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

Submission Deadline: 31 December 2025 View: 24339 Submit to Special Issue

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Prof. Chih-Lung Lin

Email: linclr@go.hwh.edu.tw

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

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

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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

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Open Access

ARTICLE

3D Enhanced Residual CNN for Video Super-Resolution Network
Weiqiang Xin, Zheng Wang, Xi Chen, Yufeng Tang, Bing Li, Chunwei Tian
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2837-2849, 2025, DOI:10.32604/cmc.2025.069784
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract Deep convolutional neural networks (CNNs) have demonstrated remarkable performance in video super-resolution (VSR). However, the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction. To address this limitation, we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network (3D-ERVSNet). This network employs a forward and backward bidirectional propagation module (FBBPM) that aligns features across frames using explicit optical flow through lightweight SPyNet. By incorporating an enhanced residual structure (ERS) with skip connections, shallow and deep features are effectively integrated,… More >

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ARTICLE

Delving into End-to-End Dual-View Prohibited Item Detection for Security Inspection System
Zihan Jia, Bowen Ma, Dongyue Chen
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >

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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 >

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

Guest Editors

Summary

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Published Papers

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2159

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904

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488

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331

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