Special Issue "Algorithms for Pattern Recognition"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 March 2020).

Special Issue Editor

Dr. Dawid Połap
Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: machine learning; multiagent systems; bioinformatics; Internet of Things
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Pattern recognition is an important topic that focuses on matching and analysing patterns. However, finding the right features or patterns that will contribute to sample recognition can be a difficult task. In recent years, there has been rapid development of various types of classifiers and techniques for data analysis and identification of relationships. Further development of the field is important in order to reduce the number of calculations and the operation time and to increase the accuracy of various algorithms.

This Special Issue is devoted to the analysis and presentation of new algorithms in the area of pattern recognition. Papers should contain both theoretical and experimental information in order to present more accurate solutions against the background of existing ones.

Dr. Dawid Połap
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 papers will be 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 1400 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

  • pattern recognition
  • object detection/recognition
  • machine learning techniques
  • probabilistic methods
  • metaheuristics
  • image processing
  • medical data analysis

Published Papers (5 papers)

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Research

Open AccessArticle
On a Hybridization of Deep Learning and Rough Set Based Granular Computing
Algorithms 2020, 13(3), 63; https://doi.org/10.3390/a13030063 - 11 Mar 2020
Cited by 1
Abstract
The set of heuristics constituting the methods of deep learning has proved very efficient in complex problems of artificial intelligence such as pattern recognition, speech recognition, etc., solving them with better accuracy than previously applied methods. Our aim in this work has been [...] Read more.
The set of heuristics constituting the methods of deep learning has proved very efficient in complex problems of artificial intelligence such as pattern recognition, speech recognition, etc., solving them with better accuracy than previously applied methods. Our aim in this work has been to integrate the concept of the rough set to the repository of tools applied in deep learning in the form of rough mereological granular computing. In our previous research we have presented the high efficiency of our decision system approximation techniques (creating granular reflections of systems), which, with a large reduction in the size of the training systems, maintained the internal knowledge of the original data. The current research has led us to the question whether granular reflections of decision systems can be effectively learned by neural networks and whether the deep learning will be able to extract the knowledge from the approximated decision systems. Our results show that granulated datasets perform well when mined by deep learning tools. We have performed exemplary experiments using data from the UCI repository—Pytorch and Tensorflow libraries were used for building neural network and classification process. It turns out that deep learning method works effectively based on reduced training sets. Approximation of decision systems before neural networks learning can be important step to give the opportunity to learn in reasonable time. Full article
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
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Open AccessArticle
Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval
Algorithms 2020, 13(1), 18; https://doi.org/10.3390/a13010018 - 06 Jan 2020
Abstract
In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the [...] Read more.
In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the importance of each binary bit as the same and treat training samples at different positions equally, which causes many data pairs to share the same Hamming distance and a larger retrieval loss at the top position. To handle these problems, we propose a novel method dubbed by the top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method. The core idea is to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom and assign different weight values to their binary bits according to the distribution of query data. Specifically, we design an iterative optimization mechanism to simultaneously learn binary codes and bitwise weights, which makes their learning processes related to each other. When the iterative procedure converges, the binary codes and bitwise weights are effectively adapted to each other. To reduce the training complexity, we relax the discrete constraints of both the binary codes and the indicator function. Furthermore, we pretrain a tensor ordinal graph to decrease the time consumption of computing a relative similarity relationship among data points. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed TORBW method can achieve superior performance over state-of-the-art approaches. Full article
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
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Open AccessArticle
Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
Algorithms 2019, 12(12), 253; https://doi.org/10.3390/a12120253 - 26 Nov 2019
Cited by 3
Abstract
Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main [...] Read more.
Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice. Full article
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
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Open AccessArticle
Idea of Using Blockchain Technique for Choosing the Best Configuration of Weights in Neural Networks
Algorithms 2019, 12(8), 163; https://doi.org/10.3390/a12080163 - 10 Aug 2019
Cited by 5
Abstract
The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training [...] Read more.
The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights. Full article
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
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Open AccessFeature PaperArticle
Distributed Centrality Analysis of Social Network Data Using MapReduce
Algorithms 2019, 12(8), 161; https://doi.org/10.3390/a12080161 - 09 Aug 2019
Cited by 7
Abstract
Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the [...] Read more.
Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset. Full article
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
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