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REVIEW

A-Seong Moon, Haesung Kim, Ye-Chan Park, Jaesung Lee^*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076411 - 12 March 2026
Abstract Multimodal emotion recognition has emerged as a key research area for enabling human-centered artificial intelligence, supported by the rapid progress in vision, audio, language, and physiological modeling. Existing approaches integrate heterogeneous affective cues through diverse embedding strategies and fusion mechanisms, yet the field remains fragmented due to differences in feature alignment, temporal synchronization, modality reliability, and robustness to noise or missing inputs. This survey provides a comprehensive analysis of MER research from 2021 to 2025, consolidating advances in modality-specific representation learning, cross-modal feature construction, and early, late, and hybrid fusion paradigms. We systematically review visual,… More >

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Salma Akther¹, Wencheng Yang^1,*, Song Wang², Shicheng Wei¹, Ji Zhang¹, Xu Yang³, Yanrong Lu⁴, Yan Li¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076358 - 12 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are effective for traditional networks and applications but lack the capabilities to address DL-specific threats, such as model inversion, membership inference, and adversarial attacks. This review provides a comprehensive analysis of penetration testing for the privacy of DL models, examining the shortfalls of existing frameworks, tools, and testing methodologies. Through systematic evaluation of existing literature and empirical analysis, we identify three major contributions: (i) a critical… More >

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REVIEW

Xuehan Li¹, Tao Jing², Yang Wang², Bo Gao³, Jing Ai⁴, Minghao Zhu^5,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075426 - 12 March 2026
（This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)
Abstract With the deep integration of cloud computing, edge computing and the Internet of Things (IoT) technologies, smart manufacturing systems are undergoing profound changes. Over the past ten years, an extensive body of research on cloud-edge-end systems has been generated. However, challenges such as heterogeneous data fusion, real-time processing and system optimization still exist, and there is a lack of systematic review studies. In this paper, we review a cloud-edge-end collaborative sensing-communication-computing-control (SC3) system. This system integrates four layers of sensing, communication, computing and control to address the complex challenges of real-time decision making, resource… More >

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298

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REVIEW

Hafiz Burhan Ul Haq¹, Waseem Akram², Haroon ur Rashid Kayani³, Khalid Mahmood^4,*, Chihhsiong Shih⁵, Rupak Kharel^6,7, Amina Salhi⁸
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073188 - 12 March 2026
Abstract The Internet of Things (IoT) is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications. Anomaly detection has widely attracted researchers’ attention in the last few years, and its effects on diverse applications. This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city. In this work, we present a comprehensive literature review (2011 onwards) of three major types of anomalies: network anomalies, sensor anomalies, and video-based anomalies, along with their methods and software… More >

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168

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REVIEW

Jingjing Liu¹, Chuanyang Liu^1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075348 - 12 March 2026
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence, robotics, and other related technologies. It plays a key role in ensuring power grid safety, reducing operation and maintenance costs, driving the digital transformation of the power industry, and facilitating the achievement of the dual-carbon goals. This review focuses on vision-based power line inspection, with deep learning as the core perspective to systematically analyze the latest research advancements in this field. Firstly, at the technical foundation level, it elaborates on deep learning algorithms for intelligent transmission line inspection based on image… More >

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85

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REVIEW

Changxu Zhao¹, Jianping Liu^1,*, Xiaofeng Wang¹, Wei Sun², Libo Liu³, Haiyu Ren¹, Pan Liu¹, Qiantong Wang¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074409 - 12 March 2026
Abstract Foundation models are reshaping artificial intelligence, yet their deployment in specialised domains such as agricultural question answering (AQA) still faces challenges including data scarcity and barriers to domain-specific knowledge. To systematically review recent progress in this area, this paper adopts a task–paradigm perspective and examines applications across three major AQA task families. For text-based QA, we analyse the strengths and limitations of retrieval-based, generative, and hybrid approaches built on large language models, revealing a clear trend toward hybrid paradigms that balance precision and flexibility. For visual diagnosis, we discuss techniques such as cross-modal alignment and More >

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286

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ARTICLE

Saeid Sheikhi^*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077788 - 12 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications, 2nd Edition)
Abstract Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas… More >

