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.88, No.3, 2026, DOI:10.32604/cmc.2026.084503 - 23 July 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice, 2nd Edition)
Abstract The unique topological properties of the electronic band structures in topological materials have increasingly attracted attention in both fundamental research and next-generation technological applications. With the rise of machine learning, the connection between topological materials and machine learning has deepened significantly. This review systematically summarizes the interaction between these two fields, tracing the history of their mutual promotion and synergistic development. We further examine the transformative impact of machine learning across multiple domains of topological materials, with a particular focus on recent progress in inverse design and generation of topological materials, topological superconductivity, and the More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081804 - 23 July 2026
（This article belongs to the Special Issue: Machine Learning for Robotics: Algorithms, Applications, and Emerging Trends)
Abstract The integration of Deep Learning, Deep Reinforcement Learning, and massive Vision-Language-Action (VLA) foundation models has catalysed a profound paradigm shift in robotics, transitioning systems from rigid automation to dynamic, open-world autonomy. Despite transformative breakthroughs in fields such as healthcare, ranging from adaptive robotic rehabilitation to autonomous surgical manipulation and silver care, widespread real-world deployment remains severely bottlenecked. This limitation primarily stems from the “Reality Gap” inherent to sim-to-real transfer and a fundamental epistemological tension: the stochastic, “black-box” nature of unconstrained neural networks fundamentally conflicts with the deterministic, zero-violation safety guarantees demanded by physical robotics. To… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082757 - 23 July 2026
Abstract Deep reinforcement learning (DRL) has become an important method in Unmanned Aerial Vehicle(UAV) path planning, but the field still lacks a dedicated bibliometric review that summarizes its publication patterns, intellectual structure, and thematic evolution. This study analyzes 1402 Web of Science publications from 2010 to 2025 using CiteSpace, VOSviewer, and the Bibliometrix R package. Three main findings are reported. First, the bibliometric evidence suggests a four-phase evolution of the field—foundational exploration (2015–2016), continuous-control breakthrough (2017–2019), multi-agent collaborative coordination (2020–2022), and complex-scenario integration (2023–2025)—as reflected in publication trends, keyword bursts, and co-citation clusters. Second, co-citation and keyword More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081005 - 23 July 2026
Abstract Standard retrieval-augmented generation (RAG) can perform poorly in AI for IT Operations (AIOps) settings because it is topology-blind. Basic RAG retrieves isolated, flat text snippets without enforcing structural or causal constraints, causing large language models to generate explanations that contradict the running system’s actual dependency structure. To address this gap, we conducted a systematic review following PRISMA 2020, searching Scopus, IEEE Xplore, Web of Science, and Google Scholar (last searched 31 January 2026). We included empirical or systems-oriented studies applying graph-based retrieval to ground a generative model in an IT, cloud, or software-operations setting, and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081251 - 23 July 2026
Abstract Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation, viewpoint inconsistency, translation drift, and temporal misalignment. Existing contrastive skeleton learning methods primarily address action recognition and rarely integrate explicit geometric canonicalization for retrieval-oriented metric learning. This paper proposes a spatial-temporal normalized contrastive embedding framework that unifies structured nuisance suppression with scalable similarity representation learning. A four-stage normalization pipeline—torso-scale normalization, pelvis-centered alignment, posture-axis alignment, and phase-synchronized temporal resampling—removes geometric and temporal distortions prior to embedding. The normalized sequences are encoded using an acausal dilated temporal convolutional network trained with a hybrid More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081304 - 23 July 2026
Abstract Transformers have become the dominant architecture for sequence modeling in natural language processing; however, their effectiveness critically depends on how positional information is encoded. Conventional positional encodings, while effective, may have limited structural flexibility for capturing complex global sequence relationships. Recent quantum-inspired approaches have sought to address this limitation, yet many either oversimplify quantum principles or introduce substantial computational or hardware overhead. We introduce a novel Quantum Fourier Transform (QFT)-inspired positional encoding scheme for transformers, motivated by the structured frequency representation of the QFT. Unlike prior approaches that either emulate quantum operations superficially or require… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081658 - 23 July 2026
Abstract Multi-modal 3D object detection, which leverages the complementary strengths of LiDAR point clouds and camera RGB images, has emerged as a critical component of 3D perception in autonomous driving. As a critical challenge in multi-modal learning, modality alignment aims to establish accurate semantic correspondences across distinct modalities. However, existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured, such as in the presence of object occlusion or adverse environmental conditions, including illumination variations and inclement weather. To alleviate this issue, we present CG-MAE, a dual-branch Bird’s-Eye-View (BEV) masked autoencoder… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082060 - 23 July 2026
