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Electronics
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Converging Platform Technologies: Collaborative Innovations and Future Directions
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Converging Platform Technologies: Collaborative Innovations and Future Directions

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (15 November 2025) | Viewed by 11814

Special Issue Editors

Dr. Jihoon Moon

E-Mail Website
Guest Editor
Department of Data Science, Duksung Women’s University, Seoul 01369, Republic of Korea
Interests: trustworthy decision intelligence; data-centric machine learning; LLM-enabled decision support (RAG, tool use); energy/smart infrastructure analytics; resource-aware forecasting and optimization; privacy-aware deployment
Special Issues, Collections and Topics in MDPI journals
Dr. Dongwann Kang

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: computer graphics; image processing; affective computing; visual computing; human perception
Prof. Dr. Young-Ae (Claire) Jung

E-Mail Website
Guest Editor
Department of AI and Software Technology, Sunmoon University, Asan 31460, Republic of Korea
Interests: AI software engineering; advanced ML & DL; text mining & LLMs; genetic/memetic AI; AI ethics & privacy

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to exploring the convergence of platform technologies across diverse disciplines and industries. It seeks to highlight innovations that advance connectivity, functionality, and integration, bridging the gap between theoretical research and practical applications.

We invite authors to submit papers that address any aspect of platform technology, including, but not limited to, computing architectures, smart systems, network solutions, and security frameworks. Contributions that demonstrate novel approaches and offer insights into future challenges and opportunities in platform technologies are particularly welcome.

The purpose of this Special Issue is to assemble a compendium of cutting-edge research that showcases how platform technologies are reshaping the landscape of digital interaction and service delivery. It aims to foster an understanding of these technologies' pivotal roles and to encourage the exploration of potential transformative impacts in various sectors.

By integrating both conference proceedings and external submissions, this Special Issue will enrich the existing dialogue within the technology community. It will serve to expand on the discussions initiated at the 2024 International Conference on Platform Technology and Service (PlatCon-24), providing a broader, more comprehensive view that bridges current research with emerging trends.

Dr. Jihoon Moon
Dr. Dongwann Kang
Prof. Dr. Young-Ae (Claire) Jung
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 250 words) can be sent to the Editorial Office for assessment.

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

  • platform technology integration
  • emerging network systems
  • smart technology solutions
  • security in digital platforms
  • innovative computational models
  • advanced computing platforms
  • intelligent networking solutions
  • multimedia and HCI platforms
  • cybersecurity and data privacy
  • smart grid technologies
  • ubiquitous computing systems
  • cloud and distributed computing
  • machine-to-machine communications
  • virtual and augmented reality
  • artificial intelligence in platform services
  • IOT and service innovation
  • business intelligence platforms
  • educational technologies and e-learning
  • biotechnology and health informatics platforms
  • sustainable and green technologies

