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Algorithms
Special Issues
Machine Learning and Deep Learning in Pattern Recognition
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Machine Learning and Deep Learning in Pattern Recognition

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 (15 May 2023) | Viewed by 11445

Special Issue Editor

Prof. Dr. Friedhelm Schwenker

E-Mail Website
Guest Editor
Insitute of Neural Information Processing, Ulm University, James Frank Ring, 89081 Ulm, Germany
Interests: artificial neural networks; pattern recognition; cluster analysis; statistical learning theory; data mining; multiple classifier systems; sensor fusion; affective computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, in the era of Artificial Intelligence, pattern recognition, machine learning and deep learning are used to design models that can identify patterns in data. Pattern recognition is a broad collection of algorithms and techniques for the understanding of data. The data inputs for pattern recognition can be words or texts, images, audio or economical time series; thus, a pattern recognition algorithm is typically considered as prerequisite for an intelligent system. Pattern recognition algorithms are basic tools in a variety of engineering and scientific disciplines, such as biology, psychology, medicine, computer vision, speech and signal processing, data mining and artificial intelligence. 

We welcome manuscripts to this Special Issue from (but not limited to) the following methodological and application fields
  • Data preprocessing methods and tools
  • Feature analysis and feature design
  • Statistical pattern recognition
  • Syntactical pattern recognition
  • Neural networks and deep learning in pattern recognition
  • Unsupervised learning
  • Supervised learning
  • Semisupervised learning
  • Image recognition
  • Video recognition and visual search
  • Facial recognition and analysis of facial expressions
  • Optical character recognition
  • Text pattern recognition
  • Optical character recognition
  • Handwriting recognition
  • Speech recognition, speaker recognition, emotions in speech
  • Multimodal recognition of emotion, stress, pain
  • Pattern recognition in medicine
  • E-Health
  • Benchmarking and pattern recognition databases.

Prof. Dr. Friedhelm Schwenker
Guest Editor

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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

19 pages, 13038 KiB  
Article
Visual Static Hand Gesture Recognition Using Convolutional Neural Network
by Ahmed Eid and Friedhelm Schwenker
Algorithms 2023, 16(8), 361; https://doi.org/10.3390/a16080361 - 27 Jul 2023
Cited by 6 | Viewed by 3904
Abstract
Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. [...] Read more.
Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware, such as special gloves, to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, we explore the approach of convolutional neural network (CNN) in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the fields of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase in the model’s accuracy was achieved. On public benchmarks, two challenging datasets have been classified almost perfectly, with testing accuracies of 96.5% and 96.57%. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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24 pages, 1564 KiB  
Article
Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques
by Damilola Akingbesote, Ying Zhan, Rytis Maskeliūnas and Robertas Damaševičius
Algorithms 2023, 16(6), 292; https://doi.org/10.3390/a16060292 - 5 Jun 2023
Cited by 4 | Viewed by 3211
Abstract
The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition [...] Read more.
The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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17 pages, 3285 KiB  
Article
A Novel Short-Memory Sequence-Based Model for Variable-Length Reading Recognition of Multi-Type Digital Instruments in Industrial Scenarios
by Shenghan Wei, Xiang Li, Yong Yao and Suixian Yang
Algorithms 2023, 16(4), 192; https://doi.org/10.3390/a16040192 - 31 Mar 2023
Cited by 2 | Viewed by 1635
Abstract
As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and [...] Read more.
As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and handwritten digit recognition, the recognition task of multi-type digital instruments faces greater challenges due to the reading strings are variable-length with different fonts, different spacing and aspect ratios. In order to overcome this shortcoming, we propose a novel short-memory sequence-based model for variable-length reading recognition. First, we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor for capturing effective features from characters with different fonts of multi-type digital instruments images. Then, we apply an RNN-based sequence module, which strengthens short-distance dependencies while reducing the long-distance trending memory of the reading string, to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. Experimental results show that this method is effective on variable-length instrument reading recognition task, especially for invisible data, which proves that our method has outstanding generalization and robustness in real-industrial applications. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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16 pages, 1932 KiB  
Article
A Novel Classification Algorithm Based on Multidimensional F1 Fuzzy Transform and PCA Feature Extraction
by Barbara Cardone and Ferdinando Di Martino
Algorithms 2023, 16(3), 128; https://doi.org/10.3390/a16030128 - 23 Feb 2023
Cited by 3 | Viewed by 1733
Abstract
The bi-dimensional F1-Transform was applied in image analysis to improve the performances of the F-transform method; however, due to its high computational complexity, the multidimensional F1-transform cannot be used in data analysis problems, especially in the presence of a [...] Read more.
The bi-dimensional F1-Transform was applied in image analysis to improve the performances of the F-transform method; however, due to its high computational complexity, the multidimensional F1-transform cannot be used in data analysis problems, especially in the presence of a large number of features. In this research, we proposed a new classification method based on the multidimensional F1-Transform in which the Principal Component Analysis technique is applied to reduce the dataset size. We test our method on various well-known classification datasets, showing that it improves the performances of the F-transform classification method and of other well-known classification algorithms; furthermore, the execution times of the F1-Transform classification method is similar to the ones obtained executing F-transform and other classification algorithms. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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