Special Issues
Table of Content

Big Data and Artificial Intelligence in Control and Information System

Submission Deadline: 31 July 2025 View: 1533 Submit to Special Issue

Guest Editors

Prof. Didier El Baz

Email: elbaz@laas.fr

Affiliation: LAAS-CNRS, Université de Toulouse, Toulouse 31400, France

Homepage:

Research Interests: optimization, parallel and distributed computing

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Dr. Lei Shi

Email: leiky_shi@cuc.edu.cn

Affiliation: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China

Homepage:

Research Interests: Cross-media retrieval, data analysis and mining, knowledge discovery, artificial intelligence

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Dr. Feifei Kou

Email: koufeifei000@bupt.edu.cn

Affiliation: School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Homepage:

Research Interests: Artificial intelligence, big data mining, semantic learning, search and recommendation

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Dr. Pengfei Zhang

Email: zpf@aust.edu.cn

Affiliation: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China

Homepage:

Research Interests: Data privacy and Trustworthy artificial intelligence

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Summary

In recent years, big data, artificial intelligence and intelligent information processing are emerging research fields that have received widespread attention from computer science, intelligent control area and information system as well as from management science and other areas.


The focus of this special issue is on improvement control and information system with emphasis on big data, artificial intelligence and intelligent information processing. We solicit and publish original research papers on the estimation, algorithms and methodologies that highlight novel data processing and analysis technologies for big data of control system and smart computing.


Potential topics include but are not limited to:

● Techniques, models and algorithms for big data in control and information system

● Big data processing approaches for control system

● Data mining, topic modeling and data science

● Networked infrastructure and platform for smart computing in control and information system

● Big data analytics and model

● Deep learning and artificial intelligence for control big data and smart computing

● Cross-media retrieval and object detection for control big data

● Models and tools for intelligent information processing in control system

● Communication and networking to control system applications

● Machine learning for control big data expression and analytics

● Optimization intelligent processing method for control and information system

● Data-driven distributed optimization and control


Keywords

big data, artificial intelligence, models and algorithms, control and information system, data mining, intelligent information processing, control system applications

Published Papers


  • Open Access

    ARTICLE

    Toward Intrusion Detection of Industrial Cyber-Physical System: A Hybrid Approach Based on System State and Network Traffic Abnormality Monitoring

    Junbin He, Wuxia Zhang, Xianyi Liu, Jinping Liu, Guangyi Yang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064402
    (This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
    Abstract The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System (ICPS), enhancing intelligence and autonomy. However, this transition also expands the attack surface, introducing critical security vulnerabilities. To address these challenges, this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection. Specifically, an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering (IVB-NCA-NLKF) method is developed to model nonlinear system dynamics, enabling optimal state estimation in multi-sensor ICPS environments. Intrusions within the physical sensing system are identified by More >

  • Open Access

    ARTICLE

    Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

    Peiying Zhang, Yihong Yu, Jing Liu, Chong Lv, Lizhuang Tan, Yulin Zhang
    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654
    (This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
    Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

  • Open Access

    ARTICLE

    Lightweight Classroom Student Action Recognition Method Based on Spatiotemporal Multimodal Feature Fusion

    Shaodong Zou, Di Wu, Jianhou Gan, Juxiang Zhou, Jiatian Mei
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1101-1116, 2025, DOI:10.32604/cmc.2025.061376
    (This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
    Abstract The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos, providing a foundation for realizing intelligent and accurate teaching. However, the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition. In this research article, with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios, a lightweight multi-modal fusion action recognition approach is put forward. This proposed method is capable of enhancing the… More >

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