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78

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ARTICLE

Yuzhou Han^1,*, Zhuoran Li¹, Ahmad Gendia^2,3, Teruji Ide⁴, Osamu Muta^2,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076555 - 12 March 2026
（This article belongs to the Special Issue: Advancements in Mobile Computing for the Internet of Things: Architectures, Applications, and Challenges)
Abstract Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access (DSA) and a key approach to alleviating spectrum scarcity in the Internet of Things (IoT). The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios (SNRs) and non-Gaussian impulsive noise, where observations may be distorted differently across feature modalities, making conventional fusion unstable and degrading detection reliability. To address this challenge, the generalized Gaussian distribution (GGD) is adopted as the noise model, and a multimodal fusion framework termed BCAM-Net (bidirectional cross-attention multimodal network) is proposed.… More >

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101

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ARTICLE

Guotai Huang, Xiyu Gao, Peng Liu, Liming Zhou^*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074941 - 12 March 2026
Abstract To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions, this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory (LSTM) encoder, a Gated Recurrent Unit (GRU) decoder, and a multi-head attention mechanism. This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions, thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics. Experiments using long-term data from real test benches demonstrate that the model achieves… More >

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40

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ARTICLE

Yuchuan Gu^1,2, Lusheng Wang^1,*, Jun Ding¹, Yanhong Peng¹, Changgeng Li^3,*, Shaojie Gu^4,5
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075939 - 12 March 2026
（This article belongs to the Special Issue: Computational Materials Design and Intelligent Processing for Advanced Alloys and Manufacturing Systems)
Abstract In materials science and engineering design, high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation. To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large language models. A structured knowledge base integrating constitutive theory, numerical algorithms, and UMAT (User Material) interface specifications is constructed, and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing, constitutive model formulation, and automatic UMAT… More >

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40

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ARTICLE

Rama Gautam^1,*, Nikhil Marriwala¹, Reeta Devi¹, Dhariya Singh Arya²
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076136 - 12 March 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)
Abstract In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through I–V characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated More >

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38

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ARTICLE

Hongyu Xu^1,*, Xiaofeng Liu¹, Zhao Li¹, Shuai Zhang², Jintao Cui¹, Zongshuai Zhou¹, Longlong Chen¹, Mengen Zhang¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075413 - 12 March 2026
Abstract In two-scale topology optimization, enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability. This paper proposes a two-scale concurrent topology optimization strategy aimed at improving the interface connection strength. This method employs a parametric approach to explicitly divide the micro-design domain into a “boundary connection region” and a “free design domain” at the initial stage of optimization. The boundary connection region is used to generate a connection layer that enhances the interface strength, while the free design domain is not constrained by this layer, thus… More >

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ARTICLE

Wenchao Shi¹, Tao Wei², Chuan Yang³, Qichao Fan³, Hongxi Liu⁴, Bin Shao^5,*, Peng Jing^4,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075293 - 12 March 2026
Abstract Enhancing the strength of nanotwinned aluminum (Al) is essential for the development of next-generation high-end chip technology. To better understand the detwinning behavior of nanotwinned Al under conditions with no resolved shear stress acting on the twin boundaries, we conducted molecular dynamics simulations of uniaxial tensile deformation in nanotwinned single-crystal Al at room temperature. Detwinning is observed only when the twin boundary spacing is 7.01 Å. At larger spacings, twin boundaries remain parallel to the loading direction, with no rotation or bending, indicating negligible migration. Detwinning is triggered by localized stress from dislocation interactions, with More >

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100

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Shuai Li¹, Dongrong Liu¹, Shu Li^2,*, Minghua Chen²
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075830 - 12 March 2026
（This article belongs to the Special Issue: Machine Learning Methods in Materials Science)
Abstract The exploration of high-performance materials presents a fundamental challenge in materials science, particularly in predicting properties for materials beyond the known range of target property values (extrapolation). This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning (ML) models. A new ML scheme was proposed, featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization, which demonstrated superior extrapolation prediction across multiple materials datasets. Based on this ML scheme, multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature. Subsequently, More >

