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 >

Praveen Kumar Sekharamantry^1,2,*, Farid Melgani¹, Roberto Delfiore³, Stefano Lusardi³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4215-4238, 2025, DOI:10.32604/cmc.2025.062686 - 19 May 2025

Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

Huiru Cao¹, Shaoxin Li², Xiaomin Li^3,*, Yongxin Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5575-5593, 2025, DOI:10.32604/cmc.2025.061954 - 19 May 2025

Abstract Efficient flight path design for unmanned aerial vehicles (UAVs) in urban environmental event monitoring remains a critical challenge, particularly in prioritizing high-risk zones within complex urban landscapes. Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency. To address these gaps, this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization (ACO) algorithm with an Orthogonal Jump Point Search (OJPS) algorithm. Firstly, a two-dimensional grid model is constructed to simulate urban environments, with key monitoring nodes selected based on… 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 >

Khaoula Ben Abdellafou¹, Kamel Zidi², Ahamed Aljuhani¹, Okba Taouali^1,*, Mohamed Faouzi Harkat³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… 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 >

Yuxin Yan, Huirui Wang, Jingyi Wen, Zerong Lan, Liang Wang^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3059-3073, 2025, DOI:10.32604/cmc.2024.057949 - 16 April 2025

Abstract The development of artificial intelligence (AI) technologies creates a great chance for the iteration of railway monitoring. This paper proposes a comprehensive method for railway utility pole detection. The framework of this paper on railway systems consists of two parts: point cloud preprocessing and railway utility pole detection. This method overcomes the challenges of dynamic environment adaptability, reliance on lighting conditions, sensitivity to weather and environmental conditions, and visual occlusion issues present in 2D images and videos, which utilize mobile LiDAR (Laser Radar) acquisition devices to obtain point cloud data. Due to factors such as… More >

Zhe Gong¹, Ge Yan², Jie Ma¹, Chang-Lin Yan², Fu-Kang Shen¹, Hu Li³, Hua-Ping Wang^1,*

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 473-498, 2025, DOI:10.32604/sdhm.2024.057916 - 03 April 2025

Abstract Vacuum pressure vessels are one of the critical components in the aerospace field, and understanding the mechanical behavior feature is particularly important for safe operation. Therefore, it is meaningful to obtain the stress and strain distributions in the key positions of the vacuum tank, which can contribute to the safe performance assessment, operation efficiency, and fault analysis. Hence, this paper provides the distribution characteristics and variation rules of stress and tank strain of vacuum under different internal and external pressures through the elastic theoretical analysis and iteration method. The quasi-distributed fiber Bragg grating (FBG) sensors… More >

Wenhai Zhao¹, Wanrun Li^1,2,*, Ximei Li^1,2, Shoutu Li³, Yongfeng Du^1,2

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 593-611, 2025, DOI:10.32604/sdhm.2024.057759 - 03 April 2025

Abstract Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies, as well as the inability to achieve precise full-field monitoring, this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion. Firstly, the Lucas-Kanade Tomasi (LKT) optical flow method and a multi-region of interest (ROI) monitoring structure are employed to track pixel displacements, which are subsequently subjected to band pass filtering and resampling operations. Secondly, the actual displacement time history is derived through double integration of the acquired acceleration data and… More >

Aadil Mushtaq Pandith¹, Manni Kumar², Naveen Kumar³, Nitin Goyal^4,*, Sachin Ahuja², Yonis Gulzar⁵, Rashi Rastogi⁶, Rupesh Gupta⁷

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >