Open Access
ISSN:1546-2218 (print)
ISSN:1546-2226 (online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2025 Impact Factor 2.4; Scopus CiteScore (Impact per Publication 2025): 6.6; SNIP (Source Normalized Impact per Paper 2025): 0.777; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086072 - 15 September 2026
Abstract The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085434 - 15 September 2026
Abstract The integration of the Internet of Things (IoT) into Intelligent Transportation Systems (ITS) is transforming urban mobility through widespread sensing, real-time data exchange, and Artificial Intelligence (AI)-driven adaptive control. Although research in this domain has expanded rapidly, bibliometric analyses combined with critical thematic synthesis remain limited. This study addresses this gap through a two-stage analysis of 574 peer-reviewed articles indexed in Scopus from 2011 to 2024. Using performance analysis, keyword co-occurrence mapping, and co-authorship network visualization, the study maps global publication trends, institutional productivity, and collaboration patterns. The results show an annual growth rate of… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082189 - 15 September 2026
Abstract With the growing world population and demand for agricultural goods, agriculture requires innovative technologies that make the best use of resource, reduce waste, and increase productivity. So, smart agriculture, which involves the use of innovative digital technologies to enhance the quality and quantity of harvests, has come into play, superseding traditional agriculture. In recent years, the concept of the digital twin has intertwined with smart agriculture to enable precise control of entire farms, facilitating virtual replications. Overall, the digital twin enables continuous monitoring of real-time conditions in the field, providing valuable insights into crop health,… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.077836 - 15 September 2026
Abstract The rapid increase in vehicle numbers and resulting traffic congestion have amplified critical challenges related to safety, environmental impact, and transportation efficiency. Road accidents account for approximately 1.19 million deaths annually, with an additional 20 to 50 million people injured. Moreover, congestion leads to the loss of nearly 50 billion hours and around 3 billion gallons of fuel each year. These pressing issues necessitate innovative and integrated solutions that can enhance the overall performance of the road. This study investigates the integration of Emerging Computing Technologies (ECT) into smart road infrastructures as a potential response… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085383 - 15 September 2026
（This article belongs to the Special Issue: Advanced Technologies and Intelligent Applications for Autonomous Vehicles)
Abstract With the rapid development of fifth generation (5G), 5G-Advanced, edge computing, and early sixth generation (6G) technologies, the Internet of Vehicles (IoV) is evolving toward highly connected, intelligent, and delay-sensitive transportation services. Nevertheless, ground infrastructure still faces coverage holes, blockage, overloaded roadside units (RSUs), limited backhaul, and weak service continuity in urban canyons, crowded intersections, long highway segments, rural roads, and emergency areas. Unmanned aerial vehicles (UAVs) can provide flexible aerial relay, mobile sensing, temporary coverage, and lightweight edge computing support for these scenarios. This survey reviews UAV-assisted IoV from an integrated sensing, communication, and… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085515 - 15 September 2026
Abstract Trust plays an important role in shaping human behaviour in cybersecurity contexts, influencing how individuals interact with digital systems and respond to security practices. However, existing research on trust and cybersecurity behaviours remains fragmented, with different studies adopting varied definitions of trust and examining a wide range of behavioural outcomes. This scoping review aims to systematically map and synthesise the literature on trust and cybersecurity behaviours. Following the PRISMA-ScR guidelines, relevant studies were identified, screened, and analysed to examine how trust has been conceptualised, what types of behaviour have been studied, and which theoretical and… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083445 - 15 September 2026
Abstract The Internet of Things (IoT) enables seamless interconnectivity among billions of smart devices, transforming industries through real-time sensing, data processing, and intelligent decision-making. As IoT systems manage large volumes of sensitive data, ensuring secure and covert communication has become critical. Steganography, which conceals confidential information within ordinary transmissions, has emerged as a promising approach to strengthen security and privacy in IoT environments. However, despite the growing body of work, existing surveys often address steganography in general contexts without systematically analyzing its adaptation to the unique constraints of IoT systems. This article addresses this gap by… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086207 - 15 September 2026
Abstract Agriculture plays an important role in food security and social development. With the rapid development of the Fourth Agricultural Revolution, also known as Agriculture 4.0, traditional weed control methods that rely on manual experience and uniform herbicide application can no longer meet the demands for efficient, precise, and environmentally friendly production. Farmland weeds compete with crops for light, water, and nutrients, thereby seriously affecting crop yield and quality. Therefore, the development of efficient weed detection and recognition technologies is of great significance. In recent years, the rapid progress of deep learning in computer vision has… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.089115 - 15 September 2026
（This article belongs to the Special Issue: Explainable and Accountable Natural Language Processing)
Abstract Large language models and retrieval-augmented generation (RAG) systems are increasingly employed to transform evidence into decision-facing briefings, alerts, and recommendations. In these settings, explainability cannot be evaluated merely by fluency, readability, or factual correctness. A briefing may be factually correct while still being unsafe if it cites sources that do not substantiate the claim, suppresses uncertainty, converts correlational evidence into causal language, recommends an unauthorized action, or leaves no auditable path for human review. This review synthesizes 104 sources spanning explainable natural language processing (NLP), faithful explanation, hallucination and factuality evaluation, RAG, citation faithfulness, uncertainty… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084893 - 15 September 2026
Abstract Gamification has emerged as an effective approach to enhance user engagement and security awareness in the field of information security. This study presents a systematic literature review (SLR) of research published between 2012 and March 2026, analysing publications from two major databases, Scopus and Web of Science (WoS). The review was conducted following the PRISMA 2020 guidelines. A comprehensive set of seven keywords: “gamification”, “information security”, “cybersecurity”, “security awareness”, “business security and privacy training”, “security management”, and “incident response”, was used to retrieve relevant studies. A total of 1487 articles were initially identified, of which… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084278 - 15 September 2026
Abstract Recommender systems can alleviate information overload by producing item recommendations tailored to user preferences. The performance usually relies on rich user-item interaction data; however, missing entries introduce sparsity that substantially degrades performance. Early work primarily treated rating imputation as a preprocessing mechanism for mitigating sparsity and alleviating cold-start issues through explicit matrix completion. More recently, missing-data estimation has evolved beyond static preprocessing toward broader inference-oriented paradigms, including pseudo-label estimation, counterfactual inference, and debiasing mechanisms integrated directly into the learning objective. In this paper, we present a structured review of rating imputation and inference-oriented missing-data estimation… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085321 - 15 September 2026
Abstract The emergence of Large Language Models (LLMs) has transformed biomedical text summarization, shifting research beyond conventional extractive approaches toward increasingly generative and agentic reasoning paradigms. However, rapid advances in model capabilities have exceeded current understanding of their methodological rigor, evaluation adequacy, and clinical readiness. This systematic review conducts a structured exploratory audit of Transformer- and LLM-based biomedical summarization systems to characterize reporting quality, validation practices, and translational maturity. Following PRISMA 2020 guidelines and the Population–Concept–Context framework, we systematically reviewed 178 original English-language studies published between January 2017 and March 2026. A multidimensional evaluation pipeline was… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088224 - 15 September 2026
Abstract Autonomous mobile robots increasingly rely on three tightly coupled capabilities: low-latency wireless connectivity, edge-side compute acceleration, and simulation-based pre-training of perception and control models. Each has been studied extensively in isolation, but their joint deployment on a single platform remains rare. This paper presents an integrated framework combining a private 5G Stand-Alone (5G SA) access network, a Multi-Access Edge Computing (MEC) layer with adaptive offloading, and a digital-twin training pipeline in NVIDIA Omniverse Isaac Sim. It is realised on the Yahboom ROSMASTER R2 with an NVIDIA Jetson Orin NX, a Quectel RM530N-GL 5G modem in… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087308 - 15 September 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Optimization for Lightweight Materials and Structures)
Abstract Designing two-dimensional anisotropic mechanical metamaterial unit cells from prescribed effective properties remains a challenging inverse problem, particularly when directional stiffness and material usage need to be controlled simultaneously. In this work, a data-driven conditional diffusion framework is developed for generating unit-cell structures with target effective elastic moduli and volume fractions. A structure–property database containing 57,000 binary unit-cell images is first established through a random target-property-driven inverse homogenization method. The effective elastic moduli in the x and y directions, together with the volume fraction, are used as conditional labels, denoted as (Ex, Ey, V). A conditional denoising diffusion probabilistic… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086475 - 15 September 2026
Abstract Cold-start alloy-design campaigns often have too few labeled compositions to reliably locate phase boundaries for tree-structured composition-space Gaussian process regression (TCGPR). We study a controlled way to incorporate external CALPHAD-like boundary information into this partitioning step. The proposed MP-TCGPR method adds a Gaussian MAP penalty centered on a thermodynamic boundary estimate and uses an adaptive width to reduce prior influence as node-level data accumulate. The revised theory distinguishes asymptotic convergence from convergence rate: a fixed-width prior is also asymptotically negligible under local regularity, whereas the adaptive schedule accelerates finite-sample prior More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086403 - 15 September 2026
Abstract NBT-based lead-free piezoceramics are promising alternatives to Pb-containing materials, yet their functional properties arise from complex and coupled composition–processing–structure–property relationships. Here, we develop a structure-informed machine learning framework to predict and interpret the piezoelectric coefficient d33, depolarization temperature Td, and relative dielectric permittivity εr. A database of 214 records from 34 publications was compiled, including 204 records for model development and 10 records for independent literature validation. The correlation analysis involved 38 variables, including 35 candidate input descriptors and three target properties. After Pearson correlation-based redundancy filtering, 30 nonredundant input descriptors were retained for model development. To… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087556 - 15 September 2026
Abstract Hot-section nickel-based single-crystal superalloy components under isothermal cyclic loading often exhibit systematic life shifts when datasets span different stress ratios and notch severities, making it difficult to maintain a globally consistent parameter set using conventional models. Because the effects of temperature, stress ratio, and stress concentration on cyclic response and damage evolution are typically nonlinear and coupled, this study proposes a multi-level equivalent driving force framework for fatigue life prediction, in which condition-induced life differences are represented as comparable shifts on a unified engineering driving-force scale. The proposed framework links the nominal cyclic response, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084636 - 15 September 2026
Abstract The accuracy of the electrode properties is important in the lithium-ion battery manufacturing process because the thickness variation is a direct influence on the compaction and structural uniformity, transport behavior and overall manufacturing quality. Of the different types of monitoring, ultrasonic frequency-domain relies on a non-destructive pathway for quality evaluation in a process-aware manner and is a promising approach; but, interpretable predictive modeling has been limited at the electrode level. In this study, an open-access database of ultrasonic frequency-domain data of lithium-ion battery electrodes under coating and calendering conditions was used to develop an artificial… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084790 - 15 September 2026
Abstract Non-equibiaxial residual stresses are prevalent in engineering components such as welding, additive manufacturing, and surface strengthening, making their accurate detection critical for ensuring structural integrity. This paper proposes a novel method capable of simultaneously identifying two principal stress components (, ) using only an individual instrumented indentation. First, the normalized total indentation work variation Wnorm and the residual indentation ellipticity λ are extracted as sensitive features from the indentation responses through dimensional analysis. Subsequently, a finite element (FE) simulation database comprising 2400 datasets was established to train three types of neural networks: the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085634 - 15 September 2026
Abstract Hail impacts on photovoltaic laminates generate strongly nonlinear contact forces whose polynomial restoring form and coefficients govern the resulting damage pattern. Predicting the dynamic response across a range of impact velocities, and inferring substrate properties from post-event vibration measurements, are two tasks that classical time-integration schemes do not address in a unified manner. This work develops a physics-informed neural network (PINN) framework that handles both. For the forward problem, the network is conditioned on the initial velocity and trained simultaneously at four representative hail-impact speeds, i.e., m/s, so that it learns… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087255 - 15 September 2026
Abstract In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083413 - 15 September 2026
Abstract Scanning tunneling microscopy (STM) images are frequently affected by low-frequency vibrations, substrate-induced background variations, and bias-dependent contrast changes, which degrade molecular feature responses and hinder reliable segmentation. To address this challenge, we develop an STM-oriented Feature Pyramid Network (FPN) + Dual-Path Intensity Calibration (DPIC) framework by adapting a DPIC module, originally derived from a cloud-noise calibration mechanism, to the specific characteristics of STM molecular images. In this framework, DPIC is reformulated as a noise-aware feature calibration module that suppresses low-response background interference while preserving foreground molecular contours. We integrated DPIC into the FPN architecture and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084774 - 15 September 2026
（This article belongs to the Special Issue: Advanced Security and Privacy in Blockchain Systems)
Abstract Multisignature (multisig) wallets are fundamental to institutional-grade asset security on the Ethereum blockchain, yet Security Operations Centers (SOCs) currently rely on manual threshold rules to flag anomalous executions. Existing anomaly detection approaches suffer from three methodological deficiencies: (i) reliance on random train-test splits that leak future information, (ii) inclusion of post-hoc execution features unavailable at prediction time, and (iii) absence of cross-architectural benchmarking to justify algorithmic choices. This paper addresses all three gaps through a rigorous LightGBM-based framework that automates and explains SOC heuristics. We frame anomaly detection as post-execution forensic triage, where the model… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083956 - 15 September 2026
Abstract Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge,… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085467 - 15 September 2026
Abstract The engineering of Federated Learning (FL) systems faces significant challenges in balancing two critical non-functional requirements: ensuring robust system utility and maintaining high privacy protection standards under non-independent and identically distributed (Non-IID) data environments. Existing software architectures often struggle to achieve an optimal trade-off between these competing demands. This paper proposes SAM-ADPFL, a novel architectural framework designed to improve the engineering and management of privacy-preserving distributed machine learning systems. First, we design a geometry-aware adaptive aggregation component that dynamically reallocates aggregation weights based on local landscape properties, guiding the global model to effectively suppress model More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085839 - 15 September 2026
