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Application of Machine Learning and Deep Learning in Pattern Recognition...
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Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 11248

Special Issue Editors

Dr. Mohamed Hammad

E-Mail Website
Guest Editor
Faculty of Computers and Information, Menoufia University‬, Shebin El-Koom 32511, Egypt
Interests: biometrics; pattern recognition; deep learning; machine learning; AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paweł Pławiak

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
Interests: machine learning; ensemble learning; deep learning; evolutionary computation; data science

Special Issue Information

Dear Colleagues,

Patterns abound in today’s digital technologies. Since the development of artificial intelligence (AI) techniques in the modern period, numerous machine learning (ML) and deep learning (DL) models have been produced. ML is the branch of AI that can carry out unprogrammed tasks such as data analysis, the building of analytical models, and categorization. DL is a subset of ML in AI. The process of collecting meaningful properties from an image or video using DL and ML models is known as pattern recognition (PR). PR is used in a wide range of engineering applications, such as computer vision, natural language processing, character recognition, robotics, and speech recognition. It is also used in a variety of medical imaging and telemedicine applications.

This Special Issue focuses on state-of-the-art ML and DL techniques and their applications in PR. We seek contributions that include but are not limited to:

Novel applications of ML and DL in pattern recognition;

Biometrics applications based on ML and DL;

Multimodel biometrics based on ML and DL;

Novel datasets, challenges, and benchmarks for application and evaluation of pattern recognition and biometrics;

Review of new trends in pattern recognition and biometrics;

Applications of behavioral biometrics for human recognition.

Dr. Mohamed Hammad
Prof. Dr. Paweł Pławiak
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. Information 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

  • ML
  • DL
  • pattern recognition
  • biometrics
  • human recognition

Published Papers (5 papers)