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134

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ARTICLE

Azhar G. Hamad¹, Nasser Firouzi^2,*, Yousef S. Al Rjoub³
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075840 - 12 March 2026
Abstract The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials, such as hyperelastic and functionally graded materials (FGMs), is critical for ensuring their safety and optimizing their design. However, conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs. This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks (PINNs) with Finite Element Method (FEM) derived data for the robust analysis of thick-walled, axisymmetric, heterogeneous, hyperelastic pressure vessels with elliptical geometries. A PINN framework incorporating neo-Hookean constitutive relations is developed in… More >

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33

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ARTICLE

Qian Zhang¹, Lei Fu^1,2, Renzhou Chen³, Xu Zhan^4,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074939 - 12 March 2026
（This article belongs to the Special Issue: Additive Manufacturing: Advances in Computational Modeling and Simulation)
Abstract Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated,… More >

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ARTICLE

Oksana Ivanova^*, Roman Cherepanov, Sergey Zelepugin
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075451 - 12 March 2026
（This article belongs to the Special Issue: Perspective Materials for Science and Industrial: Modeling and Simulation)
Abstract This study presents an investigation into shock-induced exothermic reactions within three distinct aluminum-based energetic mixtures: aluminum/sulfur (Al/S), aluminum/copper oxide (Al/CuO), and aluminum/polytetrafluoroethylene (Al/PTFE). A challenge in current modeling efforts is accurately capturing the complex physical and chemical coupling under extreme loading, especially the influence of rapidly forming gaseous products in Al/PTFE mixtures on material integrity. To address this, a wide-range numerical model based on the Smoothed Particle Hydrodynamics (SPH) method was developed. This mesh-free approach manages large deformations and incorporates elastic-plastic flow, heat transfer, component diffusion, and chemical kinetics simulated using both zero- and first-order… More >

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29

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ARTICLE

Moises Jimenez-Martinez^1,*, Gael Ramirez², Giancarlo Marchetta-Cruz³, Manuel Coca-Gonzalez¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074260 - 12 March 2026
Abstract Most failures in component operation occur due to cyclic loads. Validation has been performed under quasistatic loads, but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life. Fatigue is a damage accumulation process where loads degrade the material, depending on the characteristics and number of repetitions of the load. Studies on the mechanical fatigue of 3D-printed Onyx are limited. In this paper, the strength of 3D-printed Onyx components under dynamic conditions (repetitive loads) is estimated. Fatigue life prediction is influenced by manufacturing processes, material properties, and… More >

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115

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Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026
Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… More >

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ARTICLE

Oumaima Saidani¹, Nazia Azim², Ateeq Ur Rehman^3,*, Akbayan Bekarystankyzy⁴, Hala AbdelHameed Mostafa⁵, Mohamed R. Abonazel⁶, Ehab Ebrahim Mohamed Ebrahim⁷, Sarah Abu Ghazalah⁸
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.072772 - 12 March 2026
Abstract The convergence of Software Defined Networking (SDN) in Internet of Vehicles (IoV) enables a flexible, programmable, and globally visible network control architecture across Road Side Units (RSUs), cloud servers, and automobiles. While this integration enhances scalability and safety, it also raises sophisticated cyberthreats, particularly Distributed Denial of Service (DDoS) attacks. Traditional rule-based anomaly detection methods often struggle to detect modern low-and-slow DDoS patterns, thereby leading to higher false positives. To this end, this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV (SDN-IoV). The hybrid framework utilizes a Residual Network (ResNet)… More >

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237

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Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073832 - 12 March 2026
（This article belongs to the Special Issue: Recent Advances in Blockchain Technology and Applications)
Abstract The Internet of Things (IoT) and cloud computing have significantly contributed to the development of smart cities, enabling real-time monitoring, intelligent decision-making, and efficient resource management. These systems, particularly in IoT networks, rely on numerous interconnected devices that handle time-sensitive data for critical applications. In related approaches, trusted communication and reliable device interaction have been overlooked, thereby lowering security when sharing sensitive IoT data. Moreover, it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment. In this research, an Artificial Intelligence (AI) recommended fault-tolerant framework is proposed that leverages blockchain More >

