Conghao Niu^1,*, 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 >

Salman Ahmed¹, Fazli Subhan^2,3, Mazliham Mohd Su’ud^3,*, Muhammad Mansoor Alam^3,4, 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 >

Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*

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 >

Haemin Jung¹, Heesung Park², Hae Sun Jung³, Kwangyon Lee^4,*

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 >

Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, 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 >

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 >

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 >

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 >

Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui¹, Amr Abdussalam², Muhammad Asim³, Naveed Ahmed⁴, Mohammed Ali Alshara^4,5

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2873-2894, 2024, DOI:10.32604/cmc.2024.054841 - 18 November 2024

Abstract Image captioning has gained increasing attention in recent years. Visual characteristics found in input images play a crucial role in generating high-quality captions. Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image, improving the effectiveness of identifying relevant image regions at each step of caption generation. However, providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features. Consequently, this leads to enhanced captioning network performance. In light… More >

Jiajun Liu¹, Lina Tan^1,*, Zhili Zhou², Weijin Jiang¹, Yi Li¹, Peng Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3221-3240, 2024, DOI:10.32604/cmc.2024.054542 - 18 November 2024

Abstract Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data. One issue with these methods is that as the steganographic capacity increases, the number of images stored in the database grows exponentially. This makes it challenging to build and manage a large image database. To improve the image library utilization and anti-attack capability of the steganography system, we propose an efficient coverless scheme based on dynamically matched substrings. We utilize You Only Look Once (YOLO) for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors.… More >