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  • Open Access

    REVIEW

    Monocular 3D Human Pose Estimation for REBA Ergonomics: A Critical Review of Recent Advances

    Ahmad Mwfaq Bataineh1,2,*, Ahmad Sufril Azlan Mohamed1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 93-124, 2025, DOI:10.32604/cmc.2025.064250 - 09 June 2025

    Abstract Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assessment (REBA) pose estimation by leveraging progress in three-dimensional human modeling. This survey provides an extensive overview of recent advancements, particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks. By reviewing literature from 2016 to 2024, this study offers a current and comprehensive analysis of techniques, existing challenges, and emerging trends in three-dimensional human pose estimation. In contrast to traditional reviews organized by learning paradigms, this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder (MSD)… More >

  • Open Access

    ARTICLE

    Context Encoding Deep Neural Network Driven Spectral Domain 3D-Optical Coherence Tomography Imaging in Purtscher Retinopathy Diagnosis

    Anand Deva Durai Chelladurai1, Theena Jemima Jebaseeli2, Omar Alqahtani1, Prasanalakshmi Balaji1,*, Jeniffer John Simon Christopher3

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1101-1122, 2025, DOI:10.32604/cmc.2025.062278 - 09 June 2025

    Abstract Optical Coherence Tomography (OCT) provides cross-sectional and three-dimensional reconstructions of the target tissue, allowing precise imaging and quantitative analysis of individual retinal layers. These images, based on optical inhomogeneities, reveal intricate cellular structures and are vital for tasks like retinal segmentation. The proposed study uses OCT images to identify significant differences in peripapillary retinal nerve fiber layer thickness. Incorporating spectral-domain analysis of OCT images significantly enhances the evaluation of Purtcher Retinopathy. To streamline this process, the study introduces a Context Encoding Deep Neural Network (CEDNN), which eliminates the time-consuming manual segmentation process while improving the… More >

  • Open Access

    ARTICLE

    An Adaptive Features Fusion Convolutional Neural Network for Multi-Class Agriculture Pest Detection

    Muhammad Qasim1,2, Syed M. Adnan Shah1, Qamas Gul Khan Safi1, Danish Mahmood2, Adeel Iqbal3,*, Ali Nauman3, Sung Won Kim3,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025

    Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… More >

  • Open Access

    ARTICLE

    Leveraging Safe and Secure AI for Predictive Maintenance of Mechanical Devices Using Incremental Learning and Drift Detection

    Prashanth B. S1,*, 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 >

  • Open Access

    ARTICLE

    MAD-ANET: Malware Detection Using Attention-Based Deep Neural Networks

    Waleed Khalid Al-Ghanem1, Emad Ul Haq Qazi2,*, Tanveer Zia2,3, Muhammad Hamza Faheem2, Muhammad Imran4, Iftikhar Ahmad5

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1009-1027, 2025, DOI:10.32604/cmes.2025.058352 - 11 April 2025

    Abstract In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In More >

  • Open Access

    ARTICLE

    XGBoost-Liver: An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

    Sumaiya Noor1, Salman A. AlQahtani2, Salman Khan3,*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1435-1450, 2025, DOI:10.32604/cmc.2025.061700 - 26 March 2025

    Abstract The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alcohol consumption, injuries, or genetic predispositions. Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates. Traditionally, diagnosing liver diseases relied heavily on clinical expertise, often leading to subjective, challenging, and time-intensive processes. However, early detection is essential for effective intervention, and advancements in machine learning (ML) have demonstrated remarkable success in predicting various conditions, including Chronic Obstructive… More >

  • Open Access

    ARTICLE

    SFPBL: Soft Filter Pruning Based on Logistic Growth Differential Equation for Neural Network

    Can Hu1, Shanqing Zhang2,*, Kewei Tao2, Gaoming Yang1, Li Li2

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4913-4930, 2025, DOI:10.32604/cmc.2025.059770 - 06 March 2025

    Abstract The surge of large-scale models in recent years has led to breakthroughs in numerous fields, but it has also introduced higher computational costs and more complex network architectures. These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization. To address this issue, various network compression techniques have been developed, such as network pruning. A typical pruning algorithm follows a three-step pipeline involving training, pruning, and retraining. Existing methods often directly set the pruned filters to zero during retraining, significantly reducing the parameter space. However, this… More >

  • Open Access

    ARTICLE

    Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

    Asma Aldrees1, Hong Min2,*, Ashit Kumar Dutta3, Yousef Ibrahim Daradkeh4, Mohd Anjum5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2487-2511, 2025, DOI:10.32604/cmes.2025.061103 - 03 March 2025

    Abstract Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus… More >

  • Open Access

    ARTICLE

    RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

    Weiwei Hu1, Kiran Sree Pokkuluri2, Rajesh Arunachalam3,*, Bander A. Jabr4, Yasser A. Ali4, Preethi Palanisamy5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4833-4851, 2024, DOI:10.32604/cmc.2024.055561 - 19 December 2024

    Abstract Wireless Sensor Network (WSNs) consists of a group of nodes that analyze the information from surrounding regions. The sensor nodes are responsible for accumulating and exchanging information. Generally, node localization is the process of identifying the target node’s location. In this research work, a Received Signal Strength Indicator (RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models. Initially, the RSSI value is identified using the Deep Neural Network (DNN). The RSSI is conceded as the range-based method and it does not require special hardware for the node… More >

  • Open Access

    ARTICLE

    Data-Driven Modeling for Wind Turbine Blade Loads Based on Deep Neural Network

    Jianyong Ao1, Yanping Li1, Shengqing Hu1, Songyu Gao2, Qi Yao2,*

    Energy Engineering, Vol.121, No.12, pp. 3825-3841, 2024, DOI:10.32604/ee.2024.055250 - 22 November 2024

    Abstract Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are… More >

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