Abstract In modern high-performance chip design, achieving timing closure is essential to design success. With the increasing scale and complexity of modern chips, timing-driven placement has become increasingly important. Traditional placement methods primarily focus on minimizing wirelength, but lack timing optimization, making it difficult to meet the strict timing closure requirements of modern designs. Therefore, developing an efficient timing-driven placement method has become a critical challenge in modern chip design. This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder (VGAE) with a nonlinear mixed-size placement optimizer. The framework identifies timing-violation paths More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.085409 - 23 July 2026
（This article belongs to the Special Issue: Advanced Privacy Computing for Intelligent Distributed Networks and Systems)
Abstract Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU methods are designed for the FedAvg paradigm, where all clients share a single global model. In practice, personalized federated learning (pFL) methods such as FedPer, FedRep, Ditto, and FedBN have become widely adopted due to their superior handling of non-IID data. These methods decompose the model into shared global layers and client-specific personalized layers, fundamentally altering the semantics of unlearning, yet this setting has… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083401 - 23 July 2026
Abstract Due to its low hardware cost and ease of deployment, WiFi fingerprinting has become a prominent research direction in indoor positioning. However, traditional methods based on Received Signal Strength (RSS) still face three critical challenges: susceptibility to noise interference, low retrieval efficiency as fingerprint databases scale up, and trajectory instability in dynamic environments. These challenges are inherently rooted in the stochastic fluctuation of RSS signals, the high-dimensional and non-Euclidean nature of fingerprint space, and the unpredictability of user movement patterns. To address these limitations, an adaptive trajectory-assisted dynamic indoor positioning algorithm based on RSS fingerprinting,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084571 - 23 July 2026
Abstract Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083797 - 23 July 2026
Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081876 - 23 July 2026
Abstract In doped two-dimensional nanomaterials, magnetism is one of the important physical properties. By introducing foreign doping atoms or molecules, the electronic structure of the material can be effectively regulated, leading to changes in magnetic behavior. Currently, magnetic property prediction has achieved considerable results with the help of traditional CNNs, but there are still obvious limitations: (1) The feature extraction of dopant sites is constrained by fixed receptive fields, making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions; (2) CNNs lack the capability… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083127 - 23 July 2026
Abstract Zinc substitution in Cd1−xZnxTe (CZT) alloys emerges as a powerful strategy for engineering their structural, elastic, mechanical, acoustic and thermal properties, thereby enhancing their potential for high-performance optoelectronic and radiation detection applications. In this work, a comprehensive first-principles investigation based on Density Functional Theory, within both the Generalized Gradient Approximation and the Local Density Approximation, is conducted to systematically explore the composition-dependent behavior of CZT across the full concentration range (0 ≤
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083128 - 23 July 2026
Abstract The ability to achieve sufficient grasping force while maintaining conformal contact with objects is highly attractive for bio-inspired flexible robotic hands and grippers. In this paper, a flexible robotic hand design is developed inspired by the human hand, where the fingers have an embedded rigid phalanx wrapped in soft silicone rubber materials. A rigid-soft numerical model is developed to investigate the static contact behavior of a fingertip with a rigid flat using finite element (FE) analysis. The Ogden constitutive model is adopted to characterize the hyper-elastic behavior of the silicone rubber material and its parameters… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083975 - 23 July 2026
（This article belongs to the Special Issue: Mechanical Behavior of Materials with Advanced Modeling and Characterization)
Abstract In this study, the effects of temperature and corrosion product porosity on the micro-galvanic corrosion behavior of the β-Li phase in Mg-8Li alloy are systematically investigated using COMSOL Multiphysics numerical simulations. A two-dimensional micro-galvanic corrosion model incorporating mass transport, electrochemical reactions, and level set-based interface tracking is established to simulate the corrosion evolution over 72 h under varying temperature and porosity levels. The results indicate that temperature can significantly accelerate the corrosion process and the exchange current density increases exponentially. As the temperature increases from 35°C to 55°C, the electrolyte potential shifts negatively, and the maximum… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082720 - 23 July 2026
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081556 - 23 July 2026
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications, 2nd Edition)
Abstract To address the issues of low exploration efficiency and “geometric myopia” caused by the lack of high-level environmental structure modeling for mobile robots in complex indoor environments, this paper proposes an active SLAM object navigation method based on Situational Semantic Augmented Graph (SSAG). Unlike methods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association, this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions. First, an online room segmentation algorithm is employed to transform unstructured sensory data into a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080758 - 23 July 2026
（This article belongs to the Special Issue: Advancing Action Recognition: Privacy, Explainability, and Optimization)
Abstract Video representation learning faces very challenging goals, including spurious temporal correlations, confounding visual features, and failure to learn real causal relationships between video events. Current transformer-based approaches learn statistical relationships rather than causal interactions, leading to weak generalization and high sensitivity to distribution changes. The current paper proposes a new Counterfactual Transformer Network, named CauFormer-V, that combines causal inference concepts with temporal representation learning for video. The framework was proposed and includes three main innovations, (1) a Causal Temporal Attention (CTA) mechanism, a mechanism that specifically models causal dependencies among video frames via do-calculus intervention,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.072275 - 23 July 2026
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract Website Fingerprinting (WF) has emerged as a promising technique for identifying user access patterns to Hidden Services (HS). Despite growing interest in WF for HS, the absence of well-established foundations and systematic guidelines for feature selection undercuts the robustness of WF techniques in the face of concept drift. To address this gap, we present an empirical study focusing on feature resilience under concept drift in WF for HS. Specifically, we categorize features into network-specific and network-agnostic groups and quantify their information leakage potential via mutual information. We further assess each feature’s resilience to concept drift More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.074236 - 23 July 2026
Abstract Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand–Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)–based demand forecasting module with a mixed-integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082481 - 23 July 2026
Abstract Open source software has become a fundamental component of modern software ecosystems, supporting a wide range of critical applications in operating systems, cloud services, embedded systems, and security-sensitive infrastructures. However, the rapid growth of open source projects also brings increasingly serious security challenges. Many widely used C/C++ components still contain hidden vulnerabilities, and attackers are no longer limited to exploiting traditional memory-related bugs such as buffer overflows or use-after-free errors. In recent years, non-memory logic flaws, including improper authentication, incorrect state transitions, flawed boundary checks, and insecure API usage, have become more prevalent and more… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026
Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081818 - 23 July 2026
Abstract Tomato, as a globally important crop, its freshness directly affects postharvest quality, market value, and consumer acceptance. Traditional tomato freshness evaluation mainly relies on manual inspection and experience-based judgment, which is time-consuming, labor-intensive, and inefficient. Meanwhile, plasma technology has shown promising potential in agricultural preservation due to its safety and effectiveness, making the evaluation of tomato freshness after plasma treatment particularly important. In recent years, with the rapid development of deep learning technology, non-destructive detection methods based on image analysis have become important tools for agricultural product quality assessment. This study proposes an improved YOLOv8n-based… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079237 - 23 July 2026
Abstract Medical texts are often complex and difficult to understand for non-specialists, creating barriers to effective communication in the clinical and rehabilitation fields. Although recent advances in natural language processing (NLP) have enabled automated text simplification, existing approaches often struggle to maintain medical accuracy and frequently result in factual inconsistencies or distortions. To address these issues, we propose the Neuro-Semantic Clinical Filter (NSCF), a novel NLP-based framework designed for clinically accurate simplification of medical texts. The proposed method integrates a Medical Concept Graph Encoder (MCGE) to incorporate structured domain knowledge, a Neuro-Symbolic Transformer (NSTR) for supervised… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081308 - 23 July 2026
Abstract The effectiveness of profiling deep learning side-channel attacks relies on the assumption that training and attack data follow the same distribution. However, when the profiling device differs from the target device, process-voltage-temperature (PVT) variations and clock jitter countermeasures cause distribution shifts in power traces, rendering models trained on the source device ineffective on the target. Existing domain adaptation methods typically rely on a single distributional constraint without jointly constraining kernel mean embeddings and covariance structure, thus limiting their effectiveness against strong defenses such as clock jitter. We propose Robust Feature Alignment for Side-Channel Analysis (RFA-SCA),… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081631 - 23 July 2026
Abstract Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Over-sampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080058 - 23 July 2026
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA (Low noise amplifier) in 22 nm FDSOI technology using NSGA-II and MOPSO algorithms. The objectives of the paper include simultaneous minimization of noise figure (NF) and power consumption while maximizing gain under matching and stability constraints. Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology, an optimization framework was created in Python, with the passive components
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026
Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083042 - 23 July 2026
Abstract While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084179 - 23 July 2026
Abstract Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms, which often suffer from premature convergence and poor recall in sparse, complex API mapping spaces. To address this, we propose QIMIG, a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering. QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima. Simultaneously, its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings. Evaluated on 9 real-world migration rules derived from 57,447 open-source projects, QIMIG statistically significantly outperforms state-of-the-art baselines More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083100 - 23 July 2026