Abstract With the development of brain-computer interfaces (BCI), more and more studies are using electroencephalography (EEG) for emotion recognition. Traditional emotion recognition often uses 2D videos and pictures to stimulate emotions, which do not provide an immersive feeling. Virtual reality (VR) can provide a more immersive and realistic experience, and recent studies are beginning to utilize EEG for emotion recognition in VR. However, due to the limited information on single-modal features, it is not possible to fully recognize individual emotions. To address this problem, we proposed a multi-modal approach in VR, which utilized a VR scene… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026
Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087068 - 15 September 2026
Abstract Printed circuit board (PCB) defect detection is critical for industrial quality control, where detection models must identify small and irregular defects while satisfying real-time inspection requirements. However, conventional deep learning-based detectors often require substantial computational resources, making deployment on edge devices difficult. To address this issue, FGE-YOLO is proposed as a deployment-oriented lightweight object detection model based on YOLOv8. The proposed model integrates a FasterNet-based backbone, a GhostConv-Based Neck, and an Efficient Channel Attention (ECA) mechanism. In the backbone, standard convolutions are retained in the shallow P1 and P2 stages to preserve low-level spatial details,… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026
Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086022 - 15 September 2026
Abstract With the rapid development of artificial intelligence technologies in machine learning-as-a-service (MLaaS), deep learning-based intelligent models have demonstrated high value in applications such as trend prediction and risk assessment. However, MLaaS data are typically highly sensitive and contain private information. In cross-institutional collaborative modeling scenarios, different departments and local centers often hold partial, heterogeneous data resources on MLaaS platforms. Given data security, privacy, and compliance requirements, raw data are difficult to share directly, which makes it challenging to apply centralized model training methods. This paper proposes FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085203 - 15 September 2026
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract This study proposes the Artificial Intelligence-integrated Inspection Ecosystem (AIIE) as an autonomous condition monitoring platform to enable Prognostics and Health Management (PHM) for underground infrastructure facilities at the Korea Electric Power Corporation (KEPCO) power Information and Communication Technology (ICT) center. To address the environmental dependency of conventional systems, which necessitate extensive control logic redesigns upon changes in target facilities or environments, a three-layer abstraction architecture separating directive, orchestration, and execution roles is established, integrating a quadrupedal robot with heterogeneous sensors into a unified control structure. To overcome the limitation of relying on one general-purpose model… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086288 - 15 September 2026
（This article belongs to the Special Issue: Large Language Models: Foundations, Advances, and Emerging Applications)
Abstract Large language models (LLMs) have emerged as a transformative foundation across natural language processing and intelligent systems, yet their security, robustness, and responsible deployment remain critical open challenges. In particular, the multi-dimensional evaluation of toxicity and bias in Chinese LLMs remains limited, posing significant risks for real-world applications that demand trustworthy AI. In this paper, we propose TrustEval, a dataset- and model-agnostic evaluation framework that provides a systematic assessment of Chinese LLMs from the perspectives of toxicity, bias, and robustness. Unlike existing benchmarks that focus primarily on capability, TrustEval explicitly targets model security and reliability… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086232 - 15 September 2026
Abstract The accuracy of partial discharge (PD) pattern recognition is essential for assessing the insulation condition of gas-insulated switchgear (GIS). However, in practical recognition tasks, phase-resolved partial discharge (PRPD) patterns often exhibit complex feature distributions, and key discharge characteristics may be weakened during feature extraction. This study proposes an improved sparrow search algorithm (ISSA)-optimized attention-enhanced ConvNeXt model for GIS PD pattern recognition. A multi-criterion grayscale evaluation scheme is first employed to select the most suitable grayscale conversion for PRPD patterns, aiming to preserve informative discharge regions and reduce redundant color interference. Subsequently, an attention-enhanced ConvNeXt model… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086097 - 15 September 2026
Abstract To address the challenge of precise station-keeping for underactuated unmanned surface vehicles (USVs) in unknown current environments, our team previously proposed a solution based on a “virtual anchoring” method. However, field tests revealed that the inherent “virtual anchor line” constraint limits positioning accuracy. This work introduces a novel control strategy to overcome the aforementioned issue, which enables accurate unmanned surface vehicle (USV) station-keeping by significantly reducing the distance constraint inherent to traditional virtual anchoring. The core innovation lies in a Dynamic Virtual Guide-Point, whose position is updated based on a real-time estimate of the current… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084783 - 15 September 2026
Abstract The management of demand response (DR) in smart grids increasingly relies on low-latency, reliable, and scalable communication, yet traditional DR signaling methods fail in dense network environments. In this paper, a communication-aware clustered demand response architecture (CCA-DR) for smart grids based on 5G technology is proposed, in which DR users are clustered based on service-area density and assigned to the closest communication-aware cluster. It is linked via directional antenna pairs to reduce cumulative signal attenuation. The model incorporates the properties of the 5G broadband channels, such as path loss, interference, latency, and reliability constraints, into… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086163 - 15 September 2026
（This article belongs to the Special Issue: Recent Advances in Malware Detection)
Abstract There is currently a lack of systematic research on the fine-grained detection of multi-language and multi-type exploit scripts. To address this gap, this study proposes a model named ASTBertX (AST + BERT + XGBoost) for identifying the specific exploit types of malicious scripts; the model organically integrates code sequence semantics with structural semantics. First, the model utilizes the pre-trained model GraphCodeBERT to extract contextual semantic representations of the scripts; simultaneously, it introduces semantic enhancement nodes into the Abstract Syntax Tree (AST) and employs GATv2 to learn the AST’s structural representation. These two representations are mapped… More >
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083871 - 15 September 2026
Abstract Currently, mobile robot path planning in unstructured forest environments remains a hot research topic in the robotics field. Studies applying the Greylag Goose Optimization (GGO) algorithm to multi-robot path planning under such scenarios are limited, and these approaches still face significant challenges, such as insufficient trajectory smoothness, frequent coordination conflicts, and relatively slow convergence to optimal solutions. To address these issues, this paper proposes an Evolutionary Game-Theoretic Improved GGO algorithm (EG-IGGO), designed to optimize path quality while ensuring robust obstacle avoidance capabilities. Specifically, two novel strategies—the population alignment strategy and the dual-source adaptive guidance strategy—are… More >
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086550 - 15 September 2026
（This article belongs to the Special Issue: Complex Network Approaches for Resilient and Efficient Urban Transportation Systems)
Abstract Accurate vehicle detection, tracking, and counting are fundamental components of Intelligent Transportation Systems (ITS) and urban traffic surveillance. However, real-world deployment remains challenging due to domain shifts, illumination variations, occlusions, dense traffic conditions, and heterogeneous data distributions. Existing studies often address detection, tracking, and counting as independent tasks, resulting in limited cross-domain generalization and inconsistent performance in complex traffic environments. To overcome these limitations, this paper proposes a unified cross-domain framework that jointly integrates vehicle detection, tracking, and lane-aware counting within a single intelligent traffic analytics pipeline. The proposed framework begins with image enhancement using… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084902 - 15 September 2026
Abstract The development of deepfake technologies is a threat to digital media authentication and cybersecurity infrastructure. The current paper proposes a method for detecting manipulated images of faces based on the Vision Transformer architecture. We fine-tune a pre-trained ViT-Base-Patch16-224 model based on this well-curated dataset of 12,137 face images, which includes an almost equal number of real and synthetic face images using a variety of different generation methods. The data set contains real-life photographs of CelebA and FFHQ, along with artificial samples of the publicly available Kaggle repositories (FaceForensics++, Celeb-DF, and DFDC) and 600 self-collected photos… More >
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082352 - 15 September 2026
Abstract Multimedia technology and social media platforms like Facebook, Telegram, Viber, and WhatsApp, as well as numerous industries like communications, banking, and the military, depend heavily on images. Therefore, the biggest issue these days is safeguarding the image from theft or hacking during storage or transmission over the internet. This study suggests a novel approach of grayscale image encoding that uses a two-dimensional (2D) logistic map and the Secure Hash Algorithm (SHA). First, convert a color image to grayscale. Second, use the recently proposed equations to calculate the initial states of the Two-Dimensional Logistic Map (2DLM).… More >
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CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087148 - 15 September 2026
Abstract Intelligent transportation systems, a critical pillar for smart cities, enable real-time vehicular communications to prevent human errors and improve traffic safety and efficiency. However, the open and highly dynamic nature of vehicular networks exposes them to various security threats, including message tampering, impersonation, and privacy violations. Several authentication and key agreement (AKA) protocols have been proposed to mitigate these risks, but often fail to maintain future-proof security against emerging quantum threats or introduce significant latency that affects real-time applications by relying on computationally expensive public-key cryptography, blockchain or centralized architectures. This paper proposes a new… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087279 - 15 September 2026
Abstract Multimodal Emotion Recognition in Conversations (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they apply shared or coarse-grained graph construction rules across modalities, overlooking their distinct dependency patterns; (2) they rely on fixed-activation MLPs for feature transformation, limiting nonlinear representation capacity in complex emotional scenarios; (3) they focus predominantly on contextual modeling while underexploring local emotion discrimination between related utterances. To address these issues, we propose Joint Differentiated Modality-Aware Heterogeneous Graph and Local Emotion Difference Supervision for Multimodal Emotion Recognition in Conversations (DMHG-LEDS), a novel MERC framework. Specifically, modality-aware… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026
Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085788 - 15 September 2026
Abstract Smart grids (SG) integrate multiple network entities to achieve automation, but their interconnected nature also exposes communication networks to various security threats, such as replay, tampering, and man-in-the-middle (MITM) attacks. Existing encryption frameworks for smart grid edge devices often suffer from high computational complexity or lack of dynamic key management, leading to key leakage risks and communication bottlenecks. To address these challenges, this research proposes a lightweight end-to-end secure communication architecture specifically designed for smart grid power monitoring. This framework employs the Message Queuing Telemetry Transport (MQTT) protocol as the asynchronous communication backbone, effectively alleviating… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085803 - 15 September 2026
Abstract This study proposes a Genetic Programming-based Search Strategy Generation Framework (GP-SSGF) and a novel variant of the tumbleweed algorithm, the genetic programming-based tumbleweed algorithm (GPTA). The framework automates the evolution of search formulas within metaheuristic algorithms, reducing reliance on manually designed update rules and enhancing adaptability. The GPTA algorithm, developed within this framework, employs evolved position-update formulas to improve search efficiency and convergence. Through extensive experiments on the CEC2017 benchmark suite across multiple dimensions, GPTA demonstrates superior solution quality and stability compared with other metaheuristic algorithms. Its practical effectiveness is further validated in emergency material More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085198 - 15 September 2026
Abstract Marble classification has traditionally relied on human visual inspection, where operators assess color, texture, and pattern alignment to determine quality. However, this manual process is subjective, inconsistent, and inefficient for large-scale industrial applications. To address these limitations, this study proposes DeepMarbleVision, a texture-aware ensemble deep learning framework with energy-layer-based feature fusion for marble quality classification. A real-world dataset was created using the MarbleVision system, including three marble quality classes acquired from an industrial marble classification environment. The proposed approach integrates energy-layer-based feature fusion into TCNN variants of AlexNet, ResNet, and DenseNet, which were initialized through… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086607 - 15 September 2026
Abstract Autonomous driving systems must reason not only about the current scene but also about how the environment may evolve under alternative actions. Although predictive world models can generate future latent rollouts, these rollouts are often consumed directly by planners or explanation modules without an explicit and auditable interpretation stage. This paper presents a predictive and explainable driving framework centered on a Future Interpretation Module (FIM), which transforms action-conditioned future rollouts into structured descriptors, including risk trend, peak risk, time-to-critical, minimum clearance, predicted collision, dominant predicted event, and confidence. An aligned latent interface, trained with feature-alignment… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083187 - 15 September 2026
（This article belongs to the Special Issue: Next-Generation Optimization: Quantum and Hybrid Classical Computing for Real-World Applications)
Abstract Quantum logic reversible synthesis is a fundamental operation in quantum computing. One of the most challenging issues in this field resides in navigating the immense search space to synthesize the most compact circuit configurations, which are critical for realizing reliable, noise-free, and error-free quantum computing systems. To address this challenge, this study proposes a novel hypercube-encoded quantum-inspired optimization framework to formulate the synthesis task as a trajectory-finding process. This structure-informed domain knowledge transformation delivers exceptional search direction guidance, moving away from blind, black-box exploration. Specifically, by mapping the reversible functions onto the hypercube architecture, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087054 - 15 September 2026
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications, 2nd Edition)
Abstract With the rapid growth in the number of end devices in the Internet of Things (IoT), network traffic has become increasingly complex and redundant, while multiple attack types often coexist, posing major challenges to traffic anomaly detection. Traditional machine learning-based methods for IoT traffic anomaly detection often suffer from severe feature redundancy, high computational complexity, and low detection efficiency, making it difficult to simultaneously achieve high detection accuracy and computational efficiency. To address this issue, metaheuristic algorithms are often introduced in the feature selection stage to reduce feature redundancy and improve detection efficiency. However, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085403 - 15 September 2026
（This article belongs to the Special Issue: Advanced and Interpretable Malware Detection in Modern Cyber Environments)
Abstract With the urgent need for Intrusion Detection Systems (IDS) to protect digital infrastructure, eXplainable Artificial Intelligence (XAI) has become an important supporting layer. The integration of XAI and IDS can rank influential features that affect IDS decisions, yet these outputs often remain difficult to translate into operational security actions. In this work, we propose LEXIS (LLM-Enhanced eXplainable Intrusion detection System), an LLM-enhanced explainable IDS that converts sample-level explanations into structured report drafts that organize feature attributions into candidate response actions for analyst review, through an evidence-bounded reporting process. Given a network trace, the classifier generates… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085049 - 15 September 2026
Abstract Feature selection (FS) plays a crucial role in machine learning by reducing data dimensionality and improving learning efficiency. In many real-world scenarios, label information is unavailable, making unsupervised FS particularly important. While Genetic Algorithm (GA) offers a powerful global search mechanism for subset selection, it often suffers from premature convergence and struggles to refine solutions in complex search spaces. To address these limitations, we propose a hybrid GA that integrates an information-theoretic local search strategy for unsupervised FS. The proposed method integrates an information-theoretic local refinement procedure, consisting of DEL and ADD operations based on… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085838 - 15 September 2026
（This article belongs to the Special Issue: Advances in Intelligent Video Object Tracking and Scene Understanding)
Abstract Robust scene-level classification and semantic understanding from aerial and disaster-related imagery are essential for intelligent vision systems deployed in emergency response, UAV-based monitoring, and safety-critical environments. However, existing deep learning approaches, including convolutional neural networks and Vision Transformers (ViTs), often struggle to simultaneously capture fine-grained local object characteristics and global semantic scene context, while also lacking reliable uncertainty estimation mechanisms for trustworthy decision-making. To address these limitations, this paper proposes MS-SLCA-ViT, a novel multi-scale scene–local cross-attention Vision Transformer framework for robust and uncertainty-aware image scene understanding. The proposed architecture introduces three major contributions. First, a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085703 - 15 September 2026
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation, 2nd Edition)
Abstract Growth of cancerous cells is unpredictable, and their effects vary across organs and levels of aggression. Identification of the pattern, size, and shape of the growth helps assess severity for better treatment. The proposed LiteDKT-Net combines the DK-IRB (Diverse Kernel Inverted Residual Block) block and Transformer to target conceptual information about shape and location. For better edge detection, LiteDKT-Net uses GAG (Group Attention Gate) followed by CBAM (Convolutional Block Attention Module). LiteDKT-Net is a lightweight encoder-decoder-based network optimized for accurate brain tumor segmentation. The network parameter optimization and reduced computational complexity in LiteDKT-Net enable high… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084242 - 15 September 2026
Abstract The Artificial Protozoa Optimizer (APO) is a population-based metaheuristic for numerical optimization and engineering design. However, its stochastic initialization and limited local refinement can reduce performance on non-convex, discontinuous, and high-dimensional landscapes. To address these issues, this paper proposes an enhanced Artificial Protozoa Optimizer with a multi-strategy framework (EAPO). The method incorporates three mechanisms: a Symmetry-Enhanced Latin Hypercube Initialization (SELHI) strategy to improve the uniformity of the initial population, an Adaptive Phase Equilibrium Strategy (APES) to regulate the exploration–exploitation balance using iteration progress and population diversity, and an Adaptive Elite Perturbation Strategy (AEPS) to strengthen More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086571 - 15 September 2026
Abstract Multi-modal information fusion integrates data from various sensors, distinct sources, or different modalities, such as audio, images, and text, to achieve a more comprehensive and accurate understanding and analysis. This paper proposes a Semi-Tensor Product-based Block Term Decomposition of Multilinear (STP-BTDM) pooling method and applies it to sentiment analysis and emotion recognition. Unlike prior factorized multilinear approaches, STP-BTDM introduces block-term decomposition with a block-diagonal core tensor, yielding a globally sparse yet locally dense structure and enabling modality-specific independent subspace learning. The technique first introduces the Semi-Tensor Product-based Block Term Decomposition (STP-BTD) model to obtain globally… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085242 - 15 September 2026
Abstract Detecting surface defects on steel is challenging because many defect regions are visually weak, have blurred boundaries, and contain minimal pixel information. In detectors from the You Only Look Once (YOLO) family, these subtle cues may be weakened at the early feature extraction stage and further attenuated during repeated downsampling. To improve the preservation and utilization of such defect-related details, this paper proposes EFAS-YOLO, a lightweight YOLOv11-based detection framework for steel surface defect inspection. First, an Edge-Frequency Aware Stem (EFAS) is introduced before the backbone to explicitly extract Sobel-based gradient responses and fuse them with… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085763 - 15 September 2026
Abstract Healthcare systems based on the Internet of Things (IoT) are widely used in patient monitoring, telemedicine, emergency care, and hospital-at-home services. However, existing IoT healthcare networks still face major challenges related to security, trust management, network control, and real-time emergency data handling. Centralized trust mechanisms and repeated cloud-based verification may increase delay and reduce reliability in critical healthcare scenarios. Moreover, suspicious medical devices must be quickly isolated, while sensitive patient data and emergency traffic must be protected and prioritized. To address these issues, this work proposes a blockchain-enabled, software-defined networking (SDN)-based edge computing framework for… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085750 - 15 September 2026
Abstract Driven by recent breakthroughs in generative artificial intelligence, modern voice cloning technologies can synthesize remarkably lifelike human speech, exacerbating security vulnerabilities associated with identity impersonation, financial fraud, and deepfake audio proliferation. To mitigate these risks, this paper introduces EchoMark, an acoustic-layer disruption framework designed to systematically undermine neural speech generation workflows. Unlike conventional digital watermarking or software-level perturbation strategies, EchoMark embeds structured, multi-tiered echo patterns directly into audio during physical playback and re-recording. This physical-layer integration severely compromises the spectral coherence essential for neural text-to-speech (TTS) modeling, resulting in degraded acoustic fidelity and impaired speech-fitting More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088561 - 15 September 2026
（This article belongs to the Special Issue: Advanced Privacy Computing for Intelligent Distributed Networks and Systems)
Abstract Speaker verification on phones, wearables, and voice-enabled Internet-of-Things gateways must balance local privacy with reliable decisions under adverse audio. Existing privacy-preserving verification schemes generally protect a fixed representation, whereas edge-offloading policies usually adapt computation without attaching an explicit feature-disclosure budget; neither line alone coordinates trial uncertainty, communication state, and privacy expenditure. This paper presents privacy-preserving uncertainty-aware adaptive feature offloading (P-UAFO), an edge-intelligence framework that keeps raw audio local and transmits only clipped, projected, quantized, and Gaussian-perturbed intermediate features when their expected benefit justifies resource cost. Its online pipeline first estimates decision uncertainty and resource state,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085342 - 15 September 2026
Abstract The modernization of communication and healthcare environments has introduced critical security challenges, as all transmission is almost certainly done through digital networks prone to attacks. To counteract this, researchers have worked to create methods of data concealment, hiding the existence of sensitive data itself from prying eyes. However, many of these methods lack the necessary ability to balance imperceptibility and payload capacity. In addition, different from generic images, medical images must preserve their structural and visual integrity, requiring frameworks that prioritize maintaining high similarity between images or, at times, complete recovery of the original image.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085338 - 15 September 2026
