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Electronics
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Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing
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Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 5581

Special Issue Editors

Dr. Pei-Chun Lin

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan
Interests: statistical modeling; hypothesis test; big data analysis; performance tuning; artificial intelligence; robotic computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tarik Taleb

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, 44801 Bochum, Germany
Interests: network function virtualization; network software define visualization; software-defined networking; network slicing; mobile cloud networking

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has played a pivotal role in revolutionizing traditional computing methods and moving towards next-generation computing. As we push the boundaries of AI computing power through various applications in different domains, enhancing AI-driven computing has become an innovative, deep-thinking approach to designing hardware, software, and systems architectures.

To summarize the rapid progress and emerging trends in this field, we are pleased to present this Special Issue on “Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing”.

This Special Issue will integrate AI-driven architectures for next-generation computing in various fields. Recent scientific discoveries and emerging technologies will also be highlighted in this Special Issue.

We welcome submissions on a wide range of topics, including, but not limited to, the following:

  • AI-Embedded System Architectures: AI-enabled system design, quantum artificial intelligence (QAI), quantum computing, neuromorphic computing, and edge and fog computing.
  • High-Performance Computing (HPC): including AI-enhanced algorithms, data processing techniques, statistical modeling, hypothesis testing, hyperparameter tuning, big data analytics for system performance tuning, AI-based predictive maintenance, and reliability analysis.
  • Artificial Intelligence Internet of Things (AIOT): including AI-driven sensor networks, intelligent environments, and AI-enabled edge computing.
  • Robotic Computing: including sentiment classification, genre classification, linguistic classification, emotional expression, and AI for autonomous systems and robotics.
  • Cloud Computing: Research using AI to optimize cloud computing infrastructure, resource allocation, and service management.
  • Software Development: including AI-driven coding, testing, and debugging of deep learning and machine learning for system optimization and automating engineering processes.
  • Network Optimization: techniques including 5G/6G networks, network function virtualization (NFV), software-defined networking (SDN), software-defined visualization (SDV) for optimizing and managing next-generation networks, and security and privacy in AI-enhanced network systems.
  • Case Studies and Applications: Research on real-world deployments of AI-driven system implementations in various industries, such as finance, transportation, smart cities, healthcare, and manufacturing.

Dr. Pei-Chun Lin
Prof. Dr. Tarik Taleb
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

  • statistical modeling
  • performance evaluations
  • hypothesis test
  • hyperparameter tuning
  • artificial intelligence
  • semantic communication
  • 6G
  • networking
  • cybersecurity

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Published Papers (4 papers)