Abstract To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization, this paper develops a dynamic parallel machine scheduling model that accounts for stochastic machine failures and order priorities, thereby more accurately reflecting the uncertainties and complexities of real-world production environments. A dual-objective optimization framework is adopted to minimize both the makespan (maximum task completion time) and the variance of task completion times, aiming to improve the coordination and reliability of intra-order task delivery. An adaptive weighted reward function is designed to balance overall scheduling efficiency with consistency… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082593 - 23 July 2026
Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082658 - 23 July 2026
Abstract With the rapid development of the Internet of Things (IoT) and edge intelligence, the volume of data generated by edge devices has grown explosively. Federated learning (FL), characterized by the paradigm of “data remaining local while models are shared,” has emerged as a key approach for adapting to the distributed architecture of edge computing, breaking down data silos, and enabling privacy preservation. However, its practical deployment in edge computing environments still faces significant challenges, including limited device resources and pronounced data heterogeneity. Existing pruning strategies for federated learning are predominantly based on static and single-design… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082310 - 23 July 2026
（This article belongs to the Special Issue: GenAI/AI in Biometric Recognition: Theoretical Foundations, Applications, and Emerging Challenges)
Abstract The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare. It enables more adaptive and intelligent human–machine interactions. Epilepsy, a common neurological disorder affecting millions worldwide, relies heavily on electroencephalography (EEG) signals for diagnosis and monitoring. Wearable consumer devices with EEG sensors support continuous physiological data collection. However, transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks. Federated learning (FL) provides a distributed training framework that keeps raw data on local devices. Despite this advantage, existing FL methods remain vulnerable to gradient leakage attacks, where adversaries may infer More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081906 - 23 July 2026
Abstract We perform a cross-layer penetration testing on one of the most popular Wi-Fi smart locks (Tuya 902V). The methodology combines wireless traffic analysis using an Alfa AWUS036AXML adapter, forced re-association via deauthentication to make Wi-Fi Protected Access 2 (WPA2) 4-way Extensible Authentication Protocol over LAN (EAPOL) handshake visible with Airodump/Aireplay, offline dictionary attack with Aircrack-ng, Android app reverse engineering using Apktool, Jadx, and MobSF; denial-of-service experiment (DoS) executed by hping3; Near-Field Communications (NFC)/Radio-Frequency Identification (RFID) key-clone attempt by Flipper Zero. Handshake is empirically captured but no Wi-Fi passphrase found under 14M dictionary entries; DoS test… More >
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079984 - 23 July 2026
Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026
（This article belongs to the Special Issue: Dynamics, Control and Optimization in Complex Networks)
Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >
Open Access
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082629 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Next-Generation Cybersecurity: Architectures, Robustness and Applications)
Abstract In recent years, the transferability of adversarial examples has attracted significant attention. To improve the effectiveness of black-box attacks, a frequency-domain decay constraint is introduced, inspired by weight decay and regularization techniques commonly employed during model training. By treating adversarial perturbations as inputs in an optimization process, this constraint aims to mitigate the excessive reliance on low-frequency components during adversarial example generation, thereby enhancing transferability. Fourier heatmaps are utilized to analyze the sensitivity of input samples, enabling a decomposition of the frequency spectrum into low-frequency and high-frequency components. Based on this analysis, low-frequency attenuation is More >
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CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081651 - 23 July 2026
Abstract The ongoing expansion of the Internet of Things (IoT) fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks. Nonetheless, vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption, a dilemma severely intensified by impending quantum computing capabilities. Defending these networks demands the integration of post-quantum cryptographic primitives; yet, the severe hardware constraints characterizing peripheral IoT components complicate practical deployment. Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations, largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance. Consequently,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082618 - 23 July 2026
Abstract Automated road damage detection is a critical component of intelligent transportation systems, enabling efficient infrastructure maintenance and improved traffic safety. However, existing approaches often suffer from limited contextual understanding, insufficient segmentation accuracy, and suboptimal real-time performance. This study presents TCR-RoadNet, a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments. The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement (TCR) module to capture both fine-grained structural details and long-range spatial dependencies across feature scales. To further enhance performance, a Decoupled Detection Head (DDH)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080119 - 23 July 2026
Abstract Background: Jailbreak attacks, which use crafted prompts to bypass safety alignments of Large Language Models (LLMs) and generate harmful content, pose a significant security threat. Existing methods often optimize for a single objective (e.g., attack success rate), neglecting critical factors like query efficiency, which limits their practicality and generalization. Methods: We propose a Componentized Multi-Objective Optimization Framework (CMOOF), which introduces a paradigm shift: it searches for generalizable and query-efficient attack strategy templates within a structured, component-based strategy space. CMOOF leverages the NSGA-II algorithm to explicitly co-optimize two first-class objectives: Attack Success Rate (ASR) and Query More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082622 - 23 July 2026
Abstract In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082998 - 23 July 2026
Abstract Many fast pattern-matching mechanisms are used in NIDS (Network Intrusion Detection Systems) to filter higher volumes of network traffic prior to invoking expensive rule verification stages. This filtering phase in signature-based engines, such as Snort, needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware. Here, we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm (WEMA). WEMA performs the most relevant matching based on deterministic ordered indexing of category units, which eliminates chaotic control flow (which occurs with automata… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026
Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083337 - 23 July 2026
Abstract Context-aware driving assistance must do more than detect objects: it has to identify the cues that materially affect risk, separate observable evidence from inference, and produce recommendations that humans can audit. This paper presents a grounded multi-agent multimodal large language model (MLLM) framework for interpretable risk assessment in driving scenes. The framework decomposes reasoning into four stages—context relevance evaluation, visual interpretation, factual verification with anomaly extraction, and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description. We evaluate the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081260 - 23 July 2026
Abstract Large Language Models (LLMs) have become a cornerstone of modern natural language processing, achieving strong performance across diverse tasks. Despite these advances, their tendency to generate hallucinated or factually unsupported content remains a critical challenge for reliable deployment. Existing evaluation approaches predominantly rely on single-task settings and aggregate performance metrics, implicitly assuming that hallucination behavior is uniform across tasks. However, this assumption is fundamentally flawed, as hallucination characteristics vary significantly depending on task formulation, linguistic context, and evaluation criteria. To address these limitations, this paper proposes HalluBench, a task-aware multi-LLM benchmarking framework designed for systematic… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081336 - 23 July 2026
Abstract Given a multi-turn conversational context and a raw user query, the goal of Conversational Query Reformulation (CQR) is to transform the query into a de-contextualized form that maximizes retrieval effectiveness for a downstream passage retriever. Conversational search seeks to retrieve relevant passages for the given questions in a conversational question answering system. Conversational Query Reformulation (CQR) improves conversational search by refining the original queries into de-contextualized forms to address issues such as omissions and coreferences. Previous CQR methods focus on imitating human-written queries, which may not always yield meaningful search results for the retriever. In… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081752 - 23 July 2026
Abstract With the evolution of information technology toward more advanced intelligence and automation, Security Orchestration, Automation, and Response (SOAR) has become a critical foundation for security incident handling, owing to its intelligent orchestration capabilities. Security playbooks, as the core mechanism for automated response in SOAR, require well-designed workflows and precise action matching to ensure efficient and accurate alert handling. However, with the rising sophistication of attacks and the expanding scale of security alerts, traditional expert-driven playbook recommendation approaches often degrade in recommendation quality or completely fail when existing playbook repositories cannot adequately cover unknown or novel… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082430 - 23 July 2026
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies, 2nd Edition)
Abstract In the intelligent inspection of power systems, the detection of equipment defects is confronted with problems such as low background discrimination, multi-scale morphological differences, and the difficulty in identifying small targets and fine-grained defects, which makes it hard for existing models to balance detection accuracy and computational efficiency. To address this, this study proposes an improved lightweight detection framework, GRID-YOLO. This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module (C2MSA), designs an enhanced multi-scale bidirectional feature pyramid network (EMFPN) to achieve… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084474 - 23 July 2026
Abstract Addressing the two critical challenges of missed detection of distant small targets and difficulty in identifying occluded targets under complex road conditions, this paper proposes YOLO-PBE, an improved high-precision vehicle detection model based on YOLOv11n. First, to tackle the fine-grained feature loss caused by conventional strided convolutions during downsampling, we add a high-resolution P2 detection layer and introduce SPD-Conv, a lossless spatial-to-depth feature transformation technique, for feature extraction. By preserving complete pixel-level information, the model's perception accuracy for distant small vehicles is enhanced. For feature fusion, we design an improved BiFPN incorporating a Ghost module.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082586 - 23 July 2026
Abstract In the expanding Internet of Things (IoT) ecosystem, billions of interconnected devices exchange sensitive data, making secure and usable authentication critical. IoT devices in public or shared environments are vulnerable to shoulder-surfing and video recorded observation attacks. Traditional passwords and static graphical schemes remain susceptible due to predictable patterns and direct credential entry. This study presents a novel recognition-based graphical authentication scheme that combines pass-image selection with compass direction substitution and rotation logic to resist observation-based attacks. A prototype was evaluated with 58 participants over three days. Usability metrics included registration time, login time, success… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081437 - 23 July 2026
