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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 11538

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Prof. Dr. Chih-Lung Lin

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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
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Prof. Dr. Bor-Jiunn Hwang

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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
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Prof. Dr. Shaou-Gang Miaou

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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
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Dr. Chi-Hung Chuang

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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.

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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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Machine Learning for Pattern Recognition in Algorithms (11 articles)
Machine Learning for Pattern Recognition (3rd Edition) in Algorithms

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Research

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17 pages, 5772 KiB

Open AccessArticle

Optimized Energy Consumption of Electric Vehicles with Driving Pattern Recognition for Real Driving Scenarios

by Bedatri Moulik, Sanmukh Kaur and Muhammad Ijaz

Algorithms 2025, 18(4), 204; https://doi.org/10.3390/a18040204 - 5 Apr 2025

Viewed by 379

Abstract

Energy management strategies (EMS) in the context of electric or hybrid vehicles can optimize the available energy by minimizing consumption. Most optimization-based EMS are not real-time-applicable for an accurate estimation of future consumption. The performance of these strategies also strongly depends on the driving patterns, which may be influenced by road and traffic conditions, among other factors such as driving style, weather, vehicle type, etc. The primary contribution of this work is to develop a novel two-layer driving pattern recognition (DPR) system for roadway type and traffic classification, thus enabling the identification of unknown patterns for the enhancement of the prediction of energy consumption of an electric vehicle (EV). The novelty of this work lies in the development of a strategy based on real-time data which is capable of classifying driving patterns and implementing an optimized EMS based on the results of the DPR. In the approach, first, labels are defined based on statistical features related to speed followed by the creation of representative driving patterns (RDPs). A neural network-based classifier is then employed for classification into six classes based on four features. A training accuracy of 97.7% is achieved with the classification of unknown speed profiles into the known RDPs. Testing with patterns from two different test routes shows an accuracy of 97.45% and 96.98% during morning and 96.65% and 94.12% during evening hours, respectively. Apart from the route and time of data collection, accuracy is also a function of sampling time horizon and the threshold values chosen for the features. A sensitivity analysis was also performed to evaluate the relative importance of each feature. An EMS based on sequential quadratic programming (SQP) was combined with DPR for the computation of optimal energy consumption. Simulation results show that maximum and minimum energy savings of 61% and 18% were obtained under suburban low traffic and highway high traffic conditions, respectively. An eco-driving or driver speed advisory system may further be developed based on information obtained from multiple routes and varying traffic scenarios. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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26 pages, 3529 KiB

Open AccessArticle

Protecting Intellectual Security Through Hate Speech Detection Using an Artificial Intelligence Approach

by Sadeem Alrasheed, Suliman Aladhadh and Abdulatif Alabdulatif

Algorithms 2025, 18(4), 179; https://doi.org/10.3390/a18040179 - 21 Mar 2025

Viewed by 400

Abstract

Online social networks (OSNs) have become an integral part of daily life, with platforms such as X (formerly Twitter) being among the most popular in the Middle East. However, X faces the problem of widespread hate speech aimed at spreading hostility between communities, especially among Arabic-speaking users. This problem is exacerbated by the lack of effective tools for processing Arabic content and the complexity of the Arabic language, including its diverse grammar and dialects. This study developed a two-layer framework to detect and classify Arabic hate speech using machine learning and deep learning with various features and word embedding techniques. A large dataset of Arabic tweets was collected using the X API. The first layer of the framework focused on detecting hate speech, while the second layer classified it into religious, social, or political hate speech. Convolutional neural networks (CNN) outperformed other models, achieving an accuracy of 92% in hate speech detection and 93% in classification. These results highlight the framework’s effectiveness in addressing Arabic language complexities and improving content monitoring tools, thereby contributing to intellectual security and fostering a safer digital space. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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53 pages, 24859 KiB

Open AccessArticle

Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks

by Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Daniel-Ioan Curiac, Mihai Sorin Radu and Nicolae Tudoroiu

Algorithms 2025, 18(3), 170; https://doi.org/10.3390/a18030170 - 16 Mar 2025

Viewed by 433

Abstract

This paper investigates the design and MATLAB/Simulink implementation of two intelligent neural reinforcement learning control algorithms based on deep learning neural network structures (RL DLNNs), for a complex Heating Ventilation Air Conditioning (HVAC) centrifugal chiller system (CCS). Our motivation to design such control strategies lies in this system’s significant control-related challenges, namely its high dimensionality and strongly nonlinear multi-input multi-output (MIMO) structure, coupled with strong constraints and a substantial impact of measured disturbance on tracking performance. As a beneficial vehicle for “proof of concept”, two simplified CCS MIMO models were derived, and an extensive number of simulations were run to demonstrate the effectiveness of both RL DLNN control algorithm implementations compared with two conventional control algorithms. The experiments involving the two investigated data-driven advanced neural control algorithms prove their high potential to adapt to various types of nonlinearities, singularities, dimensions, disruptions, constraints, and uncertainties that inherently characterize real-world processes. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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22 pages, 958 KiB

