Application of Machine Learning and Intelligent Systems

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 13294

Special Issue Editors


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Guest Editor
Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Interests: multidisciplinary application of machine learning techniques; data mining; smart systems

E-Mail Website
Guest Editor
Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Interests: application of machine learning for smart learning environments

Special Issue Information

Dear Colleagues,

The advancements in technology have helped many organizations to generate an enormous amount of data. Such data are exploited to analyze complex physical world processes and systems and make critical decisions at societal and business levels. Machine learning methods are used to build applications that can infer important facts from heterogeneous data sources generated from our daily use of technology. Many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions to information-filtering systems that learn users’ reading preferences, to autonomous vehicles that learn to drive, to smart digital health that helps to automate the medical process, to name a few.

In this Special Issue, we are interested in multidisciplinary research that applies machine learning methods to solve real-world issues. The Special Issue provides an opportunity to discuss the multidisciplinary applications, trends, and directions of machine learning methods and intelligent systems. We welcome original research articles and review contributions reporting applications of machine learning methods in, but not limited to, the following areas:

  • Medical imaging;
  • Bioinformatics;
  • Computer networks;
  • Digital humanities;
  • Network security;
  • Engineering;
  • Constructions;
  • Digital forensics;
  • E-commerce;
  • Banking and finance;
  • Education;
  • Social media;
  • Smart homes;
  • Healthcare;
  • Waste management;
  • Autonomous systems.

Dr. Isah A. Lawal
Prof. Dr. Seifedine Kadry
Dr. Sahar Yassine
Guest Editors

Manuscript Submission Information

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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 2400 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
  • intelligent systems
  • artificial intelligence

Published Papers (8 papers)

