Special Issue "Algorithms for Pattern Recognition"

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

Deadline for manuscript submissions: 30 December 2019.

Special Issue Editor

Guest Editor
Dr. Dawid Połap Website E-Mail
Institute of Mathematics, Silesian University of Technology, 44-100, Gliwice, Poland
Interests: machine learning; neural networks; fuzzy systems; pattern recognition; decision support systems; heuristics

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 1000 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 (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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
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)
Show Figures

Figure 1

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
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)
Show Figures

Figure 1

Back to TopTop