Abstract Robotic systems require reliable tactile perception to evaluate object stiffness during physical interaction. This study proposes a lightweight dual-branch architecture, named Hybrid-CNN-ResVgg, designed to improve hardness recognition using data from a low-cost piezoresistive tactile sensor. The model combines a one-dimensional convolutional neural network (1D-CNN) based on a ResNet8-Lite architecture for learning temporal signal patterns and a two-dimensional convolutional neural network (2D-CNN) based on a VGG6-Lite architecture for learning spatial representations derived from Gramian Angular Difference Fields (GADF). A cross-architecture fusion mechanism is introduced to integrate temporal and spatial features while reducing redundant representation learning. Experiments… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080593 - 23 July 2026
（This article belongs to the Special Issue: Modern Challenges in Cryptography and Cybersecurity)
Abstract Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication. Its realism has raised serious concerns in different applications such as digital forensics, cybersecurity, media authentication and voice-based security systems. However, deepfake audio detection still remains difficult. Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely. Variations in speakers, recording conditions and background noise make the task more complex. In addition, dataset imbalance and low diversity in training samples could lead to low robustness in the model. To… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083861 - 23 July 2026
Abstract Mashups are among the key web technologies that provide end-users with customizable and personalized tools. Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures; therefore, in this paper, we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks. For the structured part, we rely on the Chord lookup protocol, and for the unstructured part, we build groups of nodes via two flavors of network flooding, namely, sequence number flooding and reverse path flooding. Brokers in the unstructured part would be responsible for More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082976 - 23 July 2026
Abstract Sparse finite impulse response (FIR) filters reduce computational cost on resource-constrained devices, but selecting the sparsification threshold
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082207 - 23 July 2026
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications, 2nd Edition)
Abstract A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things (IoT) environments. Efficient intrusion detection at the network edge is essential for resource-constrained IoT deployments, where devices operate with limited processing, memory, and energy resources, making centralized or computationally intensive solutions impractical in real-world scenarios. Network traffic is represented using statistical and temporal features extracted from unidirectional flows constructed from the TII-SSRC-23 dataset. A balanced subset of 10,000 samples is used for training and evaluation, ensuring balanced data distribution and improving generalization across different traffic conditions. Three… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083124 - 23 July 2026
（This article belongs to the Special Issue: Next-Generation Optimization: Quantum and Hybrid Classical Computing for Real-World Applications)
Abstract Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments, yet its effectiveness remains closely tied to initialization quality. This study formulates network microsegmentation as a state-dependent combinatorial optimization problem in which optimization behavior depends on the availability of structural guidance. A comparative analysis is conducted across four representative optimization paradigms, including genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), and amplitude-ensemble quantum-inspired tabu search (AE-QTS), under both structured and unstructured conditions. Experiments are conducted on a representative brownfield enterprise network using 30 independent runs per configuration. In addition… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084208 - 23 July 2026
Abstract The deployment of supervised anomaly detection is typically limited by the high cost of annotation, privacy constraints, and the scarcity of anomalous samples. These constraints have motivated the use of vision-language pre-trained models for zero-shot anomaly detection. However, existing CLIP-based methods still face three limitations: a shared set of prompts is applied across feature layers, anomaly maps are fused by fixed strategies, and image-level anomaly scores are determined solely by global image-text similarity. These limitations reduce the accuracy of pixel-level localization and weaken the reliability of image-level anomaly prediction. To overcome these limitations, LaRP-CLIP is More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081445 - 23 July 2026
（This article belongs to the Special Issue: Vision, LiDAR, and Sensor Fusion-Based SLAM for Autonomous Navigation)
Abstract Dynamic objects in LiDAR SLAM often introduce ghosting artifacts that degrade map quality. While offline methods can successfully clean these maps, they lack real-time capabilities. Conversely, online methods often suffer from state oscillation (where moving objects are misclassified as static when they temporarily stop) and incomplete point cloud removal. To address these challenges, we propose DGMSE, a real-time framework for removing dynamic point clouds in complex urban environments. Our approach consists of three sequential steps. First, the PointPillars 3D detection network quickly isolates potential dynamic objects, significantly reducing computational overhead. Second, to mitigate state oscillation, More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084409 - 23 July 2026
Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081589 - 23 July 2026
