Applications of Machine Learning and Convolutional Neural Networks

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

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 3324

Special Issue Editor


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Guest Editor
Department of Technology for Smart Living, Huafan University, Taipei 223011, Taiwan
Interests: computer vision; image processing; deep learning; artificial intelligence

Special Issue Information

Dear Colleagues,

In recent years, Artificial Intelligence (AI) has been widely used in various fields, and there are many research areas within this domain such as machine learning and deep learning. Machine learning approaches are traditionally divided into three broad categories: supervised learning, unsupervised learning and reinforcement learning. Convolutional neural networks play important roles in machine learning/deep learning. Applications of machine learning and convolutional neural networks are now pervasive in many fields beyond conventional computer engineering areas. The aim of this Special Issue is to discuss new ideas and recent experimental results in convolutional neural network applications and to promote the contemporary use of convolutional neural networks for addressing challenging tasks. We welcome studies and applications that propose methods based on different architectures of convolutional neural networks.

Topics of interest include, but are not limited to, the following:

  • artificial intelligence tools and applications;
  • automatic control;
  • natural language processing;
  • computer vision and speech understanding;
  • data mining and analysis;
  • supervised and unsupervised learning;
  • heuristic and AI planning strategies;
  • intelligent system;
  • robotics;
  • evolutionary and genetic algorithms;
  • applications for automatic driving;
  • applications for 3D point clouds;
  • classification and recognition.

Prof. Dr. Cheng-Yuan Tang
Guest Editor

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Keywords

  • machine learning
  • convolutional neural networks
  • artificial intelligence
  • deep learning

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Published Papers (2 papers)

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Research

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13 pages, 876 KiB  
Article
Fault Line Selection Method for Power Distribution Network Based on Graph Transformation and ResNet50 Model
by Haozhi Wang, Yuntao Shi and Wei Guo
Information 2024, 15(7), 375; https://doi.org/10.3390/info15070375 - 28 Jun 2024
Viewed by 563
Abstract
Low-current grounding systems are the main grounding method used in power distribution networks and belong to non-direct grounding systems. The most common fault in this type of system is a single-phase grounding fault, which may cause electrical fires and endanger personal safety. Due [...] Read more.
Low-current grounding systems are the main grounding method used in power distribution networks and belong to non-direct grounding systems. The most common fault in this type of system is a single-phase grounding fault, which may cause electrical fires and endanger personal safety. Due to the difficulty of troubleshooting, the selection of fault lines in low-current grounding systems has always been an important research topic in power system relay protection. This study proposes a new approach for fault identification of power lines based on the Euler transformation and deep learning. Firstly, the current signals of the distribution network are rapidly Fourier-transformed to obtain their frequencies for constructing reference signals. Then, the current signals are combined with the reference signals and transformed into images using Euler transformation in the complex plane. The images are then classified using a residual network model. The convolutional neural network in the model can automatically extract fault feature vectors, thus achieving the identification of faulty lines. The simulation was conducted based on the existing model, and extensive data training and testing were performed. The experimental results show that this method has good stability, fast convergence speed, and high accuracy. This technology can effectively accomplish fault identification in power distribution networks. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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Review

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34 pages, 786 KiB  
Review
Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications
by Ibomoiye Domor Mienye, Theo G. Swart and George Obaido
Information 2024, 15(9), 517; https://doi.org/10.3390/info15090517 - 25 Aug 2024
Viewed by 2399
Abstract
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, [...] Read more.
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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