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Research

23 pages, 39867 KiB  
Article
Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition
by Xueqing Zhao, Fuquan Ren, Haibo Sun and Qinghong Qi
Electronics 2024, 13(3), 490; https://doi.org/10.3390/electronics13030490 - 24 Jan 2024
Viewed by 764
Abstract
Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more [...] Read more.
Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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22 pages, 607 KiB  
Article
Landmark-Based Domain Adaptation and Selective Pseudo-Labeling for Heterogeneous Defect Prediction
by Yidan Chen and Haowen Chen
Electronics 2024, 13(2), 456; https://doi.org/10.3390/electronics13020456 - 22 Jan 2024
Viewed by 616
Abstract
Cross -project defect prediction (CPDP) is a promising technical means to solve the problem of insufficient training data in software defect prediction. As a special case of CPDP, heterogeneous defect prediction (HDP) has received increasing attention in recent years due to its ability [...] Read more.
Cross -project defect prediction (CPDP) is a promising technical means to solve the problem of insufficient training data in software defect prediction. As a special case of CPDP, heterogeneous defect prediction (HDP) has received increasing attention in recent years due to its ability to cope with different metric sets in projects. Existing studies have proven that using mixed-project data is a potential way to improve HDP performance, but there remain several challenges, including the negative impact of noise modules and the insufficient utilization of unlabeled modules. To this end, we propose a landmark-based domain adaptation and selective pseudo-labeling (LDASP) approach for mixed-project HDP. Specifically, we propose a novel landmark-based domain adaptation algorithm considering marginal and conditional distribution alignment and a class-wise locality structure to reduce the heterogeneity between both projects while reweighting modules to alleviate the negative impact brought by noise ones. Moreover, we design a progressive pseudo-label selection strategy exploring the underlying discriminative information of unlabeled target data to further improve the prediction effect. Extensive experiments are conducted based on 530 heterogeneous prediction combinations that are built from 27 projects using four datasets. The experimental results show that (1) our approach improves the F1-score and AUC over the baselines by 9.8–20.2% and 4.8–14.4%, respectively and (2) each component of LDASP (i.e., the landmark weights and selective pseudo-labeling strategy) can promote the HDP performance effectively. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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16 pages, 2753 KiB  
Article
Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems
by Atefeh Momenikorbekandi and Maysam Abbod
Electronics 2023, 12(23), 4752; https://doi.org/10.3390/electronics12234752 - 23 Nov 2023
Cited by 2 | Viewed by 1341
Abstract
Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage [...] Read more.
Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the variations in production requirements. In this study, novel reinforcement learning (RL) models, including advanced Q-learning (QRL) and RL-based state–action–reward–state–action (SARSA) models, are proposed to enhance the scheduling performance of FJSPs, in order to reduce the total makespan. To more accurately depict the problem realities, two categories of simulated single-machine job shops and multi-machine job shops, as well as the scheduling of a furnace model, are used to compare the learning impact and performance of the novel RL models to other algorithms. FJSPs are challenging to resolve and are considered non-deterministic polynomial-time hardness (NP-hard) problems. Numerous algorithms have been used previously to solve FJSPs. However, because their key parameters cannot be effectively changed dynamically throughout the computation process, the effectiveness and quality of the solutions fail to meet production standards. Consequently, in this research, developed RL models are presented. The efficacy and benefits of the suggested SARSA method for solving FJSPs are shown by extensive computer testing and comparisons. As a result, this can be a competitive algorithm for FJSPs. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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14 pages, 1680 KiB  
Article
AI to Train AI: Using ChatGPT to Improve the Accuracy of a Therapeutic Dialogue System
by Karolina Gabor-Siatkowska, Marcin Sowański, Rafał Rzatkiewicz, Izabela Stefaniak, Marek Kozłowski and Artur Janicki
Electronics 2023, 12(22), 4694; https://doi.org/10.3390/electronics12224694 - 18 Nov 2023
Cited by 1 | Viewed by 1721
Abstract
In this work, we present the use of one artificial intelligence (AI) application (ChatGPT) to train another AI-based application. As the latter one, we show a dialogue system named Terabot, which was used in the therapy of psychiatric patients. Our study was motivated [...] Read more.
In this work, we present the use of one artificial intelligence (AI) application (ChatGPT) to train another AI-based application. As the latter one, we show a dialogue system named Terabot, which was used in the therapy of psychiatric patients. Our study was motivated by the fact that for such a domain-specific system, it was difficult to acquire large real-life data samples to increase the training database: this would require recruiting more patients, which is both time-consuming and costly. To address this gap, we have employed a neural large language model: ChatGPT version 3.5, to generate data solely for training our dialogue system. During initial experiments, we identified intents that were most often misrecognized. Next, we fed ChatGPT with a series of prompts, which triggered the language model to generate numerous additional training entries, e.g., alternatives to the phrases that had been collected during initial experiments with healthy users. This way, we have enlarged the training dataset by 112%. In our case study, for testing, we used 2802 speech recordings originating from 32 psychiatric patients. As an evaluation metric, we used the accuracy of intent recognition. The speech samples were converted into text using automatic speech recognition (ASR). The analysis showed that the patients’ speech challenged the ASR module significantly, resulting in deteriorated speech recognition and, consequently, low accuracy of intent recognition. However, thanks to the augmentation of the training data with ChatGPT-generated data, the intent recognition accuracy increased by 13% relatively, reaching 86% in total. We also emulated the case of an error-free ASR and showed the impact of ASR misrecognitions on the intent recognition accuracy. Our study showcased the potential of using generative language models to develop other AI-based tools, such as dialogue systems. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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14 pages, 2173 KiB  
