Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.8
Journals
Processes
Special Issues
Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems, 2nd Edition
share announcement

Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems, 2nd Edition

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 3048

Special Issue Editors

Prof. Dr. Xiong Luo

E-Mail Website1 Website2
Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: machine learning; optimization; complex system; intelligent processing
Special Issues, Collections and Topics in MDPI journals
Dr. Manman Yuan

E-Mail Website
Guest Editor
School of Computer Science, Inner Mongolia University, Hohhot 010021, China
Interests: machine learning; complex system; intelligent processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of modern technology, our world is becoming increasingly dependent on data-driven systems. Emerging paradigms such as the Internet of Things (IoT), cloud computing, and multi-agent control systems have enabled the generation of large amounts of heterogeneous data. While this data could facilitate intelligent decision-making and optimize business operations, its effective processing and analysis remain substantial challenges. Overcoming these challenges necessitates the development of novel methodologies and scalable intelligent technologies to extract actionable insights from complex and high-dimensional data environments.

Traditional data processing methods often fail to fully utilize the large amount of information embedded within large-scale data, so they have a limited ability to support intelligent, context-aware decision-making. In contrast, machine-learning-assisted intelligent processing can extract actionable insights by aggregating target-specific data from varying sources, such as network behaviors, database activities, application interactions, and user behaviors. Then, appropriate algorithms can be used to analyze these heterogeneous datasets and infer patterns, promoting the development of machine learning models that are capable of making data-driven, intelligent decisions in complex network environments.

Therefore, machine-learning-assisted intelligent processing for big data has become a critical research direction in multiple domains. It facilitates the development of adaptive and sustainable data modeling systems that are tailored to various applications, including smart city infrastructure, brain-inspired computing and industrial automation. It is curcial to integrate scalable, intelligent learning techniques in order to meet the growing demand for sustainability, reliability, and real-time responsiveness in next-generation intelligent systems.

To summarize, owing to their data-learning capabilities, machine-learning-assisted intelligent processing and optimization are poised to transform the future of numerous applications and industries, potentially playing a crucial role in the advancement of artificial-intelligence-driven systems.

This Special Issue welcomes the submission of high-quality articles that focus on recent advancements in modeling technology for both machine-learning-assisted intelligent optimization and its applications. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Machine-learning-based intelligent processing for modeling complex manufacturing systems;
  • Metaheuristic algorithms for system identification and optimization;
  • Multisource data fusion for complex industrial systems;
  • Mobile computing and sensing for real-time system simulation;
  • Distributed multi-agent modeling algorithms and their industrial applications;
  • Industrial applications of complex system theory;
  • Data-driven intelligent modeling for brain computing;
  • Stability and qualitative analysis of complex networks;
  • Malware detection and classification for industrial control systems;
  • Intelligent control of multi-agent systems;
  • The diagnosis and treatment of human brain diseases based on intelligent dynamic modeling;
  • Other related topics.

Prof. Dr. Xiong Luo
Dr. Manman Yuan
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. Processes 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

  • intelligent processing
  • machine learning
  • optimization
  • complex system
  • control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 2231 KB  
Article
Token Injection Transformer for Enhanced Fine-Grained Recognition
by Bing Ma, Zhengbei Jin, Junyi Li, Jindong Li, Pengfei Zhang, Xiaohui Song and Beibei Jin
Processes 2026, 14(3), 492; https://doi.org/10.3390/pr14030492 - 30 Jan 2026
Viewed by 614
Abstract
Fine-Grained Visual Classification (FGVC) involves distinguishing highly similar subordinate categories within the same basic-level class, presenting significant challenges due to subtle inter-class variations and substantial intra-class diversity. While Vision Transformer (ViT)-based approaches have demonstrated potential in this domain, they remain limited by two [...] Read more.
Fine-Grained Visual Classification (FGVC) involves distinguishing highly similar subordinate categories within the same basic-level class, presenting significant challenges due to subtle inter-class variations and substantial intra-class diversity. While Vision Transformer (ViT)-based approaches have demonstrated potential in this domain, they remain limited by two key issues: (1) the progressive loss of gradient-based edge and texture signals during hierarchical token aggregation and (2) insufficient extraction of discriminative fine-grained features. To overcome these limitations, we propose a Gradient-Aware Token Injection Transformer, a novel framework that explicitly incorporates gradient magnitude and orientation into token embeddings. This multi-modal feature fusion mechanism enhances the model’s capacity to preserve and leverage critical fine-grained visual cues. Extensive experiments on four standard FGVC benchmarks demonstrate the superiority of our approach, achieving 92.9% top-1 accuracy on CUB-200-2011, 90.5% on iNaturalist 2018, 93.2% on NABirds, and 95.3% on Stanford Cars, thereby validating its effectiveness and robustness. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems, 2nd Edition)
►▼ Show Figures

