Reen-Cheng Wang¹, Hong-Sheng Wang², Kuo-Chun Tseng^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083124 - 23 July 2026

Abstract Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments, yet its effectiveness remains closely tied to initialization quality. This study formulates network microsegmentation as a state-dependent combinatorial optimization problem in which optimization behavior depends on the availability of structural guidance. A comparative analysis is conducted across four representative optimization paradigms, including genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), and amplitude-ensemble quantum-inspired tabu search (AE-QTS), under both structured and unstructured conditions. Experiments are conducted on a representative brownfield enterprise network using 30 independent runs per configuration. In addition… More >

Olzhas Olzhayev¹, Bakhytzhan Kulambayev^2,*, Azizah Suliman³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082618 - 23 July 2026

Abstract Automated road damage detection is a critical component of intelligent transportation systems, enabling efficient infrastructure maintenance and improved traffic safety. However, existing approaches often suffer from limited contextual understanding, insufficient segmentation accuracy, and suboptimal real-time performance. This study presents TCR-RoadNet, a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments. The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement (TCR) module to capture both fine-grained structural details and long-range spatial dependencies across feature scales. To further enhance performance, a Decoupled Detection Head (DDH)… More >

Kexin Zhang¹, Lihua Liu^1,*, Yuting Xue¹, Tao Zhou², Fengshuai Yue¹, Ruifeng Du¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082353 - 23 July 2026

Abstract Traditional Mamba-UNet integrations employ four-stage architectures, replacing conventional five-stage UNets with VMamba blocks for global dependency modeling. Unlike Transformers, which suffer from quadratic complexity and high memory consumption in self-attention, Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling. This paper proposes TriLVM-UNet, a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge (SAB) module inspired by UltraLight VM-UNet. The model incorporates a Lightweight Vision Mamba (LVM) layer for high-resolution feature extraction, alongside multi-scale dilated convolution (MSDC) and convolutional block attention module (CBAM) for enhanced More >

Laura Baitenova¹, Gulnar Mukhamejanova², Gauhar Munaitbas^3,*, Saken Mambetov¹, Zhanna Mukanova¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081004 - 23 July 2026

Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >

Ping Zhang¹, Chungang Li², Jian Song¹, Jianjun Tan¹, Han Tian¹, Le Chen³, Yongkun Song^3,*

Journal on Artificial Intelligence, Vol.8, pp. 323-334, 2026, DOI:10.32604/jai.2026.080463 - 02 July 2026

Abstract Accurate segmentation of coal pile point clouds is essential for automated inventory management and safety supervision in industrial coal sheds. With the widespread adoption of LiDAR technology, massive point cloud data are available, yet traditional clustering algorithms often fail to distinguish physically adhering coal piles in high-density storage scenarios, resulting in severe under-segmentation. To address this challenge, this paper proposes an automated point cloud segmentation framework tailored for complex industrial environments. The proposed framework consists of three core stages: data preprocessing, ground segmentation, and instance segmentation. First, relative elevation mapping and selective statistical filtering are… More >

Anita Desiani^1,*, Indri Ramayanti², Sigit Priyanta³, Bambang Suprihatin¹, Muhammad Arhami⁴, Deshinta Arrova Dewi⁵, Puspa Sari¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083951 - 30 June 2026

Abstract Image segmentation is essential in medical image analysis for glaucoma screening. Accurate delineation of the optic disc (OD) and optic cup (OC) in retinal fundus images is required for reliable clinical assessment. Manual segmentation is time-consuming and suffers from interobserver variability, which leads to inconsistent results. To address these limitations, this study proposes ASPP Inception Attention U-Net (ASPPIAU-Ne), an enhanced encoder-decoder architecture that integrates Atrous Spatial Pyramid Pooling (ASPP), Inception modules, and attention gates for feature selection in skip connections. The ASPP module is applied after the encoder to capture multi-scale contextual information and improve… More >

Yuyan Ning^1,2, Haiyun Huang¹, Legend Zhang³, Wei Wei⁴, Hao Quan⁵, Bo Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083345 - 30 June 2026

Abstract Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron More >

Karim Gasmi^1,*, Afrah Alanazi², Inam Alanazi², Sahar Almenwer¹, Norah Alanazi¹, Sarah Almaghrabi³, Samia Yahyaoui⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026

Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >

Anh Thi Diem Nguyen^1,2,#, Tham Vo¹, Vinh Truong Hoang^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082845 - 15 June 2026

Abstract Keeping customers engaged remains a major challenge in appointment-based services, where user behavior continuously shifts due to seasonal, market, and social factors. These dynamic changes often cause concept drift, rendering traditional deep clustering models unreliable because they assume stable data distributions. Most existing approaches handle representation learning, parameter optimization, and model updating as separate components, limiting their adaptability in real-world streaming environments. This study proposes Hybrid-RL, a novel adaptive clustering framework that unifies incremental deep representation learning, multi-head reinforcement learning for joint hyperparameter optimization (number of clusters, latent dimension, and clustering method), incremental model updating,… More >

Rifat Sarker Aoyon¹, Fahmid Al Farid^2,3, Ismail Hossain⁴, Mahe Zabin⁵, Sarina Mansor^2,*, Jia Uddin^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079831 - 15 June 2026

Abstract This research introduces an innovative lightweight image segmentation framework where models of hybrid architectures work together to predict the output and also have self-adapting ability, along with maintaining data privacy. In this framework, data is distributed and trained in a decentralized way using different deep learning architectures. That is how the advantages of all these models will be integrated into the system. Each trained model makes its own prediction, and the final output is determined through cooperation among these models. Here, the confidence-level and pixel-wise voting majority algorithms will be utilized for the co-operation-based output… More >