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Research

18 pages, 3579 KB  
Article
A Novel Real-Time Data Stream Transfer System in Edge Computing of Smart Logistics
by Yue Wang, Zhihao Yu, Xiaoling Yao and Haifeng Wang
Electronics 2025, 14(18), 3599; https://doi.org/10.3390/electronics14183599 - 10 Sep 2025
Viewed by 462
Abstract
Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. [...] Read more.
Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios. Full article
(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)
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19 pages, 2268 KB  
Article
Toward the Implementation of Text-Based Web Page Classification and Filtering Solution for Low-Resource Home Routers Using a Machine Learning Approach
by Audronė Janavičiūtė, Agnius Liutkevičius and Nerijus Morkevičius
Electronics 2025, 14(16), 3280; https://doi.org/10.3390/electronics14163280 - 18 Aug 2025
Viewed by 457
Abstract
Restricting and filtering harmful content on the Internet is a serious problem that is often addressed even at the state and legislative levels. Existing solutions for restricting and filtering online content are usually installed on end-user devices and are easily circumvented and difficult [...] Read more.
Restricting and filtering harmful content on the Internet is a serious problem that is often addressed even at the state and legislative levels. Existing solutions for restricting and filtering online content are usually installed on end-user devices and are easily circumvented and difficult to adapt to larger groups of users with different filtering needs. To mitigate this problem, this study proposed a model of a web page classification and filtering solution suitable for use on home routers or other low-resource web page filtering devices. The proposed system combines the constantly updated web page category list approach with machine learning-based text classification methods. Unlike existing web page filtering solutions, such an approach does not require additional software on the client-side, is more difficult to circumvent for ordinary users and can be implemented using common low-resource routers intended for home and organizations usage. This study evaluated the feasibility of the proposed solution by creating the less resource-demanding implementations of machine learning-based web page classification methods adapted for low-resource home routers that could be used to classify and filter unwanted Internet pages in real-time based on the text of the page. The experimental evaluation of softmax regression, decision tree, random forest, and linear SVM (support vector machine) machine learning methods implemented in the C/C++ programming language was performed using a commercial home router Asus RT-AC85P with 256 MB RAM (random access memory) and MediaTek MT7621AT 880 MHz CPU (central processing unit). The implementation of the linear SVM classifier demonstrated the best accuracy of 0.9198 and required 1.86 s to process a web page. The random forest model was only slightly faster (1.56 s to process a web page), while its accuracy reached only 0.7879. Full article
(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)
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14 pages, 2772 KB  
Article
Critical Considerations for Observing Cross Quantum Capacitance in Electric-Double-Layer-Gated Transistors Based on Two-Dimensional Crystals
by Jacob D. Eisensmith, Pratik P. Dholabhai and Ke Xu
Electronics 2025, 14(9), 1811; https://doi.org/10.3390/electronics14091811 - 29 Apr 2025
Cited by 1 | Viewed by 826
Abstract
Cross quantum capacitance (CQC) has been proposed as an extension to traditional quantum capacitance (TQC) in systems where strong interfacial screening between spatially separated charge layers modifies the total capacitance—particularly in electric-double-layer-gated transistors (EDLTs) based on two-dimensional (2D) crystals. In this work, we [...] Read more.
Cross quantum capacitance (CQC) has been proposed as an extension to traditional quantum capacitance (TQC) in systems where strong interfacial screening between spatially separated charge layers modifies the total capacitance—particularly in electric-double-layer-gated transistors (EDLTs) based on two-dimensional (2D) crystals. In this work, we revisit a theoretical model of CQC to evaluate its relevance under experimentally realistic conditions. By systematically analyzing the model’s behavior across key parameter spaces, we identify the specific conditions under which CQC leads to the non-monotonic dependence of capacitance on inter-plate distance—a proposed experimental signature of CQC. However, we find that these conditions—requiring similar effective masses, high charge densities, and strong charge asymmetry—are highly restrictive and difficult to realize in typical EDLTs. Instead, we highlight a more experimentally accessible regime in which CQC enhances total capacitance beyond TQC predictions, even in the absence of non-monotonicity. These results clarify the limitations of the existing model and suggest concrete strategies for probing CQC in nanoscale devices, emphasizing the need for new theoretical frameworks that explicitly incorporate both ionic and electronic conductors. Full article
(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)
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15 pages, 2204 KB  
Article
The Effectiveness of Generative Adversarial Network-Based Oversampling Methods for Imbalanced Multi-Class Credit Score Classification
by I Nyoman Mahayasa Adiputra, Pei-Chun Lin and Paweena Wanchai
Electronics 2025, 14(4), 697; https://doi.org/10.3390/electronics14040697 - 11 Feb 2025
Cited by 4 | Viewed by 3160
Abstract
Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative [...] Read more.
Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative Adversarial Network (GAN)-based oversampling methods, including CTGAN, CopulaGAN, WGAN-GP, and DraGAN, in addressing this issue. By synthesizing realistic data for minority classes and integrating it with majority class data, the study benchmarks these GAN-based methods across classical (KNN, Decision Tree, Logistic Regression) and ensemble machine learning models (XGBoost, Random Forest, LightGBM). Evaluation metrics such as accuracy and F1-score reveal that WGAN-GP consistently achieves superior performance, especially when combined with Random Forest, outperforming other methods in balancing dataset representation and enhancing classification accuracy. The results showed that WGAN-GP + RF achieved 0.873 in accuracy, 0.936 F1-score in the “good” class, 0.806 F1-score in the “poor” class, and 0.816 F1-score in the “standard” class. The findings underscore the potential of GAN-based oversampling in improving multi-class credit score classification and highlight future directions, including hybrid sampling and cost-sensitive learning, to address remaining challenges. Full article
(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)
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