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Applications of Machine Learning and Convolutional Neural Networks
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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 42256

Special Issue Editor

Prof. Dr. Cheng-Yuan Tang

E-Mail Website
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

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. Information 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.

Keywords

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

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Published Papers (7 papers)

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Research

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13 pages, 3483 KiB  
Article
Classification of English Words into Grammatical Notations Using Deep Learning Technique
by Muhammad Imran, Sajjad Hussain Qureshi, Abrar Hussain Qureshi and Norah Almusharraf
Information 2024, 15(12), 801; https://doi.org/10.3390/info15120801 - 11 Dec 2024
Cited by 2 | Viewed by 1051
Abstract
The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully [...] Read more.
The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully grasp the potential advantages of AI, more research is needed to improve, validate, and test AI algorithms and architectures. Grammatical notations provide a word’s information to the readers. If a word’s images are properly extracted and categorized using a CNN, it can help non-native English speakers improve their learning habits. The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. This situation stresses the need to develop a computer-based system using a machine learning algorithm to classify words into proper grammatical notations. A convolutional neural network (CNN) was applied to classify English words into nine classes: noun, pronoun, adjective, determiner, verb, adverb, preposition, conjunction, and interjection. A simulation of the selected model was performed in MATLAB. The model achieved an overall accuracy of 97.22%. The CNN showed 100% accuracy for pronouns, determiners, verbs, adverbs, and prepositions; 95% for nouns, adjectives, and conjunctions; and 90% for interjections. The significant results (p < 0.0001) of the chi-square test supported the use of the CNN by non-native English learners. The proposed approach is an important source of word classification for non-native English learners by putting the word image into the model. This not only helps beginners in English learning but also helps in setting standards for evaluating documents. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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15 pages, 428 KiB  
Article
Recognition of Industrial Spare Parts Using an Optimized Convolutional Neural Network Model
by Chandralekha Mohan, Takfarinas Saber and Priyadharshini Jayadurga Nallathambi
Information 2024, 15(12), 793; https://doi.org/10.3390/info15120793 - 10 Dec 2024
Viewed by 960
Abstract
Spare parts search and retrieval processes are of paramount importance in manufacturing and supply chains. Image recognition using 2D and 3D image properties plays an important part in the success of such processes, as it facilitates the identification of the types and components [...] Read more.
Spare parts search and retrieval processes are of paramount importance in manufacturing and supply chains. Image recognition using 2D and 3D image properties plays an important part in the success of such processes, as it facilitates the identification of the types and components associated with spare parts, a step that is crucial for their success. In this article, a novel Deep Learning-based object recognition model based on a convolutional neural network architecture is proposed and constructed using stacked convolutional layers to extract and learn features of the spare parts efficiently with the goal of improving the effectiveness of the spare part image recognition process. The proposed model is assessed using industrial spare parts datasets, and its performance is compared against different transfer learning models using precision, accuracy, recall, and F1 score. The proposed model demonstrated efficiency in spare parts recognition and achieved the highest accuracy compared to state-of-the-art image recognition models. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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25 pages, 1715 KiB  
Article
Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions
by Okba Fergani, Yassine Himeur, Raihane Mechgoug, Shadi Atalla, Wathiq Mansoor and Nacira Tkouti
Information 2024, 15(11), 692; https://doi.org/10.3390/info15110692 - 3 Nov 2024
Viewed by 1067
Abstract
The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions [...] Read more.
The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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17 pages, 4475 KiB  
Article
Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications
by Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga, Fabio Batista Mota, Victor Maricato and Luiz Anastacio Alves
Information 2024, 15(10), 626; https://doi.org/10.3390/info15100626 - 11 Oct 2024
Cited by 1 | Viewed by 3749
Abstract
Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help [...] Read more.
Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help identify emerging trends. This study aims to map GNN applications, research directions, and key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals a rising trend in GNN publications, especially since 2018. Computer Science, Engineering, and Telecommunications play significant roles in GNN research, with a focus on deep learning, graph convolutional networks, neural networks, convolutional neural networks, and machine learning. China and the USA combined account for 76.4% of the publications. Chinese universities concentrate on graph convolutional networks, deep learning, feature extraction, and task analysis, whereas American universities focus on machine learning and deep learning. The study also highlights the importance of Chemistry, Physics, Mathematics, Imaging Science & Photographic Technology, and Computer Science in their respective knowledge communities. In conclusion, the bibliometric analysis provides an overview of GNN research, showing growing interest and applications across various disciplines, and highlighting the potential of GNNs in solving complex problems and the need for continued research and collaboration. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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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
Cited by 2 | Viewed by 1182
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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23 pages, 846 KiB  
Review
Machine Learning Approaches in Multi-Cancer Early Detection
by Maryam Hajjar, Somayah Albaradei and Ghadah Aldabbagh
Information 2024, 15(10), 627; https://doi.org/10.3390/info15100627 - 11 Oct 2024
Cited by 1 | Viewed by 2648
Abstract
Cancer is a prominent global cause of mortality, primarily due to delayed detection leading to limited treatment options. Current screening methods are mostly invasive and involve complex lengthy processes with high costs. Moreover, each screening typically focuses on a single type of cancer. [...] Read more.
Cancer is a prominent global cause of mortality, primarily due to delayed detection leading to limited treatment options. Current screening methods are mostly invasive and involve complex lengthy processes with high costs. Moreover, each screening typically focuses on a single type of cancer. This imposes a growing need for innovative, precise, and minimally invasive methods for early cancer detection. With the current advances in assay technologies and data science, multi-cancer early detection (MCED) tests are gaining increased interest in the research community as they offer potential for earlier diagnosis and improved patient outcomes. Different approaches are followed for MCED, and multiple machine learning methods are considered. In this paper, we systematically explore various MCED studies and their applied machine learning (ML) models for different types of biomarker data. We discuss the strengths and limitations of different study designs and compare their performance. Future directions are proposed, emphasizing the importance of integrating multi-omics data, enhancing model transparency, and fostering collaborative efforts to develop robust, cost effective and clinically applicable MCED tools. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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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
Cited by 69 | Viewed by 30727
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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