Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Maryan Rizinski^1,2,*, Dimitar Trajanov^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-34, 2026, DOI:10.32604/cmc.2025.069678 - 10 November 2025

Abstract Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically, it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, robo-advisory, and regulatory compliance (RegTech). The review focuses on advanced agent-based methodologies, including reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Zheng Yao^*, Puqing Chang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068248 - 10 November 2025

Abstract As Internet of Things (IoT) applications expand, Mobile Edge Computing (MEC) has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices. Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies, conflicting objectives, and limited resources. This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC. We jointly consider task heterogeneity, high-dimensional objectives, and flexible resource scheduling, modeling the problem as a Many-objective optimization. To solve it, we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on More >

Rodolphe Griset^1,#,*, Ioannis Lavdas^2,§, Jiří Guth Jarkovský³

Journal of Quantum Computing, Vol.7, pp. 81-105, 2025, DOI:10.32604/jqc.2025.066064 - 08 December 2025

Abstract Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution. These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces. Unfortunately, the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure, rendering this kind of… More >

Ruikang Ma^1,2, Ziying Huang¹, Zexiu Zhang³, Ruohui Lu⁴, Menghan Li¹, Zhiyi Luo³, Mengni Li⁵, Pengyue Zhang³, Xiaohong Lin³, Guozhuang Zhang^1,*, Linlin Dong^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3721-3748, 2025, DOI:10.32604/phyton.2025.072062 - 01 December 2025

Abstract Dendrobium officinale (DO) is a well-recognized medicinal and edible plant with a long history of application in traditional medicinal practices across China and Southeast Asia. Recent studies have demonstrated that DO is abundant in diverse bioactive compounds, including polysaccharides (DOP), flavonoids, alkaloids, and bibenzyls thought to exert a range of pharmacological effects, such as anti-tumor and immunomodulatory effects. However, our comprehensive understanding of two key aspects—pharmacological functions and biosynthetic mechanisms—of DO’s major constituents remains limited, especially when considered within the clinical contexts of traditional use. To address this gap, this study reviews DO’s historical applications, clinical effects, and… More > Graphic Abstract