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Electronics
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Advances in AI Engineering: Exploring Machine Learning Applications
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Advances in AI Engineering: Exploring Machine Learning Applications

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

Deadline for manuscript submissions: 15 January 2025 | Viewed by 8867

Special Issue Editors

Prof. Dr. Quan Qian

E-Mail
Guest Editor
Department of Machine Intelligence, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Interests: machine learning; materials informatics; AI-related engineering applications; data security; privacy-preserving machine learning, etc.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xing Wu

E-Mail
Guest Editor
Department of Machine Intelligence, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Interests: machine learning with multimodal data; materials informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we enter an era where artificial intelligence (AI) evolves from a supplementary tool to a primary catalyst for innovation across diverse scientific and industrial sectors, our foremost objective is to spotlight the inventive machine learning (ML) applications. We seek to accelerate diverse methods by which AI and ML are utilized to address intricate challenges, unlocking untapped potential in the process. Spanning a broad spectrum of subjects, it explores AI’s role not only in electronics but also various other industrial scenarios. We especially welcome submissions that demonstrate creative applications of AI techniques in problem-solving, predictive analysis, and the improvement of efficiency across various fields.

We are eager to publish both original research articles and reviews in this Special Issue. Areas of research may encompass, but are not limited to: AI applications, AI in industry, AI for electronics, AI for materials, AI for engineering, etc.

We look forward to receiving your contributions.

Prof. Dr. Quan Qian
Prof. Dr. Xing Wu
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 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

  • AI applications
  • AI in industry
  • AI for electronic
  • AI for materials

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Published Papers (5 papers)

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Research

33 pages, 7989 KiB  
Article
Emergency Vehicle Classification Using Combined Temporal and Spectral Audio Features with Machine Learning Algorithms
by Dontabhaktuni Jayakumar, Modugu Krishnaiah, Sreedhar Kollem, Samineni Peddakrishna, Nadikatla Chandrasekhar and Maturi Thirupathi
Electronics 2024, 13(19), 3873; https://doi.org/10.3390/electronics13193873 - 30 Sep 2024
Viewed by 877
Abstract
This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square [...] Read more.
This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound’s frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model’s ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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22 pages, 2067 KiB  
Article
FedGAT-DCNN: Advanced Credit Card Fraud Detection Using Federated Learning, Graph Attention Networks, and Dilated Convolutions
by Mengqiu Li and John Walsh
Electronics 2024, 13(16), 3169; https://doi.org/10.3390/electronics13163169 - 11 Aug 2024
Viewed by 2766
Abstract
Credit card fraud detection is a critical issue for financial institutions due to significant financial losses and the erosion of customer trust. Fraud not only impacts the bottom line but also undermines the confidence customers place in financial services, leading to long-term reputational [...] Read more.
Credit card fraud detection is a critical issue for financial institutions due to significant financial losses and the erosion of customer trust. Fraud not only impacts the bottom line but also undermines the confidence customers place in financial services, leading to long-term reputational damage. Traditional machine learning methods struggle to improve detection accuracy with limited data, adapt to new fraud techniques, and detect complex fraud patterns. To address these challenges, we present FedGAT-DCNN, a model integrating a Graph Attention Network (GAT) and dilated convolutions within a federated learning framework. FedGAT-DCNN employs federated learning, allowing financial institutions to collaboratively train models using local datasets, enhancing accuracy and robustness while maintaining data privacy. Incorporating a GAT enables continuous model updates across institutions, quickly adapting to new fraud patterns. Dilated convolutions extend the model’s receptive field without extra computational overhead, improving detection of subtle and complex fraudulent activities. Experiments on the 2018CN and 2023EU datasets show that FedGAT-DCNN outperforms traditional models and other federated learning methods, achieving a ROC-AUC of 0.9712 on the 2018CN dataset and 0.9992 on the 2023EU dataset. These results highlight FedGAT-DCNN’s robustness, accuracy, and applicability in real-world fraud detection scenarios. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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14 pages, 5399 KiB  
Article
Artificial Intelligence Application in the Field of Functional Verification
by Diana Dranga and Catalin Dumitrescu
Electronics 2024, 13(12), 2361; https://doi.org/10.3390/electronics13122361 - 17 Jun 2024
Viewed by 1340
Abstract
The rising interest in Artificial Intelligence and the increasing time invested in functional verification processes are driving the demand for AI solutions in this field. Functional verification is the process of verifying that the Register Transfer Layer (RTL) implementation behaves according to the [...] Read more.
The rising interest in Artificial Intelligence and the increasing time invested in functional verification processes are driving the demand for AI solutions in this field. Functional verification is the process of verifying that the Register Transfer Layer (RTL) implementation behaves according to the specifications provided. This is performed using a hardware verification language (HVL) such as SystemVerilog combined with the Universal Verification Methodology (UVM). Reading, identifying the key elements from multiple documentations, creating the verification plan, building the verification environment, implementing the tests defined, and achieving 100% coverage are usually the steps performed in order to complete the verification process. The verification process is considered finalized when functional coverage is at 100%. There are multiple ideas on how the process can be aided by AI, such as underlining the essential information from documentation, which would help in understanding faster how the Register Transfer Layer implementation works, thus vastly reducing time. In this paper, to greatly reduce the time spent on functional verification, two Convolutional Neural Network (CNN) architectures are implemented to properly classify the information across different documents; both approaches have significant and promising results. The database used for this classification task was created by the researchers using different documentations available. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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20 pages, 3258 KiB  
Article
AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)
by Yibrah Gebreyesus, Damian Dalton, Davide De Chiara, Marta Chinnici and Andrea Chinnici
Electronics 2024, 13(9), 1628; https://doi.org/10.3390/electronics13091628 - 24 Apr 2024
Cited by 2 | Viewed by 1084
Abstract
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) [...] Read more.
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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Graphical abstract

22 pages, 10096 KiB  
Article
Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map
by Minoru Sasaki, Yuki Tsuda and Kojiro Matsushita
Electronics 2024, 13(6), 1082; https://doi.org/10.3390/electronics13061082 - 14 Mar 2024
Viewed by 2083
Abstract
In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices [...] Read more.
In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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