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Electronics
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Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges
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Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 2459

Special Issue Editors

Dr. Zhuang Yang

E-Mail Website
Guest Editor
School of Computer Science & Technology, Soochow University, Suzhou 215006, China
Interests: machine learning; pattern recognition; stochastic optimization; large-scale models
Prof. Dr. Sai Zou

E-Mail Website
Guest Editor
Big Data and Information Engineering College, Guizhou University, Guiyang 550025, China
Interests: next-generation network; biological informatization; smart education
Dr. Yanqun Tang

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 510006, China
Interests: privacy and security in low-altitude networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pattern recognition and classification are fundamental tasks in machine learning and computer vision. In recent years, many innovative discoveries and significant advancements in pattern recognition and classification have been made. Since supervised learning, semi-supervised learning, and ensemble methods are the main approaches of machine learning, it is necessary to explore their performance in differentiated scenarios and solve challenges regarding their practical application, such as efficient image prior modelling, fast and robust large-scale optimization algorithms, etc., especially under large-scale model backgrounds.

In this Special Issue of Electronics, we seek to publish original and creative contributions in the field of supervised and semi-supervised learning (including other advanced approaches) for pattern recognition and classification. Research papers with theoretical, technical, and/or practical approaches, and review articles, are all welcome. Topics of interest include, but are not limited to:

  • Design of novel classifiers (such as deep neural networks, attention mechanisms, capsule networks, etc.);
  • Few-shot learning;
  • Explainable supervisory models;
  • Multimodal/multilabel classification;
  • Robust classification methods (adversarial attack defense, noisy label learning);
  • Graph-based semi-supervised learning;
  • The combination of active learning and semi-supervised learning;
  • Domain adaptation and transfer learning;
  • Time series data classification;
  • The application of contrastive learning in semi-supervised learning;
  • Classification methods for specific scenarios such as biomedicine, remote sensing, etc.

Dr. Zhuang Yang
Prof. Dr. Sai Zou
Dr. Yanqun Tang
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

  • neural networks
  • interpretable classification
  • advanced machine learning algorithms
  • generative models
  • large-scale optimization
  • few-shot learning

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Published Papers (3 papers)

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Research

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34 pages, 605 KB  
Article
A Physically Constrained KPP–Rate-and-State Reaction–Diffusion Framework for Stable Large-Scale Modeling of Stress Evolution
by Boi-Yee Liao
Electronics 2026, 15(5), 1131; https://doi.org/10.3390/electronics15051131 - 9 Mar 2026
Viewed by 235
Abstract
The emergence of large-scale models and machine learning has transformed the modeling of complex nonlinear systems, such as postseismic stress evolution. However, purely data-driven approaches often lack interpretability and numerical stability, leading to physically inconsistent long-term predictions. This study addresses these limitations by [...] Read more.
The emergence of large-scale models and machine learning has transformed the modeling of complex nonlinear systems, such as postseismic stress evolution. However, purely data-driven approaches often lack interpretability and numerical stability, leading to physically inconsistent long-term predictions. This study addresses these limitations by introducing a coupled Kolmogorov–Petrovsky–Piskunov–Rate-and-State (KPP–RS) reaction–diffusion system as a rigorous physical prior for large-scale modeling of stress-driven dynamics. Using analytic semigroup theory and Banach’s fixed-point theorem, we establish the global existence and uniqueness of solutions, ensuring that the governing dynamics are mathematically well posed—a necessary prerequisite for stable learning-based frameworks. We further prove the global dissipativity of the system and identify a bounded absorbing set in the H1 phase space, which imposes intrinsic physical constraints and limits unphysical parameter exploration in large-scale optimization or black-box modeling. In addition, a Courant–Friedrichs–Lewy (CFL) stability condition is derived, providing a theoretical benchmark for time-step selection in numerical implementations, including physics-informed or hybrid neural architectures. This analytical framework supplies a mathematically controlled foundation for developing robust, interpretable, and stable pattern-recognition or time-series representations in complex geophysical systems. Full article
(This article belongs to the Special Issue Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges)
12 pages, 1025 KB  
Article
Enhancing Whisper Fine-Tuning with Discrete Wavelet Transform-Based LoRA Initialization
by Liang Lan, Molin Fang, Yuxuan Chen, Daliang Wang and Wenyong Wang
Electronics 2026, 15(3), 586; https://doi.org/10.3390/electronics15030586 - 29 Jan 2026
Viewed by 330
Abstract
In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency, stability, and deployment friendliness, its performance remains constrained because random initialization fails to [...] Read more.
In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency, stability, and deployment friendliness, its performance remains constrained because random initialization fails to capture the time–frequency structural characteristics of speech signals. To address this limitation, this work proposes a structured initialization mechanism that integrates LoRA with the discrete wavelet transform (DWT). By combining wavelet-based initialization, a multi-scale fusion mechanism, and a residual strategy, the proposed method constructs a low-rank adaptation subspace that better aligns with the local time–frequency properties of speech signals. Discrete Wavelet Transform-Based LoRA Initialization (DWTLoRA) enables LoRA modules to incorporate prior modeling of speech dynamics at the start of fine-tuning, substantially reducing the search space of ineffective directions during early training and improving convergence speed, training stability, and recognition accuracy under low-resource conditions. Experimental results on Sichuan dialect speech recognition based on the Whisper architecture demonstrate that the proposed DWTLoRA initialization outperforms standard LoRA and several PEFT baseline methods in terms of character error rate (CER) and training efficiency, confirming the critical role of signal-structure-aware initialization in low-resource ASR. Full article
(This article belongs to the Special Issue Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges)
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Review

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21 pages, 3660 KB  
Review
The Current State of Research on Reputation Evaluation of Network Nodes
by Jingxiong Xu, Lisheng Huang, Fengjun Zhang, Zuoyuan Niu, Kai Shi and Qinghua Li
Electronics 2025, 14(19), 3900; https://doi.org/10.3390/electronics14193900 - 30 Sep 2025
Viewed by 1280
Abstract
As cybersecurity threats continue to escalate, assessing the security and credibility of critical network nodes, such as web servers, email servers, and URLs, becomes pivotal to ensure network integrity. This entails a comprehensive evaluation of the network nodes’ reputation, employing reputation scores as [...] Read more.
As cybersecurity threats continue to escalate, assessing the security and credibility of critical network nodes, such as web servers, email servers, and URLs, becomes pivotal to ensure network integrity. This entails a comprehensive evaluation of the network nodes’ reputation, employing reputation scores as performance indices to instigate bespoke protective measures, thereby alleviating Internet-associated risks. This paper examines the progress in the realm of IP reputation evaluation, providing an exhaustive analysis of reputation assessment methodologies premised on statistical analysis, similarity detection, and machine learning. Further, it underlines their practical applications and effectiveness in bolstering network security. In a head-to-head comparison of the assorted methods, the paper underscores their merits and demerits relative to implementation specifics and performance. In conclusion, it outlines the evolving trends and challenges in network reputation evaluation, providing a scientific framework and valuable technical references for prompt detection and effective mitigation of latent security threats in the network milieu. Full article
(This article belongs to the Special Issue Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges)
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