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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18 pages, 1356 KB  
Article
OptiPerformer as a Platform for Optical Fiber System Simulation in Distance and In-Class Learning
by Seweryn Lipiński
Electronics 2025, 14(24), 4800; https://doi.org/10.3390/electronics14244800 - 5 Dec 2025
Viewed by 555
Abstract
Simulation-based laboratories have become an essential component of modern engineering education, particularly in courses where access to physical equipment is limited. This paper presents a structured methodology for teaching the fundamentals of optical fiber communication systems using OptiPerformer 18, i.e., a freely available [...] Read more.
Simulation-based laboratories have become an essential component of modern engineering education, particularly in courses where access to physical equipment is limited. This paper presents a structured methodology for teaching the fundamentals of optical fiber communication systems using OptiPerformer 18, i.e., a freely available optical communication simulation platform. The novelty of this work lies in integrating a complete set of parameter-driven laboratory exercises, covering eye-diagram analysis, chromatic dispersion and dispersion compensation, Gaussian pulse propagation, and BER/Q-factor evaluation, into both distance and face-to-face teaching, and validating their effectiveness across four academic years involving more than 200 students. Representative simulation results generated with OptiPerformer are provided to illustrate the learning process and to demonstrate how key transmission impairments and system-level behaviors can be visualized and quantitatively analyzed without specialized hardware. The pedagogical effectiveness of the approach is assessed through student surveys and final grades, showing consistently high learning outcomes and strong student engagement in both remote and in-person settings. These findings indicate that the proposed simulation-based laboratory framework offers a scalable, hardware-independent, and conceptually rich alternative to traditional fiber-optic laboratory classes, supporting deeper understanding of transmission physics and enhancing analytical and problem-solving skills essential in modern optical communication engineering. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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22 pages, 5166 KB  
Article
A Study on Exploiting Temporal Patterns in Semester Records for Efficient Student Dropout Prediction
by Jungjo Na, Kwan Woo Kim and Hyeon Gyu Kim
Electronics 2025, 14(22), 4356; https://doi.org/10.3390/electronics14224356 - 7 Nov 2025
Viewed by 779
Abstract
Academic achievement data are essential in building a model to predict student dropout. When an attribute of the data has multiple values, each representing a student’s achievement earned over a semester, existing methods typically calculate a mean from those values and use it [...] Read more.
Academic achievement data are essential in building a model to predict student dropout. When an attribute of the data has multiple values, each representing a student’s achievement earned over a semester, existing methods typically calculate a mean from those values and use it to build learning data. Such a summary-based approach has been widely used because it can simplify learning processes, including feature extraction. However, model performance can be further improved if patterns in multiple semester values can be properly extracted and used for learning, instead of using summaries. Despite its potential, this problem has not been investigated in previous studies. In this paper, we demonstrate that recurrent neural networks (RNNs) can effectively be used to exploit the patterns in students’ academic records stored by semester. To identify patterns in the data and find solutions suitable for it, various neural network algorithms were compared. Attention was also adopted to improve model performance. Experiments conducted on real student records showed that the gate recurrent unit (GRU) model with multi-head attention achieved an F1 score of 0.9416, which was approximately 5% higher than the existing summary-based approaches. This demonstrates that the semester records exhibit temporal patterns and RNNs can effectively be used to exploit these patterns. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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25 pages, 2645 KB  
Article
Framing the Sequence: Genre-Aligned Photo Curation via Shot-Scale Embedding
by Youngsup Park, Yangmi Lim and Dongwann Kang
Electronics 2025, 14(17), 3434; https://doi.org/10.3390/electronics14173434 - 28 Aug 2025
Viewed by 1713
Abstract
This paper presents a lightweight, genre-conditioned photo curation framework that restructures user-selected image sequences based on cinematic shot scale patterns. Unlike prior frame-level approaches, our method explicitly models sequential rhythm and genre style. The proposed pipeline integrates (1) a MobileNetV3-based shot scale classifier [...] Read more.
This paper presents a lightweight, genre-conditioned photo curation framework that restructures user-selected image sequences based on cinematic shot scale patterns. Unlike prior frame-level approaches, our method explicitly models sequential rhythm and genre style. The proposed pipeline integrates (1) a MobileNetV3-based shot scale classifier optimized for on-device efficiency, (2) a conditional variational autoencoder (cVAE) for embedding temporal shot rhythms conditioned on genre, and (3) a similarity-driven adaptation module that adjusts sequences through swap and crop operations guided by latent distance reduction. Deployed as an iOS application, the system processes an 8-image sequence in ~2.02 s with a footprint under 3 MB. Quantitative evaluations show that the classifier achieved 69.9% Top-1 accuracy (F1 = 0.646), and that adaptation reduced latent distance by 22.7% compared to shuffled baselines. On-device tests confirmed practical feasibility. A user study (n = 24) using Likert ratings revealed that the method improved rhythm perception among film/media experts, though effects on genre recognition and preference were less consistent for general users. Overall, this work contributes a novel, style-aware, and mobile-ready sequencing framework that advances beyond prior frame-level methods and supports applications in memory curation, interactive storytelling, and mobile authoring. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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12 pages, 4006 KB  
Article
Development of Water Quality Analysis for Anomaly Detection and Correlation with Case Studies in Water Supply Systems
by Rahmania Hanifa, Mina Cha, Woochul Kang, Jungwon Yu, Kwang-Ju Kim, Yeo-Myeong Yun and Seongyun Kim
Electronics 2025, 14(10), 1933; https://doi.org/10.3390/electronics14101933 - 9 May 2025
Cited by 2 | Viewed by 3098
Abstract
The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data [...] Read more.
The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data (pH, turbidity, electrical conductivity, temperature, and chlorine), perform anomaly detection across parameters, and conduct a comprehensive investigation of water quality incidents that correlate with detected anomalies in water supply systems. This study can contribute to the development of an early detection and response system related to water quality incidents in water supply systems. Future work will focus on enhancing the application of systems for early detection of water quality incidents by expanding the data, developing anomaly detection methods by applying machine learning techniques, and figuring out the correlations between anomalies and water quality incidents. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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25 pages, 1926 KB  
Article
Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques
by Thi-Thu-Huong Le, Yeonjeong Hwang, Changwoo Choi, Rini Wisnu Wardhani, Dedy Septono Catur Putranto and Howon Kim 
Electronics 2024, 13(22), 4350; https://doi.org/10.3390/electronics13224350 - 6 Nov 2024
Cited by 5 | Viewed by 2716
Abstract
This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and [...] Read more.
This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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Review

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22 pages, 396 KB  
Review
Towards a Unified Digital Ecosystem: The Role of Platform Technology Convergence
by Asif Mehmood, Mohammad Arif and Faisal Mehmood
Electronics 2025, 14(24), 4787; https://doi.org/10.3390/electronics14244787 - 5 Dec 2025
Cited by 1 | Viewed by 1477
Abstract
The rapid evolution of platform technologies is transforming industries, interoperability, and innovation. Despite numerous studies on individual technologies, no prior review unifies AI, IoT, blockchain, and 5G with cross-sector standards, governance, and technical enablers to provide a comprehensive view of platform convergence. This [...] Read more.
The rapid evolution of platform technologies is transforming industries, interoperability, and innovation. Despite numerous studies on individual technologies, no prior review unifies AI, IoT, blockchain, and 5G with cross-sector standards, governance, and technical enablers to provide a comprehensive view of platform convergence. This narrative review synthesizes conceptual and technical literature from 2015–2025, focusing on how converging platform technologies interact across sectors. The review organizes findings by technological enablers, cross-domain integration mechanisms, sector-specific applications, and emergent trends, highlighting systemic synergies and challenges. The study demonstrates that AI, IoT, blockchain, cloud-edge architectures, and advanced communication networks collectively enable interoperable, secure, and adaptive ecosystems. Key enablers include standardized protocols, edge–cloud orchestration, and cross-platform data sharing, while challenges involve cybersecurity, regulatory compliance, and scalability. Sectoral examples span healthcare, finance, manufacturing, smart cities, and autonomous systems. Platform convergence offers transformative potential for sustainable and intelligent systems. Critical research gaps remain in unified architectures, privacy-preserving AI and blockchain mechanisms, and dynamic orchestration of heterogeneous systems. Emerging technologies such as quantum computing and federated learning are poised to further strengthen collaborative ecosystems. This review provides actionable insights for researchers, policymakers, and industry leaders aiming to harness platform convergence for innovation and sustainable development. Full article
(This article belongs to the Special Issue Converging Platform Technologies: Collaborative Innovations and Future Directions)
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