Ching-Lung Fan^1,*, Yu-Jen Chung²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1925-1945, 2025, DOI:10.32604/cmc.2025.059245 - 17 February 2025

Abstract This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle (UAV) imagery by integrating the Visible Atmospherically Resistant Index (VARI) with deep learning models. The primary challenge addressed is the detection of bananas interplanted with betel nuts, a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap. The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector (SSD), You Only Look Once version 3 (YOLOv3), and Faster Region-Based Convolutional Neural Network (Faster RCNN)—using Red, Green, Blue (RGB) and VARI images for banana detection. Results More >

Yuanjing Hao, Xuemin Wang, Liang Chang^*, Long Li, Mingmeng Zhang

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3131-3159, 2025, DOI:10.32604/cmc.2024.059201 - 17 February 2025

Abstract Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific… More >

Yongjun Ren¹, Zhiming Wang¹, Pradip Kumar Sharma², Fayez Alqahtani³, Amr Tolba⁴, Jin Wang^5,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1593-1613, 2025, DOI:10.32604/cmc.2025.059170 - 17 February 2025

Abstract In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defensive techniques, Zero Trust Networks (ZTN) have emerged as a widely recognized technology. Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of “never trust, always verify.” Initially proposed by John Cortez in 2010 and subsequently promoted by Google, the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments. This paper systematically compares the current mainstream cybersecurity models, thoroughly explores More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

Ziqiang Tang¹, Chao Han¹, Hongwu Li¹, Zhou Fan¹, Ke Sun¹, Yuntian Huang¹, Yuhang Chen², Chenxing Wang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2813-2829, 2025, DOI:10.32604/cmc.2024.059094 - 17 February 2025

Abstract Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask… More >

Priyanka¹, Manjit Kaur¹, Deepak Prashar¹, Leo Mrsic², Arfat Ahmad Khan^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1615-1643, 2025, DOI:10.32604/cmc.2025.059043 - 17 February 2025

Abstract “Flying Ad Hoc Networks (FANETs)”, which use “Unmanned Aerial Vehicles (UAVs)”, are developing as a critical mechanism for numerous applications, such as military operations and civilian services. The dynamic nature of FANETs, with high mobility, quick node migration, and frequent topology changes, presents substantial hurdles for routing protocol development. Over the preceding few years, researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET, which is topology change and high mobility. This paper reviews current research on routing protocols and Machine Learning (ML) approaches applied to FANETs, emphasizing developments… More >

Xin Tong¹, Jingya Wang^1,*, Ying Yang², Tian Peng³, Hanming Zhai¹, Guangming Ling⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1901-1924, 2025, DOI:10.32604/cmc.2024.059018 - 17 February 2025

Abstract With the widespread use of SMS (Short Message Service), the proliferation of malicious SMS has emerged as a pressing societal issue. While deep learning-based text classifiers offer promise, they often exhibit suboptimal performance in fine-grained detection tasks, primarily due to imbalanced datasets and insufficient model representation capabilities. To address this challenge, this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS detection. During the data processing stage, Large Language Models (LLMs) are employed for data augmentation, mitigating dataset imbalance. In the data input stage, both word-level and character-level features are More >

Wenkai Zhang^1,2, Hao Zhang^1,2, Xianming Liu¹, Xiaoyu Guo^1,2, Xinzhe Wang¹, Shuiwang Li^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025

Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

Soyoung Joo^#, So-Hyun Park^#, Hye-Yeon Shim, Ye-Sol Oh, Il-Gu Lee^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2475-2494, 2025, DOI:10.32604/cmc.2025.058963 - 17 February 2025

Abstract As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by More >

Kexin Wang^1,#, Jiancheng Liu^1,#,*, Yuqing Lin^2,*, Tuo Wang¹, Zhipeng Zhang¹, Wanlong Qi¹, Xingye Han¹, Runyuan Wen³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1879-1899, 2025, DOI:10.32604/cmc.2024.058952 - 17 February 2025

Abstract Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection. Current frameworks for oriented detection modules are constrained by intrinsic limitations, including excessive computational and memory overheads, discrepancies between predefined anchors and ground truth bounding boxes, intricate training processes, and feature alignment inconsistencies. To overcome these challenges, we present ASL-OOD (Angle-based SIOU Loss for Oriented Object Detection), a novel, efficient, and robust one-stage framework tailored for oriented object detection. The ASL-OOD framework comprises three core components: the Transformer-based Backbone (TB), the Transformer-based Neck… More >