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ARTICLE

Zhongbin Luo^1,2, Zhaoyang Guan³, Wenxing You², Yunteng Wang², Yanqiu Bi^4,5,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075704 - 12 March 2026
Abstract Gait recognition is a key biometric for long-distance identification, yet its performance is severely degraded by real-world challenges such as varying clothing, carrying conditions, and changing viewpoints. While combining silhouette and skeleton data is a promising direction, effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem. This paper introduces GaitMAFF, a novel Multi-modal Adaptive Feature Fusion Network, to address this challenge. Our approach first transforms discrete skeleton joints into a dense Skeleton Map representation to align with silhouettes, then employs an attention-based module to dynamically More >

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153

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Faisal Albalwy^1,*, Muhannad Almohaimeed²
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075098 - 12 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract Intrusion detection in Internet of Things (IoT) environments presents challenges due to heterogeneous devices, diverse attack vectors, and highly imbalanced datasets. Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines, which often show strong performance but limited generalizability, probabilistic reliability, and operational interpretability. This study proposes a stacked ensemble deep learning framework that integrates random forest, extreme gradient boosting, and a deep neural network as base learners, with CatBoost as the meta-learner. On the ToN-IoT Linux process dataset, the model achieved near-perfect discrimination (macro area under the curve = 0.998),… More >

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551

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106

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Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026
（This article belongs to the Special Issue: Recent Fuzzy Techniques in Image Processing and its Applications)
Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >

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195

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Saifullah Tumrani^1,2,*, Abdul Jabbar Siddiqui^2,3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075152 - 12 March 2026
Abstract Vehicle re-identification (ReID) is a challenging task in intelligent transportation, and urban surveillance systems due to its complications in camera viewpoints, vehicle scales, and environmental conditions. Recent transformer-based approaches have shown impressive performance by utilizing global dependencies, these models struggle with aspect ratio distortions and may overlook fine-grained local attributes crucial for distinguishing visually similar vehicles. We introduce a framework based on Swin Transformers that addresses these challenges by implementing three components. First, to improve feature robustness and maintain vehicle proportions, our Aspect Ratio-Aware Swin Transformer (AR-Swin) preserve the native ratio via letterbox, uses a… More >

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Zhichao Yu¹, Jiahui Yu¹, Simon James Fong^1,*, Yaoyang Wu^1,2
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074899 - 12 March 2026
Abstract Accurately recognizing driver distraction is critical for preventing traffic accidents, yet current detection models face two persistent challenges. First, distractions are often fine-grained, involving subtle cues such as brief eye closures or partial yawns, which are easily missed by conventional detectors. Second, in real-world scenarios, drivers frequently exhibit overlapping behaviors, such as simultaneously holding a cup, closing their eyes, and yawning, leading to multiple detection boxes and degraded model performance. Existing approaches fail to robustly address these complexities, resulting in limited reliability in safety critical applications. To overcome these pain points, we propose YOLO-Drive, a… More >

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Yuji Sang¹, Lijun Liu^1,*, Long Lv^1,*, Husheng Wu², Hemin Yin¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073837 - 12 March 2026
Abstract Aiming at the challenges of low throughput, excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance (PBFT) algorithm in blockchain networks, its application in identity verification for distributed networking of a drone cluster is limited. Therefore, a lightweight blockchain-based identity authentication model for UAV swarms is designed, and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance (CG-PBFT) algorithm is proposed. CG-PBFT introduces a reputation score evaluation mechanism, classifies the reputation levels of nodes in the network, and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology. Experimental More >

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Yiming Yu¹, Yunfei Guo², Junchen Liu³, Yiping Sun⁴, Junliang Du^5,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074959 - 12 March 2026
Abstract Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their effectiveness, most GNN-based vulnerability detectors operate as black boxes, making their decisions difficult to interpret and thus less suitable for critical security auditing. The information bottleneck (IB) principle provides a theoretical framework for isolating task-relevant graph components. However, existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics. To address these issues, we introduce ContractGIB, an interpretable graph information bottleneck framework for function-level vulnerability More >

