Submission Deadline: 31 January 2027 View: 2807 Submit to Special Issue
Prof. Dr. Jungpil Shin
Email: jpshin@u-aizu.ac.jp
Affiliation: School of Computer Science and Engineering, The University of Aizu, Aizu-wakamatsu, Japan
Research Interests: pattern recognition, image processing, computer vision, machine learning, human-computer interaction, non-touch interfaces, human gesture recognition, automatic control, Parkinson's disease diagnosis, ADHD diagnosis, user authentication, machine intelligence, bioinformatics, as well as handwriting analysis, recognition, and synthesis
Prof. Dr. Yong Seok Hwang
Email: thestone@kw.ac.kr
Affiliation: Department of Electronic Engineering, KwangWoon University, Seoul, Republic of Korea
Research Interests: machine learning based volumetric Meta holographic optical element (VMHOE), Deep learning based meta holo micro display (OLEDoS/LCoS) architecture for augmented reality (AR) devices. Machin learning based hologram data processing, machine learning, Deep learning, human–computer interaction, non-touch interfaces, human gesture recognition, ADHD and autism diagnosis, digital therapeutics
In today's digital era, patterns are omnipresent, shaping many aspects of our lives. These patterns can be physically observed or computationally identified through sophisticated algorithms. In the digital realm, patterns are often represented as vectors or matrices of feature values. With the rapid evolution of artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for analyzing and processing these features.
Machine learning, a core branch of AI, empowers computers to make decisions with minimal human intervention by leveraging pattern data. Deep learning, a subfield of ML, has gained significant attention for its ability to handle complex, high-dimensional data. The use of ML and DL models for extracting and analyzing meaningful features from text, images, videos, or sensor data is the foundation of pattern recognition (PR).
Pattern recognition is a critical enabler for a wide range of applications, including computer vision, sensor data analysis, natural language processing, speech recognition, robotics, bioinformatics, and beyond. This Special Issue aims to showcase cutting-edge research and innovative methodologies that advance the field of PR through ML and DL approaches.
We invite high-quality original research articles and comprehensive reviews that contribute to both theoretical developments and practical applications in the domain of pattern recognition. Topics of interest include, but are not limited to:
· Image processing, segmentation, and recognition
· Computer vision
· Speech recognition
· Automated target recognition
· Character recognition
· Gesture and human activity recognition
· Industrial inspection
· Medical diagnosis and health informatics
· Biosignal processing and bioinformatics
· Remote sensing
· Applications in healthcare
· Integration of ML and DL with the Internet of Things (IoT)
· Analysis of large datasets
· Current state-of-the-art and future trends in ML and DL for pattern recognition
This Special Issue seeks to provide a platform for researchers and practitioners to present innovative solutions, share insights, and discuss emerging trends in the ever-evolving field of pattern recognition.
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