Figure 1

23 pages, 528 KB  
Article
Domain-Specific Retrieval-Augmented Generation with Adaptive Embedding and Knowledge Distillation-Based Re-Ranking
by Hao Luo, Xiong Luo, Weibo Zhao, Qiaojuan Peng, Ke Chen, Yinghui Liu and Congcong Du
Processes 2026, 14(1), 99; https://doi.org/10.3390/pr14010099 - 27 Dec 2025
Viewed by 1144
Abstract
Retrieval-augmented generation (RAG) has emerged as an effective approach for analyzing massive and diverse data. It offers promising avenues for energy management and intelligent decision support amid the accelerating digital transformation of the power industry. However, when applied to this specialized domain, traditional [...] Read more.
Retrieval-augmented generation (RAG) has emerged as an effective approach for analyzing massive and diverse data. It offers promising avenues for energy management and intelligent decision support amid the accelerating digital transformation of the power industry. However, when applied to this specialized domain, traditional RAG systems face two key challenges: (1) poor comprehension of domain-specific terminology, leading to irrelevant retrieval, and (2) limited precision in re-ranking the retrieved results. To address these limitations, this paper presents an innovative integrated optimization framework. The framework enhances RAG performance in the electric power domain through two key strategies. First, we adapt a base embedding model to the domain using contrastive learning and iteratively refine hard negative samples to improve retrieval quality. Second, we employ a large language model (LLM) as a teacher to distill re-ranking knowledge into a lightweight bidirectional encoder representations from transformers (BERT) model, using a hybrid loss function that combines mean squared error (MSE) loss and margin ranking loss. The framework aims to simultaneously improve the model’s understanding of domain-specific terminology and the re-ranking accuracy of critical information. Experimental results on both a private power-domain dataset and the public DuReader_robust benchmark demonstrate that the proposed framework achieves significant performance gains. Comprehensive ablation studies confirm the necessity of each component and reveal their synergistic effects within the framework. Furthermore, sensitivity analyses of key hyperparameters confirm the effectiveness of our hybrid loss and identify optimal configurations that enhance both retrieval and generation performance. This work not only introduces an effective optimization framework tailored for domain-specific RAG applications but also advances industrial intelligence by enhancing the accuracy and reliability of information services. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems, 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 2631 KB  
Review
Machine Learning and Hybrid Approaches in the Energy Valorization of Contaminated Sludge: Global Trends and Perspectives
by Segundo Jonathan Rojas-Flores, Rafael Liza, Renny Nazario-Naveda, Félix Díaz, Daniel Delfin-Narciso, Moisés Gallozzo Cardenas and Anibal Alviz-Meza
Processes 2026, 14(2), 363; https://doi.org/10.3390/pr14020363 - 20 Jan 2026
Viewed by 788
Abstract
While the technological foundation for sludge valorization (anaerobic digestion and pyrolysis) is mature, a significant disconnect exists between traditional research and the advanced application of artificial intelligence. This study identifies that Machine Learning (ML) remains in a peripheral position, representing an untapped frontier [...] Read more.
While the technological foundation for sludge valorization (anaerobic digestion and pyrolysis) is mature, a significant disconnect exists between traditional research and the advanced application of artificial intelligence. This study identifies that Machine Learning (ML) remains in a peripheral position, representing an untapped frontier for achieving predictive and circular systems. The methodology involved a quantitative bibliometric analysis of 190 Scopus-indexed documents (2005–2025). We analyzed indicators of scientific production, collaboration, and thematic evolution using Bibliometrix and VOSviewer 1.6.20. The results reveal a rapidly growing research field, predominantly led by Chinese institutions. The temporal analysis projects a productivity peak around 2033. Core topics include established technologies like anaerobic digestion and pyrolysis. However, network and keyword analyses reveal an emerging trend toward hydrothermal processes and, crucially, the early incorporation of ML. However, ML still occupies a peripheral position within the main scientific discourse, highlighting a gap between traditional research and the advanced application of artificial intelligence. The study systematizes existing knowledge and demonstrates that, although the technological foundation is mature, the deep integration of ML represents the future frontier for achieving sludge valorization systems that are more predictive, efficient, and aligned with the principles of the circular economy. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop