Wenlong Ke^1,2,*, Zilong Li¹, Peiyu Chen¹, Benfeng Chen¹, Jinglin Lv¹, Qiang Wang², Ziyi Jia², Shigen Shen¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025

Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >

Mingyang Zhao^1,2,*, Xiaojun Li^1,3, Miao Li^1,2, Bangbang Mu^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2827-2846, 2025, DOI:10.32604/cmc.2025.065251 - 03 July 2025

Abstract The control of gas extraction in coal mines relies on the effectiveness of gas extraction. The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such as hydraulic fracturing to pre-extract gas in the drill holes. Therefore, the real-time detection of the drill pipe status is closely related to the effectiveness of gas extraction. To achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock… More >

Seongsoo Cho¹, Yeonwoo Lee^2,*, Cheolhee Yoon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3769-3793, 2025, DOI:10.32604/cmes.2025.066298 - 30 June 2025

Abstract As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions, efficient clustering mechanisms are vital to ensure stable and scalable communication. Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems (ITS). This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering (GOA-VNET) algorithm, an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks (VANETs), leveraging the Grasshopper Optimization Algorithm (GOA) to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems (ITS). The proposed GOA-VNET employs an… More >

Zuhaib Nishtar^1,*, Fangzong Wang¹, Fawwad Hassan Jaskani², Hussain Afzaal³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2919-2956, 2025, DOI:10.32604/cmes.2025.065098 - 30 June 2025

Abstract This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the… More >

Saurabh Sanjay Singh^*, Rahul Joshi, Deepak Gupta

Journal on Artificial Intelligence, Vol.7, pp. 177-203, 2025, DOI:10.32604/jai.2025.065078 - 30 June 2025

Abstract This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem. In this problem, each job has a fixed sequence of operations to be performed on parallel machines, and each operation can be assigned to any capable machine. The problem statement aims to schedule every job in a way that minimizes the total energy consumption of the job shop. The paper’s primary objective is to develop a reinforcement learning-based scheduling framework using the Advantage Actor-Critic algorithm to generate energy-efficient schedules that are computationally fast… More >

Qiang Gao¹, Lei Shen^1,*, Jiaming Shi², Xinfa Gu², Shanyun Gu¹, Yuwei Ge¹, Yang Xie¹, Xiaoqiong Zhu¹, Baoguo Zang¹, Ming Zhang¹, Muhammad Shahzad Nazir², Jie Ji²

Energy Engineering, Vol.122, No.7, pp. 2767-2800, 2025, DOI:10.32604/ee.2025.065600 - 27 June 2025

Abstract The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems. Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multi-modal data fusion. This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large language models (LLMs) with adaptive optimization, achieving three key innovations: (1) A hierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction, (2) Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds (Daubechies-4 basis, 6-level decomposition), and… More >

Jiaqi Wang, Jian Su^*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 125-152, 2025, DOI:10.32604/cmc.2025.064309 - 09 June 2025

Abstract The Intelligent Transportation System (ITS), as a vital means to alleviate traffic congestion and reduce traffic accidents, demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things (IoT) technologies. The enhancement of its performance largely depends on breakthrough advancements in object detection technology. However, current object detection technology still faces numerous challenges, such as accuracy, robustness, and data privacy issues. These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions. This study provides a comprehensive review of the development… More >

Lulu Zhang, Xuehui Du^*, Wenjuan Wang, Yu Cao, Xiangyu Wu, Shihao Wang

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1901-1919, 2025, DOI:10.32604/cmc.2025.064270 - 09 June 2025

Abstract To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features More >

Shaowei He, Wenchao Cui^*, Gang Li, Hairun Xu, Xiang Chen, Yu Tai

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 861-886, 2025, DOI:10.32604/cmc.2025.063979 - 09 June 2025

Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >

Mohammad Tabrez Quasim^1,*, Khair Ul Nisa¹, Mohammad Shahid Husain², Abakar Ibraheem Abdalla Aadam¹, Mohammed Waseequ Sheraz¹, Mohammad Zunnun Khan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1169-1187, 2025, DOI:10.32604/cmc.2025.062989 - 09 June 2025

Abstract Smart edge computing (SEC) is a novel paradigm for computing that could transfer cloud-based applications to the edge network, supporting computation-intensive services like face detection and natural language processing. A core feature of mobile edge computing, SEC improves user experience and device performance by offloading local activities to edge processors. In this framework, blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers, protecting against potential security threats. Additionally, Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically. IoT applications that require significant resources… More >