Special Issues
Table of Content

Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application

Submission Deadline: 31 December 2025 View: 1767 Submit to Special Issue

Guest Editors

Prof.  Chih-Lung Lin

Email: linclr@go.hwh.edu.tw

Affiliation: Department of Electronic Engineering, Hwa Hsia University of Technology, New Taipei 23568, Taiwan

Homepage:

Research Interests: pattern recognition, artificial intelligence, machine learning, neural network, image processing, biometric

图片6.png


Summary

With the rapid advancements in big data revolution and the development of parallel processing units, machine learning applications in pattern recognition have become increasingly widespread and in-depth, such as face detection/recognition, facial expression recognition, object detection/recognition, medical image analysis/diagnostics/recognition, gesture/behavioral recognition, industrial inspection, and advanced driver assistance systems (ADASs), etc. We cordially invite researchers, engineers, and professionals to submit their latest research findings, sharing novel techniques and practical insights. The purpose of this special issue is to provide a platform to bring together the recent high-quality advances in research, theories, algorithms, innovative ideas, and applications in the above fields but not limited to.


Keywords

machine learning, artificial intelligence, pattern recognition, neural network, biometrics, image/video recognition, audio/speech recognition, computer vision, medical data/signal recognition

Published Papers


  • Open Access

    ARTICLE

    Deep Learning-Based Algorithm for Robust Object Detection in Flooded and Rainy Environments

    Pengfei Wang, Jiwu Sun, Lu Lu, Hongchen Li, Hongzhe Liu, Cheng Xu, Yongqiang Liu
    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267
    (This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
    Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Share Link