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

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 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. Processes is an international peer-reviewed open access monthly 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.

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

• Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems in Processes (15 articles)