Jiangyong Yu, Chuanping Hu^*, Runnan Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082810 - 15 June 2026

Abstract With the proliferation of Internet of Things (IoT) devices, accurate device fingerprinting of highly encrypted traffic has emerged as a critical challenge for ensuring network security. Existing deep learning models are either difficult to deploy in real-time due to excessive computational complexity (e.g., Transformers) or are limited in performance because their structure does not match the inherent hierarchy of traffic data (e.g., flattened state space models). Furthermore, a general lack of transparency in their decision-making processes restricts their trustworthiness in security-critical scenarios. To address these challenges, this paper proposes a Hierarchical Mamba with Gated Attribution More >

Haotian Cao^1,2, Qingsheng Zhu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081695 - 15 June 2026

Abstract Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity $𝒪(n^{2}d)$, leading to rapid increases in computational cost with large-scale point cloud data. To address these problems, an iterative PCR method named Attention and Mamba Based Iterative Registration Network (AMBIR) is proposed, overcoming the shortcomings of the current PCR method on low-overlap and large-scale scenarios. Specifically, an… More >

Ronak Patel¹, Miral Patel², Deep Kothadiya³, Noor A. Khan⁴, Shaha Al-Otaibi^5,*, Roaa Khalil Mohamed Ali Abed⁶, Tanzila Saba⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080819 - 27 May 2026

Abstract Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module… More >

Manh-Tuan Ha¹, Nhu-Nghia Bui², Dinh-Quy Vu^1,*, Thai-Viet Dang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078280 - 08 May 2026

Abstract The development of Unmanned Surface Vehicles (USVs) has become a key focus in marine robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, and generalization, and varying environmental conditions. To achieve sample-efficient and robust USV navigation in dynamic maritime environments, the paper proposes a novel hierarchical multi-view latent imitation learning (IL) architecture. By formulating a latent IL objective, the framework disentangles diverse navigation modalities through continuous variables, preventing mode collapse and enhancing behavioral adaptability… More >

Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >

Qinglei Zhang, Zhenzhen Wang^*, Jianguo Duan, Jiyun Qin, Ying Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076562 - 09 April 2026

Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >

Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075756 - 12 March 2026

Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >

Chao Li^1,3,#, Chen Wang^1,3,#, Caichang Ding^2,*, Yonghao Liao^1,3, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3129-3149, 2025, DOI:10.32604/cmc.2025.067440 - 23 September 2025

Abstract Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the… More >

Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Honglin Wang¹, Yaohua Xu^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4999-5018, 2025, DOI:10.32604/cmc.2025.061848 - 19 May 2025

Abstract Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image More >