Open AccessArticle

Nonparametric Probability Density Function Estimation Using the Padé Approximation

by Hamid Reza Aghamiri, S. Abolfazl Hosseini, James R. Green and B. John Oommen

Algorithms 2025, 18(2), 88; https://doi.org/10.3390/a18020088 - 6 Feb 2025

Viewed by 833

Abstract

Estimating the Probability Density Function (PDF) of observed data is crucial as a problem in its own right, and also for diverse engineering applications. This paper utilizes two powerful mathematical tools, the concept of moments and the relatively little-known Padé approximation to achieve this. On the one hand, moments encapsulate crucial information that is central to both the “time-” and “frequency-”domain representations of the data. On the other hand, the Padé approximation provides an effective means of obtaining a convergent series from the data. In this paper, we invoke these established tools to estimate the PDF. As far as we know, the theoretical results that we have proven, and the experimental results that confirm them, are novel and rather pioneering. The method we propose is nonparametric. It leverages the concept of using the moments of the sample data—drawn from the unknown PDF that we aim to estimate—to reconstruct the original PDF. This is achieved through the application of the Padé approximation. Apart from the theoretical analysis, we have also experimentally evaluated the validity and efficiency of our scheme. The Padé approximation is asymmetric. The most unique facet of our work is that we have utilized this asymmetry to our advantage by working with two mirrored versions of the data to obtain two different versions of the PDF. We have then effectively “superimposed” them to yield the final composite PDF. We are not aware of any other research that utilizes such a composite strategy, in any signal processing domain. To evaluate the performance of the proposed method, we have employed synthetic samples obtained from various well-known distributions, including mixture densities. The accuracy of the proposed method has also been compared with that gleaned by several State-Of-The-Art (SOTA) approaches. The results that we have obtained underscore the robustness and effectiveness of our method, particularly in scenarios where the sample sizes are considerably reduced. Thus, this research confirms how the SOTA of estimating nonparametric PDFs can be enhanced by the Padé approximation, offering notable advantages over existing methods in terms of accuracy when faced with limited data. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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23 pages, 1520 KiB

Open AccessArticle

Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks

by Yao-San Lin, Hung-Yu Chen, Mei-Ling Huang and Tsung-Yu Hsieh

Algorithms 2024, 17(12), 583; https://doi.org/10.3390/a17120583 - 18 Dec 2024

Viewed by 932

Abstract

Voiceprint recognition systems often face challenges related to limited and diverse datasets, which hinder their performance and generalization capabilities. This study proposes a novel approach that integrates generative adversarial networks (GANs) for data augmentation and convolutional neural networks (CNNs) with mel-frequency cepstral coefficients (MFCCs) for voiceprint classification. Experimental results demonstrate that the proposed methodology improves recognition accuracy by up to 15% in low-resource scenarios. The optimal ratio of real-to-GAN-generated samples was determined to be 3:2, which balanced dataset diversity and model performance. In specific cases, the model achieved an accuracy of 96.6%, showcasing its effectiveness in capturing unique voice characteristics while mitigating overfitting. These results highlight the potential of combining GAN-augmented data and CNN-based classification to enhance voiceprint recognition in diverse and resource-constrained environments. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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20 pages, 11204 KiB

Open AccessArticle

Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning

by Pierre Gouton, Kacoutchy Jean Ayikpa and Diarra Mamadou

Algorithms 2024, 17(11), 473; https://doi.org/10.3390/a17110473 - 22 Oct 2024

Viewed by 1160

Abstract

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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40 pages, 2712 KiB

Open AccessArticle

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

Viewed by 1017

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 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))

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17 pages, 3327 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 2320

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 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

Open AccessArticle

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

Viewed by 1513

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 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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Review

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41 pages, 43778 KiB

Open AccessReview

UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking

by Md. Mahfuzur Rahman, Sunzida Siddique, Marufa Kamal, Rakib Hossain Rifat and Kishor Datta Gupta

Algorithms 2024, 17(12), 594; https://doi.org/10.3390/a17120594 - 23 Dec 2024

Cited by 1 | Viewed by 1455

Abstract

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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