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Algorithms
Special Issues
Machine Learning for Pattern Recognition (2nd Edition)
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Machine Learning for Pattern Recognition (2nd 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 2025 | Viewed by 1643

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

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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 for pattern recognition. Machine learning has made significant progress in the field of pattern recognition due to the big data revolution and the development of parallel processing units. Pattern recognition has been widely used 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). The purpose of this Special Issue, although not limited to the following, is to provide a platform to bring together the recent high-quality advances in research, theories, algorithms, innovative 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 1600 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 Issue

Published Papers (3 papers)

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Research

40 pages, 2708 KiB  
Article
Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
by Markus Eisenbach, Andreas Gebhardt, Dustin Aganian and Horst-Michael Gross
Algorithms 2024, 17(10), 430; https://doi.org/10.3390/a17100430 - 26 Sep 2024
Abstract
In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the [...] Read more.
In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these types of uncertainty, and that each type has its own value for improving re-identification performance. In particular, while the few state-of-the-art approaches that employ uncertainty for re-identification during inference utilize only data uncertainty to improve single-shot re-identification performance, we demonstrate that the estimated model uncertainty vector can be utilized to modify the feature vector. We explore the best method for utilizing the estimated model uncertainty based on the Market-1501 dataset and demonstrate that we are able to further enhance the performance above the already strong baseline UAL. Additionally, we show that the estimated distributional uncertainty resembles the degree to which the current sample is out-of-distribution. To illustrate this, we divide the distractor set of the Market-1501 dataset into four classes, each representing a different degree of out-of-distribution. By computing a score based on the estimated distributional uncertainty vector, we are able to correctly order the four distractor classes and to differentiate them from an in-distribution set to a significant extent. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))
17 pages, 3327 KiB  
Article
Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
by Sayed Mehedi Azim, Aravind Balasubramanyam, Sheikh Rabiul Islam, Jinglin Fu and Iman Dehzangi
Algorithms 2024, 17(9), 409; https://doi.org/10.3390/a17090409 - 12 Sep 2024
Abstract
Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established [...] Read more.
Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))
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19 pages, 4938 KiB  
Article
Classification and Regression of Pinhole Corrosions on Pipelines Based on Magnetic Flux Leakage Signals Using Convolutional Neural Networks
by Yufei Shen and Wenxing Zhou
Algorithms 2024, 17(8), 347; https://doi.org/10.3390/a17080347 - 8 Aug 2024
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Abstract
Pinhole corrosions on oil and gas pipelines are difficult to detect and size and, therefore, pose a significant challenge to the pipeline integrity management practice. This study develops two convolutional neural network (CNN) models to identify pinholes and predict the sizes and location [...] Read more.
Pinhole corrosions on oil and gas pipelines are difficult to detect and size and, therefore, pose a significant challenge to the pipeline integrity management practice. This study develops two convolutional neural network (CNN) models to identify pinholes and predict the sizes and location of the pinhole corrosions according to the magnetic flux leakage signals generated using the magneto-static finite element analysis. Extensive three-dimensional parametric finite element analysis cases are generated to train and validate the two CNN models. Additionally, comprehensive algorithm analysis evaluates the model performance, providing insights into the practical application of CNN models in pipeline integrity management. The proposed classification CNN model is shown to be highly accurate in classifying pinholes and pinhole-in-general corrosion defects. The proposed regression CNN model is shown to be highly accurate in predicting the location of the pinhole and obtain a reasonably high accuracy in estimating the depth and diameter of the pinhole, even in the presence of measurement noises. This study indicates the effectiveness of employing deep learning algorithms to enhance the integrity management practice of corroded pipelines. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))
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