Article
Improving Norwegian Translation of Bicycle Terminology Using Custom Named-Entity Recognition and Neural Machine Translation
by Daniel Hellebust and Isah A. Lawal
Electronics 2023, 12(10), 2334; https://doi.org/10.3390/electronics12102334 - 22 May 2023
Viewed by 1721
Abstract
The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging [...] Read more.
The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging for online translators, such as Google. For this reason, local companies outsource translation or translate product descriptions manually, which is cumbersome. In light of the Norwegian B2B bicycle industry, this paper explores transfer learning to improve the machine translation of bicycle-specific terminology from English to Norwegian, including generic text. Firstly, we trained a custom Named-Entity Recognition (NER) model to identify cycling-specific terminology and then adapted a MarianMT neural machine translation model for the translation process. Due to the lack of publicly available bicycle-terminology-related datasets to train the proposed models, we created our dataset by collecting a corpus of cycling-related texts. We evaluated the performance of our proposed model and compared its performance with that of Google Translate. Our model outperformed Google Translate on the test set, with a SacreBleu score of 45.099 against 36.615 for Google Translate on average. We also created a web application where the user can input English text with related bicycle terminologies, and it will return the detected cycling-specific words in addition to a Norwegian translation. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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19 pages, 9965 KiB  
Article
EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
by Tong Xu, Yazhou Chen, Yuming Wang, Dongxiao Zhang and Min Zhao
Electronics 2023, 12(7), 1631; https://doi.org/10.3390/electronics12071631 - 30 Mar 2023
Cited by 3 | Viewed by 1154
Abstract
In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and [...] Read more.
In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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18 pages, 4211 KiB  
Article
Prediction Method for Sugarcane Syrup Brix Based on Improved Support Vector Regression
by Songjie Hu, Yanmei Meng and Yibo Zhang
Electronics 2023, 12(7), 1535; https://doi.org/10.3390/electronics12071535 - 24 Mar 2023
Cited by 2 | Viewed by 2013
Abstract
The brix of syrup is an important parameter in sugar production. To accurately measure syrup brix, a novel measurement method based on support vector regression (SVR) is presented. With the resonant frequency and quality factor as inputs and syrup brix as the output, [...] Read more.
The brix of syrup is an important parameter in sugar production. To accurately measure syrup brix, a novel measurement method based on support vector regression (SVR) is presented. With the resonant frequency and quality factor as inputs and syrup brix as the output, a mathematical model of the relationship between the resonant frequency, quality factor, and syrup brix is established. Simultaneously, the particle swarm optimization (PSO) algorithm is used to optimize the penalty coefficient and radial basis kernel function of SVR to improve the performance of the model. The calculation model is trained and tested using the collected experimental data. The results show that the mean absolute error, mean absolute percentage error, and root mean square error of the syrup brix calculation model based on the improved SVR model can reach 0.74 °Bx, 2.24%, and 0.90 °Bx, respectively, while the determination coefficient can reach 0.9985. The simulation of the online measurement of syrup brix in the actual production process proves the excellent prediction performance of the syrup brix calculation model based on the improved PSO–SVR model, which can thus be used to predict the syrup brix. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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11 pages, 3028 KiB  
Article
Merchant Recommender System Using Credit Card Payment Data
by Suyoun Yoo and Jaekwang Kim
Electronics 2023, 12(4), 811; https://doi.org/10.3390/electronics12040811 - 6 Feb 2023
Cited by 2 | Viewed by 2117
Abstract
As the size of the domestic credit card market is steadily growing, the marketing method for credit card companies to secure customers is also changing. The process of understanding individual preferences and payment patterns has become an essential element, and it has developed [...] Read more.
As the size of the domestic credit card market is steadily growing, the marketing method for credit card companies to secure customers is also changing. The process of understanding individual preferences and payment patterns has become an essential element, and it has developed a sophisticated personalized marketing method to properly understand customers’ interests and meet their needs. Based on this, a personalized system that recommends products or stores suitable for customers acts to attract customers more effectively. However, the existing research model implementing the General Framework using the neural network cannot reflect the major domain information of credit card payment data when applied directly to store recommendations. This study intends to propose a model specializing in the recommendation of member stores by reflecting the domain information of credit card payment data. The customers’ gender and age information were added to the learning data. The industry category and region information of the settlement member stores were reconstructed to be learned together with interaction data. A personalized recommendation system was realized by combining historical card payment data with customer and member store information to recommend member stores that are highly likely to be used by customers in the future. This study’s proposed model (NMF_CSI) showed a performance improvement of 3% based on HR@10 and 5% based on NDCG@10, compared to previous models. In addition, customer coverage was expanded so that the recommended model can be applied not only to customers actively using credit cards but also to customers with low usage data. Full article
(This article belongs to the Special Issue Application of Machine Learning and Intelligent Systems)
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