and presentation skills should not be underestimated. You should strive to present your ideas clearly, backing up your findings with robust data and adhering to the publication style guide of the journal.
Becoming a contributor to a respected journal such as Pattern Recognition isn't just about the prestige - it's about the chance to be part of the ongoing dialogues that are shaping the future of AI, Machine Learning, and other related fields.
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(2020)Chongyi Li;Saeed Anwar;Saeed Anwar;Fatih Porikli
(2020)Song Bai;Feihu Zhang;Philip H.S. Torr
(2021)L. Minh Dang;Kyungbok Min;Hanxiang Wang;Md. Jalil Piran
(2020)Vitjan Zavrtanik;Matej Kristan;Danijel Skočaj
(2021)Haotong Qin;Ruihao Gong;Xianglong Liu;Xiao Bai
(2020)Xingjun Ma;Yuhao Niu;Lin Gu;Yisen Wang
(2021)Liangchen Song;Cheng Wang;Lefei Zhang;Bo Du
(2020)Rijun Liao;Shiqi Yu;Shiqi Yu;Weizhi An;Yongzhen Huang
(2020)Alfredo Nazábal;Pablo M. Olmos;Zoubin Ghahramani;Zoubin Ghahramani;Isabel Valera;Isabel Valera
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