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Talha Farooq Khan¹, Fahad Ali², Majid Hussain¹, Lal Khan^3,*, Hsien-Tsung Chang^4,5,6,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074557 - 12 March 2026
Abstract The outstanding growth in the applications of large language models (LLMs) demonstrates the significance of adaptive and efficient prompt engineering tactics. The existing methods may not be variable, vigorous and streamlined in different domains. The offered study introduces an immediate optimization outline, named PROMPTx-PE, that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM. The proposed system features a timely selection scheme which is informed by reinforcement learning, a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability More >

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Qingwen Yang¹, Zhi Li², Jiawei Tang¹, Yanyi Liu¹, Tiezheng Guo¹, Yingyou Wen^1,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074384 - 12 March 2026
（This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)
Abstract Deploying Large Language Model (LLM)-based agents in the Industrial Internet of Things (IIoT) presents significant challenges, including high latency from cloud-based APIs, data privacy concerns, and the infeasibility of deploying monolithic models on resource-constrained edge devices. While smaller models (SLMs) are suitable for edge deployment, they often lack the reasoning power for complex, multi-step tasks. To address these issues, this paper introduces LEAF, a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge. LEAF employs a novel architecture where multiple expert SLMs—specialized for planning, execution, and interaction—work in concert, decomposing complex… More >

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Chunqian Guo^*, Gang Chen
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.072184 - 12 March 2026
Abstract Despite the ability of the anonymous communication system The Onion Router (Tor) to obscure the content of communications, prior studies have shown that passive adversaries can still infer the websites visited by users through website fingerprinting (WF) attacks. Conventional WF methodologies demonstrate optimal performance in scenarios involving single-tab browsing. Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing. However, in real-world network environments, users often engage in multi-tab browsing, which generates overlapping traffic patterns from different websites. This overlap has been shown to significantly degrade the performance of classifiers that rely on… More >

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Zheng Zhao¹, Doo Heon Song², Kwang Baek Kim^1,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073969 - 12 March 2026
（This article belongs to the Special Issue: Recent Fuzzy Techniques in Image Processing and its Applications)
Abstract Deep learning attention mechanisms have achieved remarkable progress in computer vision, but still face limitations when handling images with ambiguous boundaries and uncertain feature representations. Conventional attention modules such as SE-Net, CBAM, ECA-Net, and CA adopt a deterministic paradigm, assigning fixed scalar weights to features without modeling ambiguity or confidence. To overcome these limitations, this paper proposes the Fuzzy Attention Network Layer (FANL), which integrates intuitionistic fuzzy set theory with convolutional neural networks to explicitly represent feature uncertainty through membership (μ), non-membership (ν), and hesitation (π) degrees. FANL consists of four core modules: (1)… More >

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Omar Almomani^1,*, Mahran Al-Zyoud¹, Ahmad Adel Abu-Shareha², Ammar Almomani^3,4,*, Said A. Salloum⁵, Khaled Mohammad Alomari⁶
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075465 - 12 March 2026
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract In Wireless Sensor Networks (WSNs), survivability is a crucial issue that is greatly impacted by energy efficiency. Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints in WSNs. This paper presents an Adaptive Enhanced Grey Wolf Optimizer (AEGWO) for energy-efficient cluster head (CH) selection that mitigates the exploration–exploitation imbalance, preserves population diversity, and avoids premature convergence inherent in baseline GWO. The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation, a hybrid velocity-momentum update based on the dynamics of PSO, and… More >

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Jeng-Shyang Pan^1,2, Yan-Na Wei³, Ling-Da Chi⁴, Shu-Chuan Chu^1,*, Ru-Yu Wang⁵, Junzo Watada⁶
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074447 - 12 March 2026
（This article belongs to the Special Issue: Advances in Nature-Inspired and Metaheuristic Optimization Algorithms: Theory, Applications, and Emerging Trends)
Abstract Multilevel image segmentation is a critical task in image analysis, which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms. In this paper, an improved Artificial Protozoa Optimization algorithm, termed the two-stage Taguchi-assisted Gaussian–Lévy Artificial Protozoa Optimization (TGAPO) algorithm, is proposed and applied to multilevel image segmentation. The proposed algorithm adopts a two-stage evolutionary mechanism. In the first stage, Gaussian perturbation is introduced to enhance local search capability; in the second stage, Lévy flight is incorporated to expand the global search range; and finally, the Taguchi strategy is employed to… More >

