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Electronics
Special Issues
Intelligent Data Analysis and Learning
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Intelligent Data Analysis and Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2434

Special Issue Editors

Prof. Dr. Junhua Ding

E-Mail Website
Guest Editor
The Anuradha and Vikas Sinha Department of Data Science, University of North Texas, Denton, TX 76203, USA
Interests: automated software engineering; biomedical computing; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Haihua Chen

E-Mail Website
Guest Editor
The Anuradha and Vikas Sinha Department of Data Science, University of North Texas, Denton, TX 76203, USA
Interests: natural language processing; data mining; data quality; AI testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. W. Eric Wong

E-Mail Website
Guest Editor
Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
Interests: system and software dependability (quality, reliability, security, and safety)

Special Issue Information

Dear Colleagues,

Intelligent data are data that have been processed and refined for building intelligent systems. This Special Issue on “Intelligent Data Analysis and Learning” will explore the latest advancements and applications in the field of data quality with regard to data-driven artificial intelligence We invite original research papers, reviews, and case studies that delve into innovative techniques for analysing, learning, and interpreting intelligent data. Topics of interest include, but are not limited to, data quality evaluation and assurance, data security and privacy, high-dimensional data analysis, high-dimensional data reduction learning, in-context learning in deep learning, and the evaluation of large language models and their applications across various domains such as healthcare, finance, and software engineering. Our goal is to provide a comprehensive platform for researchers and practitioners to share insights, methodologies, and experiences that push the boundaries of intelligent data analysis and learning. This Special Issue will highlight cutting-edge research that not only advances our shared theoretical understanding but also demonstrates practical utility in solving real-world problems, fostering an interdisciplinary approach to data-driven discovery and innovation.

Prof. Dr. Junhua Ding
Dr. Haihua Chen
Prof. Dr. W. Eric Wong
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

  • intelligent data
  • data quality
  • data-driven artificial intelligence
  • large language model
  • deep learning
  • data security and privacy
  • in-context learning
  • high-dimensional data analysis
  • high-dimensional data reduction learning

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Published Papers (2 papers)

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Research

23 pages, 768 KiB  
Article
Robust Momentum-Enhanced Non-Negative Tensor Factorization for Accurate Reconstruction of Incomplete Power Consumption Data
by Dengyu Shi and Tangtang Xie
Electronics 2025, 14(2), 351; https://doi.org/10.3390/electronics14020351 - 17 Jan 2025
Viewed by 890
Abstract
Power consumption (PC) data are fundamental for optimizing energy use and managing industrial operations. However, with the widespread adoption of data-driven technologies in the energy sector, maintaining the integrity and quality of these data has become a significant challenge. Missing or incomplete data, [...] Read more.
Power consumption (PC) data are fundamental for optimizing energy use and managing industrial operations. However, with the widespread adoption of data-driven technologies in the energy sector, maintaining the integrity and quality of these data has become a significant challenge. Missing or incomplete data, often caused by equipment failures or communication disruptions, can severely affect the accuracy and reliability of data analyses, ultimately leading to poor decision-making and increased operational costs. To address this, we propose a Robust Momentum-Enhanced Non-Negative Tensor Factorization (RMNTF) model, which integrates three key innovations. First, the model utilizes adversarial loss and L2 regularization to enhance its robustness and improve its performance when dealing with incomplete data. Second, a sigmoid function is employed to ensure that the results remain non-negative, aligning with the inherent characteristics of PC data and improving the quality of the analysis. Finally, momentum optimization is applied to accelerate the convergence process, significantly reducing computational time. Experiments conducted on two publicly available PC datasets, with data densities of 6.65% and 4.80%, show that RMNTF outperforms state-of-the-art methods, achieving an average reduction of 16.20% in imputation errors and an average improvement of 68.36% in computational efficiency. These results highlight the model’s effectiveness in handling sparse and incomplete data, ensuring that the reconstructed data can support critical tasks like energy optimization, smart grid maintenance, and predictive analytics. Full article
(This article belongs to the Special Issue Intelligent Data Analysis and Learning)
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25 pages, 2178 KiB  
Article
User Sentiment Analysis of the Shared Charging Service for China’s G318 Route
by Mei Wang, Siva Shankar Ramasamy, Xi Yu, Mutong Liu, Ahmad Yahya Dawod and Huayue Chen
Electronics 2024, 13(22), 4335; https://doi.org/10.3390/electronics13224335 - 5 Nov 2024
Viewed by 1028
Abstract
Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing [...] Read more.
Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing factors and their mechanisms vary significantly across regions, particularly along the Chengdu–Lhasa (G318) route, which features large elevation changes, diverse climatic conditions, rugged terrain, and frequent geological disasters, making the influencing factors particularly complex. This study analyzes comment texts from 38 shared charging stations along the G318 route in the e-Charging APP, totaling 15,214 comments. A comprehensive approach is employed, including high-frequency word analysis, term frequency–inverse document frequency (TF-IDF) comparison, co-occurrence semantic network and co-word matrix feature correlation analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis. This multifaceted analysis explores core themes, user viewpoints, and sentiments in the comments, focusing on users’ perspectives on service quality, usage experience, and environmental impact of the charging stations. The findings indicate that charging speed, service attitude, environment, operational status of hardware and software, and pricing are key factors influencing user sentiment. Users have a high demand for the perfection of supporting facilities of shared charging stations, directly affecting user satisfaction and indirectly influencing the brand image and market competitiveness of enterprises. Full article
(This article belongs to the Special Issue Intelligent Data Analysis and Learning)
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