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Research

15 pages, 6053 KiB  
Article
Deep Supervised Hashing by Fusing Multiscale Deep Features for Image Retrieval
by Adil Redaoui, Amina Belalia and Kamel Belloulata
Information 2024, 15(3), 143; https://doi.org/10.3390/info15030143 - 05 Mar 2024
Viewed by 953
Abstract
Deep network-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structural information that contains important semantic details, which are crucial [...] Read more.
Deep network-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structural information that contains important semantic details, which are crucial for effective hash learning. On the one hand, structural information is important for capturing the spatial relationships between objects in an image. On the other hand, image retrieval tasks often require a more holistic representation of the image, which can be achieved by focusing on the semantic content. The trade-off between structural information and image retrieval accuracy in the context of image hashing and retrieval is a crucial consideration. Balancing these aspects is essential to ensure both accurate retrieval results and meaningful representation of the underlying image structure. To address this limitation and improve image retrieval accuracy, we propose a novel deep hashing method called Deep Supervised Hashing by Fusing Multiscale Deep Features (DSHFMDF). Our approach involves extracting multiscale features from multiple convolutional layers and fusing them to generate more robust representations for efficient image retrieval. The experimental results demonstrate that our method surpasses the performance of state-of-the-art hashing techniques, with absolute increases of 11.1% and 8.3% in Mean Average Precision (MAP) on the CIFAR-10 and NUS-WIDE datasets, respectively. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics)
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19 pages, 833 KiB  
Article
Radar-Based Invisible Biometric Authentication
by Maria Louro da Silva, Carolina Gouveia, Daniel Filipe Albuquerque and Hugo Plácido da Silva
Information 2024, 15(1), 44; https://doi.org/10.3390/info15010044 - 12 Jan 2024
Viewed by 2333
Abstract
Bio-Radar (BR) systems have shown great promise for biometric applications. Conventional methods can be forged, or fooled. Even alternative methods intrinsic to the user, such as the Electrocardiogram (ECG), present drawbacks as they require contact with the sensor. Therefore, research has turned towards [...] Read more.
Bio-Radar (BR) systems have shown great promise for biometric applications. Conventional methods can be forged, or fooled. Even alternative methods intrinsic to the user, such as the Electrocardiogram (ECG), present drawbacks as they require contact with the sensor. Therefore, research has turned towards alternative methods, such as the BR. In this work, a BR dataset with 20 subjects exposed to different emotion-eliciting stimuli (happiness, fearfulness, and neutrality) in different dates was explored. The spectral distributions of the BR signal were studied as the biometric template. Furthermore, this study included the analysis of respiratory and cardiac signals separately, as well as their fusion. The main test devised was authentication, where a system seeks to validate an individual’s claimed identity. With this test, it was possible to infer the feasibility of these type of systems, obtaining an Equal Error Rate (EER) of 3.48% if the training and testing data are from the same day and within the same emotional stimuli. In addition, the time and emotion results dependency is fully analysed. Complementary tests such as sensitivity to the number of users were also performed. Overall, it was possible to achieve an evaluation and consideration of the potential of BR systems for biometrics. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics)
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13 pages, 1144 KiB  
Article
Develop a Lightweight Convolutional Neural Network to Recognize Palms Using 3D Point Clouds
by Yu-Ming Zhang, Chia-Yuan Cheng, Chih-Lung Lin, Chun-Chieh Lee and Kuo-Chin Fan
Information 2023, 14(7), 381; https://doi.org/10.3390/info14070381 - 03 Jul 2023
Viewed by 1290
Abstract
Biometrics has become an important research issue in recent years, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various [...] Read more.
Biometrics has become an important research issue in recent years, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various biometrics due to their unique features and easy accessibility. However, traditional palm recognition methods involve 3D point clouds, which can be complex and difficult to work with. To mitigate this challenge, this paper proposes two methods which are Multi-View Projection (MVP) and Light Inverted Residual Block (LIRB).The MVP simulates different angles that observers use to observe palms in reality. It transforms 3D point clouds into multiple 2D images and effectively reduces the loss of mapping 3D data to 2D data. Therefore, the MVP can greatly reduce the complexity of the system. In experiments, MVP demonstrated remarkable performance on various famous models, such as VGG or MobileNetv2, with a particular improvement in the performance of smaller models. To further improve the performance of small models, this paper applies LIRB to build a lightweight 2D CNN called Tiny-MobileNet (TMBNet).The TMBNet has only a few convolutional layers but outperforms the 3D baselines PointNet and PointNet++ in FLOPs and accuracy. The experimental results show that the proposed method can effectively mitigate the challenges of recognizing palms through 3D point clouds of palms. The proposed method not only reduces the complexity of the system but also extends the use of lightweight CNN. These findings have significant implications for developing biometrics and could lead to improvements in various fields, such as access control and security control. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics)
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13 pages, 6546 KiB  
Article
Assessing Cardiac Functions of Zebrafish from Echocardiography Using Deep Learning
by Mao-Hsiang Huang, Amir Mohammad Naderi, Ping Zhu, Xiaolei Xu and Hung Cao
Information 2023, 14(6), 341; https://doi.org/10.3390/info14060341 - 16 Jun 2023
Viewed by 1352
Abstract
Zebrafish is a well-established model organism for cardiovascular disease studies in which one of the most popular tasks is to assess cardiac functions from the heart beating echo-videos. However, current techniques are often time-consuming and error-prone, making them unsuitable for large-scale analysis. To [...] Read more.
Zebrafish is a well-established model organism for cardiovascular disease studies in which one of the most popular tasks is to assess cardiac functions from the heart beating echo-videos. However, current techniques are often time-consuming and error-prone, making them unsuitable for large-scale analysis. To address this problem, we designed a method to automatically evaluate the ejection fraction of zebrafish from heart echo-videos using a deep-learning model architecture. Our model achieved a validation Dice coefficient of 0.967 and an IoU score of 0.937 which attest to its high accuracy. Our test findings revealed an error rate ranging from 0.11% to 37.05%, with an average error rate of 9.83%. This method is widely applicable in any laboratory setting and can be combined with binary recordings to optimize the efficacy and consistency of large-scale video analysis. By facilitating the precise quantification and monitoring of cardiac function in zebrafish, our approach outperforms traditional methods, substantially reducing the time and effort required for data analysis. The advantages of our method make it a promising tool for cardiovascular research using zebrafish. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics)
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16 pages, 449 KiB  
Article
A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching
by Allam Jaya Prakash, Kiran Kumar Patro, Saunak Samantray, Paweł Pławiak and Mohamed Hammad
Information 2023, 14(2), 65; https://doi.org/10.3390/info14020065 - 23 Jan 2023
Cited by 15 | Viewed by 3431
Abstract
An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully [...] Read more.
An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully in a number of biometric recognition applications where security is a top priority. The major challenges in the existing literature are (i) the noise components in the signals, (ii) the inability to automatically extract the feature set, and (iii) the performance of the system. This paper suggests a beat-based template matching deep learning (DL) technique to solve problems with traditional techniques. ECG beat denoising, R-peak detection, and segmentation are done in the pre-processing stage of this proposed methodology. These noise-free ECG beats are converted into gray-scale images and applied to the proposed deep-learning technique. A customized activation function is also developed in this work for faster convergence of the deep learning network. The proposed network can extract features automatically from the input data. The network performance is tested with a publicly available ECGID biometric database, and the proposed method is compared with the existing literature. The comparison shows that the proposed modified Siamese network authenticated biometrics have an accuracy of 99.85%, a sensitivity of 99.30%, a specificity of 99.85%, and a positive predictivity of 99.76%. The experimental results show that the proposed method works better than the state-of-the-art techniques. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Pattern Recognition and Biometrics)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Machine Learning and Deep Learning Models for Detecting Fake News: A Comparative Analysis on Fake-NewsNet Dataset
Authors: Robertas Damaševičius
Affiliation: Kauno technologijos universitetas | Kaunas University of Technology Programų inžinerijos katedra | Department of Software Engineering K. Baršausko St. 59-A320, LT-51423, Kaunas, Lithuania
Abstract: The proliferation of fake news through social media has emerged as one of the most pressing challenges of the twenty-first century. This paper presents a machine learning approach to detecting fake news, with the goal of analyzing the linguistic designs that distinguish true and false news. The study uses two machine learning models, Naïve Bayes and Support Vector Machine, and one deep learning model, LSTM, to determine the accuracy of each method in identifying fake news. The dataset used for the study is the Fake-NewsNet dataset, which contains 432 fake news articles and 624 real news articles, with a total of 4304 tweets collected. The results show that Naïve Bayes had the highest accuracy at 0.584, followed by Support Vector Machine at 0.574, and LSTM with the lowest accuracy of 0.560. The authors speculate that the reason for the low LSTM score may be due to the small size of the dataset and the complex structure of the network. Overall, the study highlights the potential for machine learning methods to aid in the detection of fake news, but also underscores the importance of continued research in this area to improve detection accuracy.