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Tran Minh Bao¹, Kumar Shashvat², Nguyen Gia Nhu^3,*, Dac-Nhuong Le⁴
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075161 - 12 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the… More >

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Heesoo Kim¹, Minwook Kim¹, Hyorim Han², Soongbong Lee², Tai-jin Song^1,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074802 - 12 March 2026
（This article belongs to the Special Issue: AI-Driven Big Data Analytics for Sustainable Mixed Traffic and Mobility Systems)
Abstract Autonomous vehicles operate without direct human intervention, which introduces safety risks that differ from those of conventional vehicles. Although many studies have examined safety issues related to autonomous driving, high-risk situations have often been defined using single indicators, making it difficult to capture the complex and evolving nature of accident risk. To address this limitation, this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process. High-risk situations are described using three complementary indicators: accident likelihood, accident severity, and accident duration. These indicators explain how risk emerges, increases, and… More >

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87

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Shih-Ming Cho¹, Sung-Wen Wang¹, Min-Chie Chiu^2,*, Shao-Chun Chen¹
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074911 - 12 March 2026
（This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)
Abstract To address crop depredation by intelligent species (e.t, macaques) and the habituation from traditional methods, this study proposes an intelligent, closed-loop, adaptive laser deterrence system. A core contribution is an efficient multi-stage Semi-Supervised Learning (SSL) and incremental fine-tuning (IFT) framework, which reduced manual annotation by ~60% and training time by ~68%. This framework was benchmarked against YOLOv8n, v10n, and v11n. Our analysis revealed that YOLOv12n’s high Signal-to-Noise Ratio (SNR) (47.1% retention) pseudo-labels made it the only model to gain performance (+0.010 mAP) from SSL, allowing it to overtake competitors. Subsequently, in the IFT stress test,… More >

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Wang Cheng¹, Zhuodong Liu², Xiangyu Li^3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074250 - 12 March 2026
Abstract Smart pest control is crucial for building farm resilience and ensuring sustainable agriculture in the face of climate change and environmental challenges. To achieve effective intelligent monitoring systems, agricultural pest and disease detection must overcome three fundamental challenges: feature degradation in dense vegetation environments, limited detection capability for sub-32×32 pixel targets, and inadequate bounding box regression for irregular pest morphologies. This study proposes YOLOv12-KMA, a novel detection framework that addresses these limitations through four synergistic architectural innovations, specifically optimized for agricultural environments. First, we introduce efficient multi-head attention (C3K2-EMA), which reduces noise interference by 41%… More >

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Anfeng Yang, Fei Kang, Wenjuan Bu^*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073979 - 12 March 2026
Abstract Large language models (LLMs) have demonstrated significant capabilities in semantic understanding and code generation. However, cybersecurity tasks often require prompting the adaptation of open-source models to this domain. Despite their effectiveness, large-parameter LLMs incur substantial memory usage and runtime costs during task inference and downstream fine-tuning for cybersecurity applications. In this study, we fine-tuned six LLMs with parameters under 4 billion using LoRA (Low-Rank Adaptation) on specific cybersecurity instruction datasets, employing evaluation metrics similar to Hackmentor. Results indicate that post-fine-tuning, smaller models achieved victory or parity rates up to 85% against larger models like Qwen-1.5-14B… More >

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Zhengyu Wu¹, Kejun Kang², Yixiu Liu^3,*, Chenpu Li³
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075593 - 12 March 2026
Abstract Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention More >

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151

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Yao-Hsin Chou¹, Cheng-Yen Hua¹, Ru-Wei Tseng¹, Shu-Yu Kuo^2,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075705 - 12 March 2026
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract The rapid growth of mobile and Internet of Things (IoT) applications in dense urban environments places stringent demands on future Beyond 5G (B5G) or Beyond 6G (B6G) networks, which must ensure high Quality of Service (QoS) while maintaining cost-efficiency and sustainable deployment. Traditional strategies struggle with complex 3D propagation, building penetration loss, and the balance between coverage and infrastructure cost. To address this challenge, this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate (GQTS-QNG) framework for 3D base-station deployment optimization. The problem is formulated as a multi-objective model… More >

