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Electronics
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Big Data Analytics and Information Technology for Smart Cities
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Big Data Analytics and Information Technology for Smart Cities

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2381

Special Issue Editors

Dr. Luís Miguel Matos

E-Mail Website
Guest Editor
ALGORITMI R&D Centre, Department of Information Systems, University of Minho, 4804-533 Guimarães, Portugal
Interests: deep learning; machine learning; automated machine learning; eXplainable artificial intelligence (XAI)
Dr. Filippo Sciarrone

E-Mail Website
Guest Editor
Faculty of Economics, Universitas Mercatorum, 00186 Rome, Italy
Interests: e-learning; MOOCs; machine learning; learning analytics; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Temperini

E-Mail Website
Guest Editor
Associate Professor, DIAG-Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: e-learning; MOOCs; machine learning; learning analytics; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The forthcoming Special Issue on “Big Data Analytics and Information Technology for Smart Cities” seeks to offer a forum for the exchange of knowledge and practices regarding the utilization of big data analytics and information technology in the development of smart urban environments. This Special Issue will place a particular emphasis on the application of these technologies in various aspects of urban planning, management, and sustainability, such as transportation, energy, water management, waste management, etc. Moreover, this Special Issue will consider the implementation of data-driven approaches to address pressing urban challenges (e.g., pollution, resource management). We welcome papers that present novel approaches, techniques, and tools for big data analytics and IT in smart cities, as well as case studies that demonstrate the effectiveness of these approaches in real-world settings. The aim of this Special Issue is to provide a comprehensive overview of the state of the art in big data analytics and IT for smart cities, and to identify future research directions and opportunities within this field.

Dr. Luís Miguel Matos
Dr. Filippo Sciarrone
Dr. Marco Temperini
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

  • big data analytics
  • smart cities
  • information technology
  • urban planning
  • sustainability
  • transportation
  • resource management
  • intelligent systems for education and learning

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Published Papers (2 papers)

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Research

22 pages, 16054 KiB  
Article
Machine Learning-Based Grading of Engine Health for High-Performance Vehicles
by Edgar Amalyan and Shahram Latifi
Electronics 2025, 14(3), 475; https://doi.org/10.3390/electronics14030475 - 24 Jan 2025
Viewed by 761
Abstract
This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. [...] Read more.
This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. Data are collected from sensors monitoring revolutions per minute, boost, rail pressure, timing, and temperature. The data are processed for supervised learning and analyzed using visualizations such as a heatmap and t-SNE plots. Models are trained, innovatively tuned through hyperparameter optimization, and tested for their ability to grade new data logs. The results highlight K-Neighbors, Extra Trees, and Extreme Gradient Boosting as exceptional regressors for this task. The automated grading of engine health and performance enhances objectivity and efficiency in the tuning process and potentially serves as a basis for a digital twin. The developed methodology is discussed in the context of health evaluation for any sensor-based system, with practical applications extending across various domains and industries. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Technology for Smart Cities)
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24 pages, 5134 KiB  
Article
A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks
by Jaehyun Lee, Youngho Cho, Ryungeon Lee, Simon Yuk, Jaepil Youn, Hansol Park and Dongkyoo Shin
Electronics 2025, 14(2), 374; https://doi.org/10.3390/electronics14020374 - 18 Jan 2025
Viewed by 1090
Abstract
Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data [...] Read more.
Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Technology for Smart Cities)
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