Pattern Recognition - Elsevier - Impact Factor

Pattern Recognition - Elsevier | 2024 Impact Factor:7.6 | Cite Score:15.8 | Q1

Impact Factor and Journal Rank of Pattern Recognition

About: Pattern Recognition is a premier scientific journal that publishes high-quality research articles in the field of pattern recognition. Published by Elsevier, this journal serves as a vital resource for researchers, practitioners, and academics involved in the study and application of pattern recognition methods and technologies. The journal covers a wide array of topics related to the detection, classification, and analysis of patterns in data, contributing significantly to advancements in this interdisciplinary field.
Content Types: Original research presenting new theories, methodologies, and empirical studies in pattern recognition. Comprehensive reviews that summarize the state-of-the-art, discuss key trends, and highlight future research directions in the field. Brief reports on innovative techniques, tools, and findings relevant to pattern recognition.
High Standards and Impact: The journal employs a rigorous peer-review process to ensure the publication of high-quality, accurate, and impactful research. Articles published in Pattern Recognition are frequently cited, indicating the journal significant influence and relevance in the field. Known for its high standards and contribution to the field, the journal is widely respected and trusted by the scientific community.
Global Reach: The journal attracts contributions from researchers worldwide, providing a diverse and global perspective on pattern recognition. As part of Elsevier portfolio, Pattern Recognition ensures broad accessibility to its content through various academic and research databases, libraries, and online platforms.
Significance: It is a leading journal that significantly contributes to the advancement of pattern recognition research and applications. Its comprehensive scope, rigorous standards, and international reach make it an invaluable resource for researchers, practitioners, and academics dedicated to the study and application of pattern recognition technologies.

Journal Home: Journal Homepage

Editor-in-Chief: Zoran Duric

Scope: The journal focuses on the development and application of techniques for identifying patterns in data, including theoretical approaches, algorithms, and practical applications. The scope of the journal is broad and interdisciplinary, encompassing a wide range of topics related to pattern recognition.
Key areas covered by the journal include, but are not limited to:
Statistical Pattern Recognition: Research on statistical methods for pattern recognition, including probabilistic models, statistical learning, and Bayesian approaches.
Machine Learning and Artificial Intelligence: Studies on machine learning algorithms, neural networks, deep learning, reinforcement learning, and AI techniques for pattern recognition.
Image Processing and Computer Vision: Research on the analysis and interpretation of visual data, including image segmentation, object detection, image recognition, and video analysis.
Signal Processing: Techniques for processing and analyzing signals in various domains, including audio, speech, biomedical signals, and time-series data.
Feature Extraction and Selection: Methods for identifying and selecting relevant features from data to improve the performance of pattern recognition systems.
Clustering and Classification: Research on algorithms and methods for clustering data into meaningful groups and classifying data into predefined categories.
Dimensionality Reduction: Techniques for reducing the dimensionality of data while preserving important patterns, including principal component analysis (PCA) and manifold learning.
Pattern Recognition in Bioinformatics: Applications of pattern recognition in biological data analysis, including gene expression analysis, protein structure prediction, and biomedical image analysis.
Document and Text Analysis: Techniques for recognizing patterns in textual data, including text classification, information retrieval, document clustering, and natural language processing (NLP).
Robustness and Performance Evaluation: Studies on the robustness of pattern recognition algorithms to noise, variability, and other challenges, as well as methods for evaluating their performance.