Sabina-Cristiana Necula^*, Napoleon-Alexandru Sireteanu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070861 - 09 December 2025

Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… More >

Yu Zhao, Zhijie Zhou^*, Hongdong Fan, Xiaoxia Han, Jie Wang, Manlin Chen

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 73-91, 2024, DOI:10.32604/iasc.2024.042285 - 29 March 2024

Abstract In industrial production and engineering operations, the health state of complex systems is critical, and predicting it can ensure normal operation. Complex systems have many monitoring indicators, complex coupling structures, non-linear and time-varying characteristics, so it is a challenge to establish a reliable prediction model. The belief rule base (BRB) can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities. Since each indicator of the complex system can reflect the health state to some extent, the BRB is built based on the causal relationship… More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Souad Larabi-Marie-Sainte³, Mohamed K. Nour⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Anwer Mustafa Hilal¹, Mesfer Al Duhayyim⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3055-3071, 2022, DOI:10.32604/cmc.2022.024490 - 29 March 2022

Abstract Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process More >

Rongxing Duan¹, Yanni Lin¹, Longfei Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.1, pp. 17-34, 2017, DOI:10.3970/cmes.2017.113.015

Abstract Fault tolerant technology has greatly improved the reliability of modern systems on one hand and makes their failure mechanisms more complex on the other. The characteristics of dynamics of failure, diversity of distribution and epistemic uncertainty always exist in these systems, which increase the challenges in the reliability assessment of these systems significantly. This paper presents a novel reliability analysis framework for complex systems within which the failure rates of components are expressed in interval numbers. Specifically, it uses a dynamic fault tree (DFT) to model the dynamic fault behaviors and copes with the epistemic More >