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Algorithms
Special Issues
Machine Learning for Pattern Recognition (3rd Edition)
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Machine Learning for Pattern Recognition (3rd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 5196

Special Issue Editors

Prof. Dr. Chih-Lung Lin

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Guest Editor
Graduate Institute of Intelligent Robotics, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
Interests: artificial intelligence; machine learning; image processing; biometrics; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bor-Jiunn Hwang

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Guest Editor
Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan
Interests: multimedia network services; computer network; wireless communication and network; image/video processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaou-Gang Miaou

E-Mail Website
Guest Editor
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
Interests: wireless multimedia communication; digital signal processing; pattern recognition; voice, image, video and biomedical signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Chi-Hung Chuang

E-Mail Website
Guest Editor
Computer Science and Information Engineering, Chung Yuan Christian University, Taoyuan 32001, Taiwan
Interests: machine learning; deep learning; virtual and augmented reality; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of artificial intelligence, machine learning is a well-known framework utilized for pattern recognition. Machine learning has led to significant advances in the field of pattern recognition due to the big data revolution and the development of parallel processing units. Pattern recognition has been widely employed in various real-world applications, such as face detection/recognition, facial expression recognition, medical image analysis/recognition, gesture recognition, behavioral recognition, and advanced driver assistance systems (ADASs). This Special Issue aims to provide a platform for the presentation of high-quality research regarding novel theories, algorithms, ideas, and applications in the above areas.

Prof. Dr. Chih-Lung Lin
Prof. Dr. Bor-Jiunn Hwang
Prof. Dr. Shaou-Gang Miaou
Dr. Chi-Hung Chuang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • algorithms
  • pattern recognition
  • gesture recognition
  • behavioral recognition
  • lightweight neural network
  • biometrics
  • image/video processing
  • audio/speech recognition
  • computer vision

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Related Special Issues

Published Papers (4 papers)

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Research

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28 pages, 4443 KB  
Article
UCINet: A Multi-Task Network for Umbilical Coiling Index Measurement in Obstetric Ultrasound
by Zhuofu Liu, Lichen Niu, Zhixin Di and Meimei Liu
Algorithms 2025, 18(9), 592; https://doi.org/10.3390/a18090592 - 22 Sep 2025
Viewed by 386
Abstract
The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which [...] Read more.
The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which suffers from low efficiency and susceptibility to inter-observer variability. In response to the challenges in measuring the umbilical coiling index during obstetric ultrasound, we propose UCINet, a multi-task neural network engineered explicitly for this purpose. UCINet demonstrates enhanced operational efficiency and significantly improved accuracy in detection, catering to the nuanced requirements of obstetric imaging. Firstly, this paper proposes a Frequency–Spatial Domain Downsampling Module (FSDM) to extract features in both the frequency and spatial domains, thereby reducing the loss of umbilical cord features and enhancing their representational capacity. The proposed Multi-Receptive Field Feature Perception Module (MRPM) employs receptive fields of varying sizes across different stages of the feature maps, enhancing the richness of feature representation. This approach allows the model to capture a more diverse set of spatial information, contributing to improved overall performance in feature extraction. A Multi-Scale Feature Aggregation Module (MSAM) comprehensively leverages multi-scale features via a dynamic fusion mechanism, optimizing the integration of disparate feature scales for enhanced performance. In addition, the UCI dataset, which consisted of 2018 annotated ultrasound images, was constructed, each labeled with the number of vascular coils and keypoints at both ends of the umbilical cord. Compared with state-of-the-art methods, UCINet achieves consistent improvements across two tasks. In object detection, UCINet outperforms Deformable DETR-R50 with an improvement of 1.2% points in mAP@50. In keypoint localization, it further exceeds YOLOv11 with a 3.0% gain in mAP@50, highlighting its effectiveness in both detection accuracy and fine-grained keypoint prediction. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
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27 pages, 1902 KB  
Article
Few-Shot Breast Cancer Diagnosis Using a Siamese Neural Network Framework and Triplet-Based Loss
by Tea Marasović and Vladan Papić
Algorithms 2025, 18(9), 567; https://doi.org/10.3390/a18090567 - 8 Sep 2025
Viewed by 555
Abstract
Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the [...] Read more.
Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images—combining a Siamese network model with a triplet-based loss function—to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework’s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
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21 pages, 3825 KB  
Article
Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
by Wenji Ai, Jiaxuan Zou, Zongchao Liu, Shaodi Wang and Shuai Teng
Algorithms 2025, 18(8), 462; https://doi.org/10.3390/a18080462 - 24 Jul 2025
Viewed by 423
Abstract
Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss [...] Read more.
Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss for boundary precision. Our approach significantly outperforms DeepLabV3+, SCTNet, and LarvSeg by 10.6–13.4% IoU, demonstrating particular strength in detecting small cracks (78.1% IoU) under challenging low-light/high-turbidity conditions. The solution provides a practical framework for automated underwater infrastructure inspection. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
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Other

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83 pages, 3818 KB  
Systematic Review
Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
by Daniele Pelosi, Diletta Cacciagrano and Marco Piangerelli
Algorithms 2025, 18(7), 443; https://doi.org/10.3390/a18070443 - 18 Jul 2025
Cited by 1 | Viewed by 2971
Abstract
Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct [...] Read more.
Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct yet often conflated paradigms: explainable AI (XAI), which refers to post hoc techniques that provide external explanations for model predictions, and interpretable AI, which emphasizes models whose internal mechanisms are understandable by design. Meanwhile, the phenomenon of concept and data drift—where models lose relevance due to evolving conditions—demands renewed attention. High-impact events, such as financial crises or natural disasters, have highlighted the need for robust interpretable or explainable models capable of adapting to changing circumstances. Against this backdrop, our systematic review aims to consolidate current research on explainability and interpretability with a focus on concept and data drift. We gather a comprehensive range of proposed models, available datasets, and other technical aspects. By synthesizing these diverse resources into a clear taxonomy, we intend to provide researchers and practitioners with actionable insights and guidance for model selection, implementation, and ongoing evaluation. Ultimately, this work aspires to serve as a practical roadmap for future studies, fostering further advancements in transparent, adaptable machine learning systems that can meet the evolving needs of real-world applications. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
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