Title: Deep learning-based method for cardiac function assessment in zebrafish from echocardiography
Authors: Mao-Hsiang Huang1, Amir Mohammad Naderi1, Ping Zhu2, Xiaolei Xu2, Hung Cao1
Affiliation: 1 Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, USA; 2 Department of Biochemistry and Molecular Biology/Department of Cardiovascular Medicine, Mayo Clinic Rochester, MN, USA
Abstract: Zebrafish is a well-established model organism for investigating cardiovascular function. Nevertheless, current monitoring techniques are often time-intensive and error-prone, rendering them unsuitable for large-scale video analysis. To tackle this issue, we have devised an approach that utilizes a deep learning model architecture to automate the evaluation of cardiovascular indices, including ejection fraction, from zebrafish echocardiography videos. Our model achieved a validation Dice coefficient of 0.867 and an IoU score of 0.860, which attest to its high accuracy. Our test findings revealed an error rate ranging from 0.15% to 14.71%, with an average error rate of 5.18%. This method is widely applicable in any laboratory setting and can be combined with binary recordings to optimize the efficacy and consistency of large-scale video analysis. By facilitating the precise quantification and monitoring of cardiac function in zebrafish, our approach outperforms traditional methods, substantially reducing the time and effort required for data analysis. The advantages of our method make it a promising tool for future research into the cardiovascular system in zebrafish.

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