Pattern Recognition Letters

Top Research Topics at Pattern Recognition Letters?

The foci of Pattern Recognition Letters are Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Machine learning. Image processing, Pattern recognition (psychology), Segmentation, Image (mathematics) and Classifier (UML) studies are all carried out as a component of the study in Artificial intelligence presented. It concentrated on Segmentation research, specifically Image segmentation and Scale-space segmentation.

In addition to Pattern recognition research, it aims to explore topics under Feature (computer vision) and Cluster analysis. The study on Cluster analysis presented in Pattern Recognition Letters intersects with the topics under Data mining. Pixel, Object (computer science), Edge detection, Histogram and Biometrics are some of the facets of Computer vision tackled in Pattern Recognition Letters.

It explores research in Algorithm and the adjacent study of Mathematical optimization. The journal features studies on Machine learning, including topics such as Artificial neural network.

• Artificial intelligence (69.58%)
• Pattern recognition (39.18%)
• Computer vision (29.31%)

What are the most cited papers published in the journal?

• An introduction to ROC analysis (12196 citations)
• Floating search methods in feature selection (2547 citations)
• Information fusion in biometrics (1398 citations)

Research areas of the most cited articles at Pattern Recognition Letters:

The most cited papers tackle a plethora of topics, such as Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Machine learning. The published papers dive deep in exploring the relationship between the study of Artificial intelligence and Data mining. The journal papers explore issues in Pattern recognition which can be linked to other research areas like Artificial neural network and Cluster analysis.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

Pattern Recognition Letters generally zeroes in on subjects such as Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Convolutional neural network. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Computer vision. While work presented in it provided substantial information on Pattern recognition, it also covered topics in Pixel, Representation (mathematics) and Benchmark (computing).

Contextual image classification, Face (geometry) and Biometrics are some topics wherein Deep learning research discussed in the journal have an impact. Machine learning research presented in it encompasses a variety of subjects, including Field (computer science) and Task (project management).

The most cited articles from the last journal are:

• Large group activity security risk assessment and risk early warning based on random forest algorithm (68 citations)
• Deep learning for real-time semantic segmentation: Application in ultrasound imaging (26 citations)
• Facial Expression Recognition using Distance and Shape Signature features (22 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Pattern Recognition Letters (based on the number of publications) are:

• Azriel Rosenfeld (57 papers) absent at the last edition,
• Sankar K. Pal (33 papers) absent at the last edition,
• Bidyut B. Chaudhuri (30 papers) absent at the last edition,
• Edwin R. Hancock (30 papers) published 1 paper at the last edition, 2 less than at the previous edition,
• Josef Kittler (28 papers) published 1 paper at the last edition the same number as at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Pattern Recognition Letters (based on the number of publications) are:

• Chinese Academy of Sciences (158 papers) published 8 papers at the last edition, 3 less than at the previous edition,
• Indian Statistical Institute (134 papers) published 5 papers at the last edition, 3 less than at the previous edition,
• University of Maryland, College Park (99 papers) published 1 paper at the last edition,
• Shanghai Jiao Tong University (88 papers) absent at the last edition,
• Nanyang Technological University (85 papers) published 2 papers at the last edition, 1 more than at the previous edition.

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.

Publication chance based on affiliation

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, 9.23% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 8.14% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.81% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.19% of all publications and 71.86% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Application of Pattern Recognition in Preschool Education

Immersing knowledge from Pattern Recognition Letters can potentially influence the research on preschool education. The study of artificial intelligence (AI) and machine learning, discussed in the journal, has palpable improbability to steer advances in the development of educational tools for preschool learning. For instance, feature extraction within AI can be beneficial in creating learning software for children with learning disabilities. In the realm of preschool education, particularly for those interested in specializing within this field as a preschool teacher, Pattern Recognition skill is one of the essentials. These skills not only bolster learning capabilities but accelerate problem-solving, enhance cognitive development, and boost information processing. To become a preschool teacher in Illinois, there are specific educational and certification requirements. Knowing the applicability of pattern recognition can be advantageous for aspiring educators in fulfilling their preschool teacher requirements in Illinois. The pattern recognition aspect of AI provides an extraordinary avenue to develop technological tools, organize learning techniques, and support early childhood education reform. These works notwithstanding the conventional education pathway, unravel new methodologies to make learning interactive, efficient, and inclusive. Taking root from the concepts stipulated within Pattern Recognition Letters, educators, researchers, and curriculum developers can indeed harness these insights to further the horizon of preschool education.