Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Lang Qin¹, Zhengrui Jiang¹, Xueshu Xing², Xiao Wang¹, Yaohua Yin², Yuhui Zhou², Zhiqin He^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5279-5298, 2025, DOI:10.32604/cmc.2025.063358 - 19 May 2025

Abstract In permanent magnet synchronous motor (PMSM) control, the jitter problem affects the system performance, so a novel reaching law is proposed to construct a non-singular fast terminal sliding mode controller (NFTSMC) to reduce the jitter. To enhance the immunity of the system, a disturbance observer is designed to observe and compensate for the disturbance to the sliding mode controller. In addition, considering that the controller parameters are difficult to adjust, and the traditional zebra optimization algorithm (ZOA) is prone to converge prematurely and fall into local optimum when solving the optimal solution, the improved zebra… More >

He Su, Jianwei Gao, Kang Kong^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025

Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

Qing Xie^*, Ruiyun Yu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4757-4774, 2025, DOI:10.32604/cmc.2025.063345 - 19 May 2025

Abstract This study presents a groundbreaking method named Expo-GAN (Exposition-Generative Adversarial Network) for style transfer in exhibition hall design, using a refined version of the Cycle Generative Adversarial Network (CycleGAN). The primary goal is to enhance the transformation of image styles while maintaining visual consistency, an area where current CycleGAN models often fall short. These traditional models typically face difficulties in accurately capturing expansive features as well as the intricate stylistic details necessary for high-quality image transformation. To address these limitations, the research introduces several key modifications to the CycleGAN architecture. Enhancements to the generator involve… More >

Hao Huang¹, Kongyu Yang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5373-5391, 2025, DOI:10.32604/cmc.2025.063311 - 19 May 2025

Abstract Many existing immune detection algorithms rely on a large volume of labeled self-training samples, which are often difficult to obtain in practical scenarios, thus limiting the training of detection models. Furthermore, noise inherent in the samples can substantially degrade the detection accuracy of these algorithms. To overcome these challenges, we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation (LP-CRI). The dataset is randomly partitioned into multiple subsets, each of which undergoes clustering followed by label propagation and evaluation. The rebound mechanism assesses the model’s performance after propagation and More >

Zhipeng Qin^1,2,*, Hanbing Yan³, Biyang Zhang², Peng Wang², Yitao Li³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.063308 - 19 May 2025

Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating More >

Changlong Wang¹, Jiawei Jiang¹, Chong Han^1,2,*, Hengyi Ren³, Lijuan Sun^1,2, Jian Guo^1,2

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4537-4550, 2025, DOI:10.32604/cmc.2025.063295 - 19 May 2025

Abstract Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body. However, static individuals, lacking prominent motion features, do not generate Doppler information. Moreover, radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls, limiting recognition performance. To address these challenges, this study proposes a novel through-wall human activity recognition method based on MIMO radar. Utilizing a MIMO radar operating at 1–2 GHz, we capture activity data of individuals through walls and process it into range-angle maps to represent activity features. To… More >

Hongliang Tian, Zhong Fan^*, Zhiyang Ruan, Aomen Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5199-5217, 2025, DOI:10.32604/cmc.2025.063274 - 19 May 2025

Abstract With the continuous growth of exponential data in IoT, it is usually chosen to outsource data to the cloud server. However, cloud servers are usually provided by third parties, and there is a risk of privacy leakage. Encrypting data can ensure its security, but at the same time, it loses the retrieval function of IoT data. Searchable Encryption (SE) can achieve direct retrieval based on ciphertext data. The traditional searchable encryption scheme has the problems of imperfect function, low retrieval efficiency, inaccurate retrieval results, and centralized cloud servers being vulnerable and untrustworthy. This paper proposes… More >

Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

Peicheng Shi^1,*, Li Yang¹, Xinlong Dong¹, Heng Qi², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3877-3917, 2025, DOI:10.32604/cmc.2025.063205 - 19 May 2025

Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >