Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >

He Su, Jianwei Gao, Kang Kong^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025

Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

Zhipeng Qin^1,2,*, Hanbing Yan³, Biyang Zhang², Peng Wang², Yitao Li³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.063308 - 19 May 2025

Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating More >

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4979-4998, 2025, DOI:10.32604/cmc.2025.060881 - 19 May 2025

Abstract Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable… More >

Bin Jiang¹, Houbin Wang^2,*

Energy Engineering, Vol.122, No.5, pp. 2001-2057, 2025, DOI:10.32604/ee.2025.062765 - 25 April 2025

Abstract Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality, strong coupling, nonlinearity, and non-convexity, a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper. The algorithm is based on external elite archive and Pareto dominance, and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm. Average entropy and cubic chaotic mapping initialization strategies are proposed to increase population diversity. In the proposed method, we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.… More >

Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

Liang Zhou¹, Qiyao Hu^1,2,3,*, Xianlin Peng^4,5, Qianlong Liu⁶

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2375-2401, 2025, DOI:10.32604/cmc.2025.061894 - 16 April 2025

Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >

Hui Jin^1,2,*, Shuaiqi Xu¹, Chengyi Duan¹, Ruixue He¹, Ji Zhang¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1041-1055, 2025, DOI:10.32604/cmc.2025.060304 - 26 March 2025

Abstract Instance segmentation is crucial in various domains, such as autonomous driving and robotics. However, there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices. Therefore, it is essential to enhance detection speed while maintaining high accuracy. In this study, we propose you only look once-layer fusion (YOLO-LF), a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications. Based on the You Only Look Once version 8 nano (YOLOv8n) framework, we introduce a lightweight convolutional module and design a lightweight layer aggregation module… More >

Shijie Xiang, Dong Zhou, Dan Tian^*, Zihao Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244 - 26 March 2025

Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… More >

Kening Wang^1,#, Feipeng Gao^2,#, Jie Yang^1,2,*, Hao Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4797-4820, 2025, DOI:10.32604/cmc.2025.060042 - 06 March 2025

Abstract With the rapid advancement of Voice over Internet Protocol (VoIP) technology, speech steganography techniques such as Quantization Index Modulation (QIM) and Pitch Modulation Steganography (PMS) have emerged as significant challenges to information security. These techniques embed hidden information into speech streams, making detection increasingly difficult, particularly under conditions of low embedding rates and short speech durations. Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals. To address these challenges, this paper proposes an Efficient Sliding Window… More >