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  • Open Access

    PROCEEDINGS

    Hybrid Inverse Modeling Technique to Determine the Fracture Properties of Intermetallic Layer Formed at Al/Steel Dissimilar Weld Interface

    Kiyoaki T. Suzuki1,*, Sylvain Dancette2, Shun Tokita3, Yutaka S. Sato4

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012072

    Abstract Dissimilar welding of aluminum (Al) alloy to steel has been a long-running scientific and technological problem mainly for the automotive industry. It would allow to achieve new designs of optimized vehicle structures combining strength, lightweight and energy absorption ability. However, the weld strength is limited because of a brittle intermetallic layer (IML) formed at the weld interface. In our previous study, we demonstrated a significant improvement in weld strength by the addition of Ni to aluminum alloy. However, the effect of Ni addition on the fracture properties of IML remains unexplored. Moreover, additional Ni should… More >

  • Open Access

    PROCEEDINGS

    In-Silico Automated 3D Reconstruction of the Biomechanical Trapeziometacarpal Joint from 4D Imaging

    Yen-Jen Lai1, I-Ling Chang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012918

    Abstract Biomechanical research reveals that the geometric shapes and dynamic behaviors of organ tissues play a pivotal role in determining their mechanical properties. Recent advancements in time-correlated imaging technologies, such as Computed Tomography (4D-CT) and Magnetic Resonance Imaging (4D-MRI), have enabled the non-invasive capture of both geometric data and dynamic information over time. However, the manual segmentation of these extensive datasets proves to be laborious and expensive. This study introduces an automated workflow designed for image segmentation and classification within 4D-CT scans, with a specific focus on the bone structures surrounding the Trapeziometacarpal (TMC) joint in More >

  • Open Access

    ARTICLE

    SAR-LtYOLOv8: A Lightweight YOLOv8 Model for Small Object Detection in SAR Ship Images

    Conghao Niu1,*, Dezhi Han1, Bing Han2, Zhongdai Wu2

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

    Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

  • Open Access

    REVIEW

    A Survey of Lung Nodules Detection and Classification from CT Scan Images

    Salman Ahmed1, Fazli Subhan2,3, Mazliham Mohd Su’ud3,*, Muhammad Mansoor Alam3,4, Adil Waheed5

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1483-1511, 2024, DOI:10.32604/csse.2024.053997 - 22 November 2024

    Abstract In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL… More >

  • Open Access

    REVIEW

    AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

    Mohd Asif Hajam1, Tasleem Arif1, Akib Mohi Ud Din Khanday2, Mudasir Ahmad Wani3,*, Muhammad Asim3,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

    Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

  • Open Access

    ARTICLE

    Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net

    Haemin Jung1, Heesung Park2, Hae Sun Jung3, Kwangyon Lee4,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2261-2279, 2024, DOI:10.32604/cmc.2024.057118 - 18 November 2024

    Abstract The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to… More >

  • Open Access

    ARTICLE

    Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

    Jeng-Shyang Pan1,2, Wenda Li1, Shu-Chuan Chu1,*, Xiao Sui1, Junzo Watada3

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024

    Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

  • Open Access

    ARTICLE

    PCB CT Image Element Segmentation Model Optimizing the Semantic Perception of Connectivity Relationship

    Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2629-2642, 2024, DOI:10.32604/cmc.2024.056038 - 18 November 2024

    Abstract Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception… More >

  • Open Access

    ARTICLE

    Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

    Mohana Priya Govindarajan*, Sangeetha Subramaniam Karuppaiya Bharathi

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2967-2986, 2024, DOI:10.32604/cmc.2024.055536 - 18 November 2024

    Abstract In the present research, we describe a computer-aided detection (CAD) method aimed at automatic fetal head circumference (HC) measurement in 2D ultrasonography pictures during all trimesters of pregnancy. The HC might be utilized toward determining gestational age and tracking fetal development. This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited. The CAD system is divided into two steps: to begin, Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull. We identified the HC using dynamic programming,… More >

  • Open Access

    ARTICLE

    A Novel Filtering-Based Detection Method for Small Targets in Infrared Images

    Sanxia Shi, Yinglei Song*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2911-2934, 2024, DOI:10.32604/cmc.2024.055363 - 18 November 2024

    Abstract Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.… More >

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