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Lixing Tan^1,2, Liusiyu Chen¹, Yang Wang¹, Zhenyu Song^1,*, Zenan Lu^1,3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075959 - 12 March 2026
（This article belongs to the Special Issue: Advanced Networking Technologies for Intelligent Transportation and Connected Vehicles)
Abstract Anomaly detection is a vibrant research direction in controller area networks, which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles. However, existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features, resulting in fragmented feature representations that compromise model stability and accuracy. To improve the extraction of valuable features, this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering (EDESC-IDS). Following the… More >

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Ksenia Kharitonova¹, David Pérez-Fernández², Zoraida Callejas^1,3, David Griol^1,3,*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075777 - 12 March 2026
（This article belongs to the Special Issue: Security and Robustness of Large Language Models (LLMs))
Abstract Building reliable intent-based, task-oriented dialog systems typically requires substantial manual effort: designers must derive intents, entities, responses, and control logic from raw conversational data, then iterate until the assistant behaves consistently. This paper investigates how far large language models (LLMs) can automate this development. In this paper, we use two reference corpora, Let’s Go (English, public transport) and MEDIA (French, hotel booking), to prompt four LLM families (GPT-4o, Claude, Gemini, Mistral Small) and generate the core specifications required by the rasa platform. These include intent sets with example utterances, entity definitions with slot mappings, response templates,… More >

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ARTICLE

Rui Yao^1,2, Yuye Wang^1,2,*, Fei Yu^1,2,3,*, Hongrun Wu^1,2, Zhenya Diao^1,2
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073861 - 12 March 2026
Abstract Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based More >

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ARTICLE

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026
Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

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86

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ARTICLE

Mohammad Nauman^*
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077259 - 12 March 2026
Abstract Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves More >

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462

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ARTICLE

Hamad Ali Abosaq¹, Jarallah Alqahtani^1,*, Fahad Masood², Alanoud Al Mazroa³, Muhammad Asad Khan⁴, Akm Bahalul Haque⁵
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075250 - 12 March 2026
Abstract The technological advancement of the vehicular Internet of Things (IoT) has revolutionized Intelligent Transportation Systems (ITS) into next-generation ITS. The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment. The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient, mobility-aware, and secure system for distributed intelligence. This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning (DRL-FL) approach to design an energy-efficient and threat-resilient ITS. In this approach, a Policy Proximal Optimization (PPO)-based DRL agent is first employed for adaptive client selection. Second, More >

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60

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ARTICLE

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
（This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
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 >

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241

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Lei Sun^1,2, Jingwen Wang^2,*, Peng Hu², Xiuqing Mao^1,2, Cuiyun Hu^1,2, Zhihong Wang²
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073657 - 12 March 2026
Abstract Transformer models face significant computational challenges in private inference (PI). Existing optimization methods often rely on isolated techniques, neglecting joint structural and operational improvements. We propose IG-3D, a unified framework that integrates structured compression and operator approximation through accurate importance assessment. Our approach first evaluates attention head importance using Integrated Gradients (IG), offering greater stability and theoretical soundness than gradient-based methods. We then apply a three-dimensional optimization: (1) structurally pruning redundant attention heads; (2) replacing Softmax with adaptive polynomial approximation to avoid exponential computations; (3) implementing layer-wise GELU substitution to accommodate different layer characteristics. A More >

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366

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Peiying Zhang^1,2, Yihong Yu^1,2, Jia Luo^3,4,*, Nguyen Gia Ba⁵, Lizhuang Tan^6,7, Lei Shi⁸
CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076690 - 12 March 2026
（This article belongs to the Special Issue: AI-Driven Next-Generation Networks: Innovations, Challenges, and Applications)
Abstract Next-Generation Networks (NGNs) demand high resilience, dynamic adaptability, and efficient resource utilization to enable ubiquitous connectivity. In this context, the Space-Air-Ground Integrated Network (SAGIN) architecture is uniquely positioned to meet these requirements. However, conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics, such as its heterogeneous structure, dynamic topology, and constrained resources, leading to suboptimal performance under disruptions such as node failures or cyberattacks. To meet these demands for SAGIN, this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation. Methodologically, we define three core routing performance metrics,… More >

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