Abstract Temporal action detection aims to localize action instances in untrimmed videos and recognize their categories. Although recent detectors have achieved strong performance, accurate boundary localization remains challenging due to gradual action transitions, temporal ambiguity, and annotation uncertainty. Existing boundary supervision usually relies on point-wise classification or local regression losses, which compare predictions and targets at corresponding temporal positions but do not explicitly model temporal displacement between misaligned boundary responses. To address this issue, this paper proposes Boundary Measure Alignment (BMA), a training-stage auxiliary objective for temporal action detection. BMA represents predicted and annotated action starts… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084624 - 15 September 2026
Abstract With the rapid growth of the Internet of Things, intrusion detection systems face severe challenges in processing massive, high-dimensional, and redundant network traffic while satisfying strict low-latency and high-efficiency requirements. To address these challenges,this paper improves the original artificial lemming algorithm (ALA) and proposes a dynamic multi-subpopulation artificial lemming algorithm (DMSALA) for feature selection, and then constructs an intrusion detection framework for IoT based on DMSALA. The proposed DMSALA introduces an adaptive clustering-based dynamic multi-subpopulation structure to alleviate premature convergence during the search process. In addition, a cosine-based nonlinear weighting strategy is designed to achieve… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085313 - 15 September 2026
Abstract The operation of complex equipment is typically monitored by multiple sensors, and the vast amount of status data generated from this monitoring provides strong support for predicting the remaining useful life (RUL). Due to the influence of unstable operational conditions, the degradation trajectory of the equipment often exhibits a high degree of nonlinearity. Conventional approaches for processing univariate time series data often struggle to effectively identify inherent degradation trends and unstable fluctuations, while exhibiting limited capability in comprehensive modeling of multi-source time series data. This paper proposes a novel spatiotemporal neural network for RUL prediction.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084547 - 15 September 2026
Abstract The increasing frequency of natural and human-induced disasters has intensified the need for reliable methods to identify crisis-relevant information from Twitter/X streams. However, tweets are often short, noisy, informal, ambiguous, and context-dependent, making disaster-related tweet classification challenging. This study proposes a lightweight attention-enhanced hybrid deep learning framework for binary disaster-related tweet classification. The framework integrates CNN-based local feature extraction, recurrent contextual modeling, static pre-trained word embeddings, class weighting, training-only data augmentation, and learned neural attention. Two architectures, CNN–LSTM–Attention and CNN–BiGRU–Attention, are evaluated on the labeled Kaggle Disaster Tweets dataset using a leakage-aware protocol in which More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083690 - 15 September 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems, 2nd Edition)
Abstract The Internet of Things (IoT) networks in smart cities experience high-dimensional, time-dependent traffic types, and the detection of attacks is difficult in a timely fashion, particularly in the case of imbalanced classes of attacks. Two hybrid deep learning-based intrusion detection frameworks, TimeSpaceNet and ContextFusionNet, are proposed for IoT intrusion detection: TimeSpaceNet, a CNN-LSTM model enhanced with spatial-temporal normalization, and ContextFusionNet, a CNN-BiLSTM model strengthened with contextual fusion attention. For both models, class imbalance is addressed with SMOTE, and training convergence is assisted by the ADOPT optimizer. All the models are tested on the IoT Bot… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085661 - 15 September 2026
Abstract Deep reinforcement learning (DRL) has gained significant attention as an essential technology for constructing intelligent agents capable of handling high-dimensional visual observations in complex control environments. With the rapid development of knowledge transfer paradigms, reincarnating reinforcement learning (RRL) has emerged as a promising approach to accelerate policy convergence and alleviate the inefficiency of traditional tabula rasa training by reusing pre-trained teacher policies. However, existing RRL approaches primarily focus on improving knowledge transfer efficiency, while how student networks adaptively regulate and selectively utilize inherited representations during the teacher–student transition remains underexplored. As a result, student agents… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084385 - 15 September 2026
（This article belongs to the Special Issue: Super-Resolution for Remote Sensing, Medicine and Intelligent Vision Systems)
Abstract Lightweight real-time image denoising is crucial for resource-constrained edge devices, yet existing compact convolutional neural networks (CNNs) often lose high-frequency details due to limited capacity and the absence of explicit frequency-domain supervision. This paper proposes a 0.18M-parameter dual-branch denoising network driven by a novel Frequency Domain Perception Loss (FDPL). The architecture decouples noise removal and detail recovery via a low-frequency branch composed of four Residual-in-Residual Dense Blocks (RRDB) and a high-frequency branch with two Residual Channel Attention Blocks (RCAB). The composite loss combines brightness-aware Mean Square Error (MSE), Visual Geometry Group 19-layer (VGG19) perceptual loss,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084480 - 15 September 2026
Abstract Large Language Models (LLMs) currently lack the robust command and decision-making (C&D) capabilities essential for the command and control domain. To address this critical gap, this paper proposes an emergence mechanism that integrates a domain-specialized Chain of Thought (CoT) framework with a Process Reward Model (PRM)-inspired evaluation and inference-time optimization paradigm. We construct a novel Chain of Command and Decision (CoCD) framework, a C2-specific CoT structure with contextual persistence, knowledge accumulation, and a human-in-the-loop feedback loop, and define a four-dimensional PRM-inspired evaluation framework for process-level assessment of C&D reasoning. Experimental evaluations on 40 C&D scenarios… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082491 - 15 September 2026
Abstract As artificial intelligence becomes increasingly embedded in financial systems, ensuring the security and robustness of these models is critical, particularly in sensitive tasks like credit card fraud detection. Despite their predictive success, deep learning models remain vulnerable to adversarial examples: subtly manipulated inputs that can mislead classification outcomes. Unlike existing approaches that typically rely on either adversarial training or standalone input filtering, this paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions. Using a real-world, imbalanced… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087350 - 15 September 2026
Abstract The Spherical Asymmetric Traveling Salesman Problem (SATSP), characterized by spherical geometry and direction-dependent travel costs, is a challenging combinatorial optimization problem, particularly in large-dimensional scenarios. Although the recently proposed Crested Porcupine Optimizer (CPO) has shown promising performance in continuous optimization, its applicability to discrete asymmetric routing problems remains largely unexplored. To address this limitation, we propose a Discrete Crested Porcupine Optimizer (DCPO), which integrates a discrete solution representation with dual crossover operators, namely order crossover and partially mapped crossover, as well as a multi-strategy mutation mechanism including inversion mutation and swap mutation. A 2-opt local… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085516 - 15 September 2026
（This article belongs to the Special Issue: Applied Cryptography and Privacy-Enhancing Technologies for Secure Digital Infrastructures)
Abstract To address the issues of multi-neuron architectures, high parameter redundancy, and complex hardware implementation in existing memristive Hopfield neural networks (MHNN) for image encryption, a simple structure dual-neuron memristive Hopfield neural network (DNMHNN) modulated by multifrequency square waves is proposed in this study. The proposed DNMHNN model consists of only two neurons and one memristor, and by introducing dual-frequency square-wave external excitation into the memristor, the dynamical behavior of the DNMHNN model can be flexibly regulated. The simulation results verify that the proposed DNMHNN model can generate stable chaotic behavior over a wide parameter range.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083509 - 15 September 2026
（This article belongs to the Special Issue: Advanced Machine Learning for Natural Language Processing: Methods and Applications)
Abstract Objective, interpretable decision support for Post-Traumatic Stress Disorder (PTSD) screening remains a challenge in computational psychiatry, where existing methods either rely on costly neuroimaging or lack the diagnostic transparency required for clinical accountability. This study presents Multimodal HetGAT-PTSD, a heterogeneous graph attention network (HetGAT) that integrates unstructured clinical narratives with structured item-level responses from the PTSD Checklist for DSM-5 (PCL-5). The model operates under a graph topology constrained by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria to ensure structural alignment between clinical theory and graph-based learning. For each patient, a 25-node directed… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084993 - 15 September 2026
（This article belongs to the Special Issue: Deep Learning for Next-Generation Cybersecurity: Architectures, Robustness and Applications)
Abstract Internet of Things (IoT) intrusion detection systems face increasing pressure from adversarial attacks that can manipulate not only feature vectors but also the relational structure on which graph based models rely. This paper proposes Trust Filtered GraphSAGE (TF-SAGE), a graph based intrusion detection system (IDS) pipeline in which edges are assigned trust scores, filtered before message passing, and coupled with uncertainty aware inference to reduce overconfident decisions under unstable neighborhoods. The model is evaluated on NF-ToN-IoT-v2 as the main benchmark and CICIIoT2025 as an independent confirmation benchmark under the same FSAA and GSAA evaluation protocol.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085854 - 15 September 2026
Abstract Small object detection is a common and challenging task in transmission line inspection scenarios. Existing one-stage and two-stage object detection methods in this scenario are still constrained by extremely small object scale and limited feature representation capability, resulting in suboptimal performance in small object detection. To address these issues, this paper proposes a Cost-Sensitive Transformer-based Network for small object detection. First, a Transformer-based backbone is designed, coupled with a neck that integrates Feature Pyramid and Path Aggregation structures, enabling enhanced multi-scale feature extraction and fusion. Second, a hybrid-domain attention-based decoupled detection head is proposed, where… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087927 - 15 September 2026
Abstract Wireless sensor networks (WSNs) and Internet of Things (IoT) systems require lightweight cryptographic primitives that provide strong security under strict constraints on area, latency, and timing predictability. Ascon, selected by National Institute of Standards and Technology (NIST) as the standard for lightweight authenticated encryption, is well suited for such environments, yet application-oriented hardware implementations for WSN platforms remain underexplored. This paper presents a comprehensive evaluation of field programmable gate array (FPGA)-based Ascon architectures for secure WSN and IoT deployments, using iterative design with single permutation round and hybrid design with two-round unrolling across two FPGA… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086702 - 15 September 2026
Abstract Existing fake news detection methods largely rely on single-source datasets, leading models to overfit platform-specific features and perform poorly on heterogeneous multi-source data. Even with the emergence of Large Language Models (LLMs), our benchmarks show that general-purpose LLMs still struggle to identify deceptive intent when source-specific context is unavailable. To address unseen-source-group generalization, we propose SHIELD (Social Heterogeneous Interaction Embedding for Latent Deception). SHIELD models interaction patterns shared across sources rather than relying only on isolated text features or semantic inference. Specifically, we construct a Social Reaction-Aware Heterogeneous Interaction Graph to capture consistencies and discrepancies… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087209 - 15 September 2026
Abstract Biomedical texts naturally contain multiple biological and medical concepts within a document, resulting in a semantically rich and complex structure. Consequently, multi-label text classification (MLTC) has become a suitable framework for comprehensively modeling biomedical texts, including clinical reports, laboratory records, and scientific abstracts. However, relying solely on contextual language representations may be insufficient to explicitly reflect the broader scientific focus and conceptual orientation of a document. In this study, the MLTC problem in the biomedical domain is investigated using the Hallmarks of Cancer (HoC) dataset. Topic probability distributions obtained from CombinedTM are incorporated as an… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085806 - 15 September 2026
（This article belongs to the Special Issue: Applied Cryptography and Privacy-Enhancing Technologies for Secure Digital Infrastructures)
Abstract The rapid growth of the Internet of Things (IoT) and edge computing has increased the demand for secure and lightweight image encryption suitable for resource-constrained environments. This paper proposes a hybrid framework combining a residual-based pretrained autoencoder with a novel Cylinder XOR-Cascade (CXC) encryption scheme. The autoencoder compresses images into a compact latent representation while a residual branch preserves fine spatial details for accurate reconstruction. Both representations are encrypted using CXC, a two-pass column-wise stream cipher that enhances confusion and diffusion through sequential SHA3-256-based chaining and a cylinder-like feedback mechanism. Experiments on the USC-SIPI dataset More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086887 - 15 September 2026
Abstract OAuth authorization ecosystems contain a large and diverse collection of capabilities distributed across multiple cloud platforms. Although previous studies have investigated OAuth security, privacy, and authorization management, the structural organization of authorization capabilities across providers has received limited attention. This study presents a cross-provider structural analysis of OAuth capabilities from seven major authorization platforms. A unified capability dictionary is constructed by normalizing publicly documented OAuth scopes into a common semantic representation. The normalized capabilities are transformed into an undirected semantic topology in which nodes represent capabilities and edges represent deterministic semantic relationships. Standard network analysis… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087310 - 15 September 2026
Abstract Knowledge graphs organize real-world facts as structured triples and have become a fundamental resource for search engines, question answering, recommender systems, and knowledge-enhanced large language models. However, real-world knowledge graphs remain highly incomplete, which limits their downstream reasoning ability. Existing pre-trained language model-based knowledge graph completion methods provide strong textual semantic representations, but they usually model graph structure only as shallow auxiliary features and remain weak in distinguishing structurally similar entities and topology-near negative samples. To address this limitation, this paper proposes a Dual-Level Structural Context Collaborative Framework (DSC2F) for knowledge graph completion. At the instance… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085032 - 15 September 2026
Abstract Log parsing is a fundamental prerequisite for automated system monitoring, anomaly detection, and root cause analysis in large-scale distributed environments. However, existing parsing approaches often rely on heuristic rules, manually engineered features, or fixed similarity thresholds, limiting their adaptability to heterogeneous and evolving log structures. To address these challenges, this study presents SemBERT, a fully unsupervised log parsing framework that integrates semantic BERT embeddings, Incremental Principal Component Analysis (IPCA), HDBSCAN clustering, and adaptive centroid-based cluster merging for robust template mining. Unlike conventional methods that employ fixed merging criteria, SemBERT adaptively determines semantic merging thresholds according… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085768 - 15 September 2026
Abstract In multimodal recommendation, sparse implicit feedback can lead to noisy collaborative graphs, while long-tail and cold-start items often lack reliable collaborative signals. Textual and visual features provide useful item-side information, but they may also be incomplete or inconsistent across modalities. These issues make robust user and item representation learning difficult. To address them, we propose Diffusion Denoising and Generative Enhancement for Multimodal Recommendation (DDGEM). DDGEM first applies node-wise diffusion denoising in the latent collaborative space to reduce unreliable user–item signals. It then uses relational diffusion to reconstruct adaptive item–item relations instead of relying on fixed More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088083 - 15 September 2026
Abstract On-chain risky address identification is an important task in blockchain security analysis and digital asset risk management. In practical on-chain risk control, however, risky addresses are usually far fewer than benign ones. Fund flows also follow complex propagation paths and strict temporal orders, which makes it difficult for existing methods to handle class imbalance, structural semantic modeling, and information leakage under temporal split settings at the same time. To address these challenges, this paper proposes AMLHunter, an on-chain risky address identification method based on temporally consistent transaction semantic constraints and generative graph augmentation. AMLHunter first… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086257 - 15 September 2026
Abstract The increasing trend of phishing sites is among the important threats against the Internet security, associated with monetary loss, data leakage, and identity swindle. In response to this urgent problem, this paper proposes a new phishing website detection framework based on the Modified Differential Evolution (mDE) algorithm in conjunction with state-of-the-art machine learning classifiers. The proposed mDE integrates with dynamic mutation and crossover strategies to improve the global search capability and the convergence speed, which is superior to traditional single optimization methods. We conduct experiments on two benchmark datasets: the UCI Phishing Websites dataset and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086183 - 15 September 2026
Abstract Federated Variational Autoencoders (VAEs) keep interaction data local, but existing federated VAE recommenders typically apply uniform KL regularization and do not adapt dimension-wise penalties to unreliable posteriors in sparse interaction scenarios. We propose -FedVAE, which uses a detached, clipped normalized signal-to-noise ratio as a local confidence gate for each KL dimension of a fused user posterior, without extra communication. Across MovieLens-100K, MovieLens-1M, and Amazon Video, -FedVAE improves mean HR@20 by 7.8%–55.3% and NDCG@20 by 8.0%–65.8% over FedDAE. These results indicate that -FedVAE improves personalized recommendation under sparse and decentralized settings while preserving the communication More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088952 - 15 September 2026
（This article belongs to the Special Issue: Dynamics, Control and Optimization in Complex Networks)
Abstract Complex networks in cyber–physical, transportation, and information infrastructures operate under topology variations, unmeasured disturbances, and limited actuation. This paper proposes robust disturbance-aware data-driven decision control (R-D3C), which couples a sliding-window graph-regularized estimator, disturbance-envelope adaptation, sparse intervention allocation, receding-horizon optimization, and a robust safety projection. The theory directly bounds the dynamic prediction regret of the implemented sliding-window estimator. A checkable sufficient condition for safety-filter feasibility is coupled with an explicit slack-and-backup fallback when the strict projection is infeasible. Practical input-to-state stability and sparse-allocation risk reduction are established. The nominal comparison uses 30 paired runs with standard More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085191 - 15 September 2026
Abstract Multipath load balancing can effectively improve network throughput and reliability by aggregating the available bandwidth of multiple paths. However, existing load balancing schemes including Equal-Cost Multi-Path forwarding (ECMP), Weighted-Cost Multi-Path forwarding (WCMP) or LetFlow may lead to significant performance degradation due to hash conflicts and only target fixed symmetric topologies (e.g., Fattree). Flowlet switching has been proven to be a fine-grained load balancing technique, but remains elusive for widespread deployment. The emergence of network programmability including the control plane and data plane provides a new insight for the management of multipath load balancing. To achieve… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085757 - 15 September 2026
Abstract Aiming at the problem that factory stamping parts have various types of defects, random locations, different sizes, and both known and unknown defects, it is difficult for traditional single inspection methods to achieve both accurate classification and generalized identification capabilities. To this end, the Hybrid Unsupervised Learning-Supervised Learning Fusion Defect Detection (HUSLFDD) model is proposed. The model adopts a dual-branch shared backbone network architecture, in which the supervised learning branch focuses on the accurate classification of known defects, and the unsupervised learning branch realizes feature capture and identification of unknown defects. The weighted fusion of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085975 - 15 September 2026
Abstract The increasing demand for sustainable and energy-aware agricultural practices due to climate change, limited natural resources, and increasing food requirements has accelerated the adoption of Internet of Things (IoT) and machine learning (ML) technologies in smart farming. This study proposes a solar-powered IoT and ML-based framework for energy-aware crop suitability prediction in precision agriculture. The system employs low-power IoT sensors to continuously monitor important environmental and soil parameters, including temperature, humidity, soil moisture, soil temperature, heat index, nitrogen (N), phosphorus (P), and potassium (K) levels. To reduce dependence on conventional energy sources, the framework is… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086743 - 15 September 2026
Abstract With the sustained expansion of complex Internet of Things (IoT) ecosystems, malicious traffic detection has become critical for maintaining both cyber security and operational continuity. Modern IoT deployments contain heterogeneous devices, ubiquitous sensing layers, edge services, and autonomous assets, so abnormal communication may affect not only data confidentiality but also physical operations. To address the limitations of independent flow-level detection and heavy graph propagation, this paper proposes a Lightweight Graph-Attentive Network for Traffic Detection (LGNT). LGNT constructs a directed traffic-interaction graph from NetFlow records, where communication entities are represented as nodes and traffic sessions are… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088018 - 15 September 2026
Abstract Mobile edge services require deterministic communication across Time-Sensitive Networking (TSN) and 5G access, where the standardized integration architecture exposes the 5G System (5GS) to the TSN controller as a logical bridge. We study dynamic admission of time-triggered (TT) flows using reported 5GS bridge delay and TSN-to-5GS Quality of Service (QoS) mapping in route selection and Gate Control List (GCL) scheduling. Arrivals and departures can split available transmission time into noncontiguous windows. Online insertion preserves admitted schedules but may reduce subsequent schedulability, whereas full recomputation can restore schedulability but changes many routes and GCL entries, complicating… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084063 - 15 September 2026
Abstract The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084782 - 15 September 2026
Abstract Micro-expression recognition (MER) is a challenging task because micro-expressions are extremely short in duration, weak in intensity, and often distributed over subtle local facial regions. Existing methods either rely on handcrafted descriptors with limited representation capacity or focus on single-stream deep models that do not fully exploit structural facial dependency and cross-modal complementarity. As a result, they often fail to effectively capture subtle local muscle activations, model structured facial interactions, and robustly integrate complementary motion and appearance cues, especially under the weak-motion conditions characteristic of micro-expressions. To address these limitations, this paper proposes a Dual-Stream… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087636 - 15 September 2026
Abstract Cross-view geo-localization (CVGL) estimates the location of a street-level image by retrieving its matching GPS-tagged satellite tile. Semi-supervised methods reduce the need for dense annotations by mining pseudo labels, but most of them keep only one positive reference for each query. In real-world galleries, several overlapping satellite tiles may cover the same ground location. As a result, valid matches can be discarded as negatives, which gives the model conflicting supervision. To address this problem, we propose DMP-Geo, a semi-supervised cross-view geo-localization method that mines multiple positives for each query from unlabeled data. A bird’s-eye fusion More >
Open Access
CORRECTION
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.090428 - 15 September 2026
Abstract This article has no abstract. More >
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