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Electronics
Special Issues
Machine Learning and Deep Learning Based Pattern Recognition
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Special Issue "Machine Learning and Deep Learning Based Pattern Recognition"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 1575

Special Issue Editors

Prof. Dr. Jungpil Shin

E-Mail Website
Guest Editor
Pattern Processing Lab, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: pattern recognition; character recognition; image processing; computer vision; human–computer interaction; neurological disease analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Md. Al Mehedi Hasan

E-Mail Website
Guest Editor
Computer Science and Engineering, Rajshahi University of Engineering and Technology(RUET), Rajshahi 6204, Bangladesh
Interests: bioinformatics; artificial intelligence; pattern recognition; medical image and signal processing; machine learning; computer vision
Dr. Hoang D. Le

E-Mail Website
Guest Editor
Computer Communications Laboratory, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: applications of artificial intelligence/machine learning for wireless networks; wireless communication networks; network security

Special Issue Information

Dear Colleagues,

In the modern digital world, patterns can be found in many facets of daily life. They can be physically observed or computationally detected using algorithms. In the digital environment, a pattern is represented by a vector or matrix feature value. Recently, numerous machine learning (ML)- and deep learning (DL)-based techniques have been widely used in order to handle or analyze these feature values in the artificial intelligence (AI) domain. ML is a branch of AI and its goal is to let the computer make its own decisions with minimal human involvement using pattern data. On the other hand, DL is a branch of ML and a popular topic in the field of AI.  Using DL and ML models to extract meaningful features from the given text, image, video, or sensor data and analyze those features is known as pattern recognition (PR). PR has been used in various applications in the fields of engineering such as computer vision, sensor data analysis, natural language processing, speech recognition, robotics, bioinformatics, and so on. The goal of this Special Issue is to publish innovative and technically sound research papers that exhibit theoretical and practical contributions to PR utilizing ML and DL methodologies.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Image processing/segmentation/recognition;
  • Computer vision;
  • Speech recognition;
  • Automated target recognition;
  • Character recognition;
  • Gesture and human activity recognition;
  • Industrial inspection;
  • Medical diagnosis;
  • Health informatics;
  • Biosignal processing;
  • Bioinformatics;
  • Remote sensing ;
  • Healthcare application;
  • ML and DL and the Internet of Things (IoT);
  • Large dataset analysis;
  • Current state-of-the-art and future trends of ML and DL.

Prof. Dr. Jungpil Shin
Prof. Dr. Md. Al Mehedi Hasan 
Dr. Hoang D. Le
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. Electronics is an international peer-reviewed open access semimonthly 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 2000 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

  • machine learning
  • deep learning
  • pattern recognition

Published Papers (2 papers)

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Research

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Article
Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network
by
Md. Moazzem Hossain
,
Md. Ali Hossain
,
Abu Saleh Musa Miah
,
Yuichi Okuyama
,
Yoichi Tomioka
and
Jungpil Shin
Electronics 2023, 12(9), 2082; https://doi.org/10.3390/electronics12092082 - 02 May 2023
Viewed by 546
Abstract
The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality [...] Read more.
The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality reduction plays a significant role in HSI data analysis (e.g., effective processing and seamless interpretation). In this article, a sophisticated technique established as t-Distributed Stochastic Neighbor Embedding (tSNE) following the dimension reduction along with a blended CNN was implemented to improve the visualization and characterization of HSI. In the procedure, first, we employed principal component analysis (PCA) to reduce the HSI dimensions and remove non-linear consistency features between the wavelengths to project them to a smaller scale. Then we proposed tSNE to preserve the local and global pixel relationships and check the HSI information visually and experimentally. Lastly, it yielded two-dimensional data, improving the visualization and classification accuracy compared to other standard dimensionality-reduction algorithms. Finally, we employed deep-learning-based CNN to classify the reduced and improved HSI intra- and inter-band relationship-feature vector. The evaluation performance of 95.21% accuracy and 6.2% test loss proved the superiority of the proposed model compared to other state-of-the-art DR reduction algorithms. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Based Pattern Recognition)
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Communication
Supervised Learning Spectrum Sensing Method via Geometric Power Feature
by
Qian Hu
,
Zhongqiang Luo
and
Wenshi Xiao
Electronics 2023, 12(7), 1616; https://doi.org/10.3390/electronics12071616 - 29 Mar 2023
Viewed by 355
Abstract
In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning [...] Read more.
In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning spectrum sensing method for training and testing based on the actual captured data set. Experimental results show that the detection performance of the GP-based supervised learning spectrum sensing method is better than that of the Energy Statistics (ES) and Differential Entropy (DE)-based supervised learning spectrum sensing methods. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Based Pattern Recognition)
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