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Electronics
Special Issues
Artificial Intelligence-Driven Emerging Applications
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Artificial Intelligence-Driven Emerging Applications

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1348

Special Issue Editors

Dr. Jinwen Liang

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: data security; cryptography; AI security
Special Issues, Collections and Topics in MDPI journals
Dr. Jixin Zhang

E-Mail Website
Guest Editor
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Interests: AI and security

Special Issue Information

Dear Colleagues,

This Special Issue focuses on research and applications related to artificial intelligence (AI), and aims to serve as a comprehensive platform for disseminating cutting-edge research, innovative methodologies, and practical applications in the field of artificial intelligence (AI). This collection aims to highlight significant advancements and address emerging challenges within AI, with an emphasis on large language models, security and various application domains. This Special Issue seeks to compile contributions from leading researchers, practitioners, and industry experts who are at the forefront of innovations in AI. By focusing on both theoretical foundations and real-world applications, we aim to foster a deeper understanding of how AI can be leveraged to solve complex problems across a range of sectors.

This Special Issue will not only provide fresh perspectives and insights, but will also serve as a bridge that connects theory with practice, and academia with industry. By presenting broad and in-depth analyses, it aims to advance the comprehensive development of AI, ensuring that this technology remains at the forefront of scientific and technological progress.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Large language model theory, methods and applications;
  • Knowledge graph retrieval-augmented generation;
  • Ethics and security of large language model;
  • Artificial intelligence-aided design;
  • Artificial intelligence-aided education;
  • Artificial intelligence for network and system security;
  • Security and privacy in artificial intelligence.

Dr. Jinwen Liang
Dr. Jixin Zhang
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

  • artificial intelligence
  • large language models
  • application
  • security and privacy

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

Published Papers (3 papers)

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Research

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18 pages, 2263 KiB  
Article
Rapid Right Coronary Artery Extraction from CT Images via Global–Local Deep Learning Method Based on GhostNet
by Yanjun Li, Takaaki Yoshimura and Hiroyuki Sugimori
Electronics 2025, 14(7), 1399; https://doi.org/10.3390/electronics14071399 - 31 Mar 2025
Viewed by 296
Abstract
The right coronary artery plays a crucial role in cardiac function and its accurate extraction and 3D reconstruction from CT images are essential for diagnosing and treating coronary artery disease. This study proposes a novel, automated, deep learning pipeline that integrates a transformer-based [...] Read more.
The right coronary artery plays a crucial role in cardiac function and its accurate extraction and 3D reconstruction from CT images are essential for diagnosing and treating coronary artery disease. This study proposes a novel, automated, deep learning pipeline that integrates a transformer-based network with GhostNet to improve segmentation and 3D reconstruction. The dataset comprised CT images from 32 patients, with the segmentation model effectively extracting vascular cross-sections, achieving an F1 score of 0.887 and an Intersection over Union of 0.797. Meanwhile, the proposed model achieved an inference speed of 7.03 ms, outperforming other state-of-the-art networks used for comparison, making it highly suitable for real-time clinical applications. Compared to conventional methods, the proposed approach demonstrates superior segmentation performance while maintaining computational efficiency. The results indicate that this framework has the potential to significantly improve diagnostic accuracy and interventional planning for coronary artery disease. Future work will focus on expanding dataset diversity, refining real-time processing capabilities, and extending the methodology to other vascular structures. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)
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25 pages, 7436 KiB  
Article
A Co-Optimization Method for Analog IC Placement and Routing Based on Sequence Pairs and Random Forests
by Zhouhu Huo, Niannian Sun, Chaofei Zhang, Bingbing Li and Jixin Zhang
Electronics 2025, 14(7), 1349; https://doi.org/10.3390/electronics14071349 - 28 Mar 2025
Viewed by 331
Abstract
In the physical design of integrated circuits (ICs), conventional methodologies treat placement and routing as two sequential and independent steps, resulting in placement adjustments that do not sufficiently account for routing requirements. Such decoupled optimization leads to various routing challenges, including congestion and [...] Read more.
In the physical design of integrated circuits (ICs), conventional methodologies treat placement and routing as two sequential and independent steps, resulting in placement adjustments that do not sufficiently account for routing requirements. Such decoupled optimization leads to various routing challenges, including congestion and excessive wirelength. To address these limitations, we propose a novel iterative co-optimization framework that simultaneously considers placement and routing. Our methodology establishes an interdependent relationship between placement and routing, where routing is guided by the placement results and placement is adaptively adjusted based on the routing feedback. The proposed framework comprises two key components. First, we introduce an efficient gridless routing algorithm based on line exploration, which rapidly generates routing results by leveraging the placement state and netlist connectivity. The routing connectivity ratio and wirelength are then incorporated as the primary optimization metrics for placement refinement. Second, we develop an advanced placement optimization algorithm that integrates random forest techniques with Monte Carlo-based optimization. This algorithm systematically integrates routing information to guide iterative placement refinement, with the goal of achieving a higher routing connectivity ratio and a reduction in wirelength. We evaluated our approach on a dataset provided by Empyrean. Compared to the baseline placements in the dataset, the algorithm proposed in this work achieved an average improvement of 8.03% in terms of routing connectivity ratio and an average reduction of 18.33% in wirelength. Additionally, a comparative analysis of four optimization algorithms under the proposed co-optimization framework and the traditional half-perimeter wirelength (HPWL) method reveals achieved improvements of 12.41%, 14.16%, 13.87%, and 14.02% in the routing connectivity ratio, respectively, significantly outperforming the HPWL-based method. These results substantiate the efficacy of our co-optimization approach in enhancing IC physical design outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)
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Review

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48 pages, 1127 KiB  
Review
Artificial Intelligence vs. Efficient Markets: A Critical Reassessment of Predictive Models in the Big Data Era
by Antonio Pagliaro
Electronics 2025, 14(9), 1721; https://doi.org/10.3390/electronics14091721 - 23 Apr 2025
Viewed by 443
Abstract
This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance [...] Read more.
This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance across different market regimes or reconcile statistical significance with economic relevance. We analyze techniques ranging from traditional statistical models to advanced deep learning architectures, finding that ensemble methods like Extra Trees, Random Forest, and XGBoost consistently outperform single classifiers, achieving directional accuracy of up to 86% in specific market conditions. Our analysis reveals that hybrid approaches integrating multiple data sources demonstrate superior performance by capturing complementary market signals, yet many models showing statistical significance fail to generate economic value after accounting for transaction costs and market impact. By addressing methodological challenges including backtest overfitting, regime changes, and implementation constraints, we provide a novel comprehensive framework for rigorous model assessment that bridges the divide between academic research and practical implementation. This review makes three key contributions: (1) a reconciliation of the Efficient Market Hypothesis with AI-driven predictability through an adaptive market framework, (2) a multi-dimensional evaluation methodology that extends beyond classification accuracy to financial performance, and (3) an identification of promising research directions in explainable AI, transfer learning, causal modeling, and privacy-preserving techniques that address current limitations. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)
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