Abstract Implicit Neural Representation (INR) is a technique that models continuous signals using neural networks, replacing traditional discrete grid representations with a coordinate-to-value mapping function. As a data carrier, INR is gradually being adopted as the target for steganographic processing. However, existing INR-based steganographic schemes typically require modifying network structures (e.g., weights, nodes) and retraining to obtain stego INRs, leading to high time consumption and the need for re-training when replacing cover images. To address this issue, this paper proposes StegaMIR (Steganography via Modulated Implicit Representations), an image steganographic scheme based on modulated implicit representations. It… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081004 - 23 July 2026
Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083649 - 23 July 2026
（This article belongs to the Special Issue: Intelligent Control and Sensing for Industrial and Autonomous Applications)
Abstract Industrial predictive maintenance is a critical challenge in modern manufacturing, where unexpected equipment failures cause significant economic losses through downtime, repair costs, and disrupted production. Conventional maintenance approaches, whether reactive or schedule-based, are becoming inadequate to manage the high-dimensional sensor information of the IoT-enabled machineries. The paper presents a novel hybrid neuro-symbolic digital twin that builds upon Remaining Useful Life (RUL) estimation by combining temporal transformers, physics-informed constraints, and counterfactual reasoning. The model integrates complementary approaches into a single and interpretable predictive system. A temporal transformer backbone is a model of long-range dependencies in multivariate… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082357 - 23 July 2026
（This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
Abstract Underwater imagery is degraded by depth-dependent absorption and scattering, which often introduce color casts and contrast attenuation. Although recent Vision Mamba models provide efficient long-range dependency modeling, their conventional 2D scanning patterns are not explicitly designed to exploit the depth-correlated structure of underwater degradation and may therefore weaken geometry-aware feature dependencies. To address this limitation, we propose Isoline-Guided Evolutionary Mamba (IG-Mamba), a physics-inspired framework that uses a depth-correlated potential prior to organize state-space token propagation. Specifically, we introduce a Topology-Preserving Isoline Scanning mechanism. By leveraging a geometric prior, this mechanism quantizes the scene into discrete… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083596 - 23 July 2026
（This article belongs to the Special Issue: Advances in Object Detection and Recognition)
Abstract Indoor object detection presents unique challenges such as occlusions, varying lighting conditions, and cluttered environments. While several object detection frameworks, including RetinaNet, Faster R-CNN, SSD, and EfficientDet, have been proposed, they often suffer from high computational cost, reduced inference speed, and limited accuracy in terms of mean Average Precision (mAP), particularly in real-time scenarios. In this study, lightweight YOLO variants, namely YOLOv7, YOLOv8s, YOLOv9s, and a fine-tuned YOLOv9s which considers the optimized training strategy based on albumentations. All the models are evaluated for indoor object detection using the RGB TUT Indoor dataset. The models are… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082572 - 23 July 2026
（This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)
Abstract Unmanned Aerial Vehicles (UAVs) are finding more and more applications in logistics, surveillance, and other operations at a large scale. However, autonomous navigation in dynamic traffic situations is not an easy task due to limited energy, moving obstacles, and inter-agent interactions. The proposed paper can be discussed as a Generative World Modeling (GWM) framework of risk-focused UAV navigation in the dynamic traffic network. This paper proposes a GWM framework for risk-aware UAV navigation in dynamic traffic networks. The proposed design incorporates three key elements; a generative world model for predicting future environmental conditions, a diffusion-based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083828 - 23 July 2026
（This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)
Abstract Unmanned aerial vehicles (UAVs) are becoming a common solution to urban mobility, and traffic monitoring as well, owing to their ability to be deployed flexibly, ability to see a broader area and real-time sensing. However, the reliability of UAV-assisted traffic systems can be compromised through identity spoofing, Sybil attacks, false data injection, and trajectory manipulation. Current authentication techniques primarily verify cryptographic identities but often cannot detect when a claimed identity is inconsistent with physical movement patterns and settings. To overcome this drawback, this paper presents a context-aware identity validation system, CIV-UAV, for UAV-based urban traffic… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080735 - 23 July 2026
Abstract Polyether ether ketone (PEEK) is a radical filament with excellent strength equivalent to cortical bone and high thermal-mechanical properties. PEEK’s acquisition is acceptable in the fabrication of cranio-maxillofacial implants because of its exceptional strength-to-weight ratio and biocompatibility. However, its implementation in fused filament fabrication (FFF) is impeded by the lack of a cohesive optimisation framework that involves varying vital parameters: layer height, infill density and two post-process parameters: annealing temperature, annealing time, which affect its mechanical performance. This research work introduces a comprehensive methodology that integrates experimental design, hybrid Genetic Algorithm Artificial Neural Network (GA-ANN)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083713 - 23 July 2026
Abstract Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081460 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition. The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition. Specifically, the proposed framework employs an InceptionV3 Convolutional Neural Network (CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance. These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory (LSTM) or Hidden Markov Model (HMM) for temporal modeling. For the speech modality, Mel-Frequency Cepstral Coefficients (MFCC) or filter… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083677 - 23 July 2026
（This article belongs to the Special Issue: Advances in Image Generation: Theories, Architectures, and Applications)
Abstract Transthoracic Echocardiography (TTE) often suffers from a limited field of view (FoV), which may obscure peripheral cardiac structures and hinder comprehensive visual assessment. Although FoV extension can be formulated as outpainting, public studies on echocardiographic FoV outpainting remain scarce and are mainly represented by cGAN-based reconstruction methods such as echoGAN. However, in noisy ultrasound images with weak boundaries, the challenge is not only realistic texture synthesis, but also structural continuity across the observed–generated boundary. To address this issue, we propose a structure-aware diffusion framework for echocardiographic FoV outpainting. To the best of our knowledge, this More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083643 - 23 July 2026
Abstract Large language models have recently demonstrated promising capabilities in mathematical reasoning; however, their performance on tasks requiring strict symbolic manipulation, such as solving differential equations, remains limited, especially for compact models. In this work, we investigate whether activation steering combined with reinforcement learning can improve the quality of solutions generated by pretrained language models without modifying their weights. In particular, we focus on relatively small-scale models, which exhibit limited baseline performance on symbolic mathematical tasks, and study whether their capabilities can be enhanced through activation-level interventions. The proposed approach introduces trainable steering vectors that are… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082979 - 23 July 2026
Abstract Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection, traffic classification, and adaptive Quality-of-Service (QoS) control. This paper proposes NeuroPulse, a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks (SNNs) with the representational power of sparse self-attention, enabling efficient deployment on neuromorphic network processors (NNPs). We propose a Rate-Coded Cross-Attention (RCCA) module, which converts population-coded spike-trains into attention queries, allowing long-range dependency modeling within sub-milliwatt (sub-mW) power budgets. NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083491 - 23 July 2026
Abstract Single-wave order picking in dynamic warehouses is a sequential multi-goal navigation problem. A robot must visit an ordered set of shelves and then a delivery station while avoiding moving obstacles under partial observability. Existing approaches either entangle long-horizon task logic with low-level obstacle avoidance or rely on static-environment assumptions that limit responsiveness in dynamic settings. This paper proposes the Single-Wave Dynamic Warehouse Navigation System (SW-DWNS), a lightweight scheduling framework that extends a pretrained ColorDynamic point-to-point local planner to ordered warehouse picking without retraining. The scheduler maintains a shelf queue, exposes only the active subgoal to… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084301 - 23 July 2026
（This article belongs to the Special Issue: Deep Reinforcement Learning for Space-Air-Ground Integrated Edge Computing: Architectures, Algorithms, and Applications)
Abstract High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084384 - 23 July 2026
Abstract Plants are fundamental to global food security; however, plant diseases significantly reduce agricultural productivity, making early and accurate detection essential. Traditional inspection approaches rely heavily on manual observation, which is labor-intensive, subjective, difficult to scale, and susceptible to human error. In contrast, artificial intelligence (AI) combined with computer vision (CV) offers an effective solution for early-stage disease detection, minimizing yield losses while overcoming the limitations of manual monitoring systems. In this study, a novel deep learning architecture, the Swin Transformer with Harmonic Densely Connected Network (STHarDNet), is proposed. The framework integrates a Swin Transformer (ST)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083365 - 23 July 2026
Abstract Automated wood surface defect detection is difficult to evaluate reliably because defects are often small, low-contrast, and visually confounded by natural wood texture, while reported performance can vary substantially with benchmark design and domain shift. To address this issue, we conduct a comparative study across three practically relevant settings: a curated seven-class benchmark, a broader in-domain seven-class protocol derived from the same source dataset, and supervised adaptation to a low-resource Vietnamese target domain. We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with MobileNetV3-FPN against a compact You Only Look… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082842 - 23 July 2026
Abstract Integrating high-frequency sequential signals with low-frequency contextual descriptors into a unified deep encoder is a recurring challenge in computational modelling, exemplified by cross-sectional stock ranking where price dynamics must be jointly modelled with quarterly accounting fundamentals. Existing approaches use late concatenation, where the contextual signal influences only the final prediction head and cannot shape upstream feature extraction. We propose Feature-wise Linear Modulation (FiLM) as an intermediate conditioning mechanism: fundamentals generate per-channel scaling (gamma) and shifting (beta) parameters that affinely transform the encoder’s intermediate representations before aggregation. The same price sequence thus yields different temporal features… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083923 - 23 July 2026
Abstract Existing knowledge distillation methods for object detection struggle to bridge the teacher-student capacity gap and overlook the inherent differences between classification and regression subtasks. To address these issues, we propose a Bridging Multi-dimensional Gaps Knowledge Distillation (BMGKD) method, which comprises two core modules: a feature difference distillation module and a response difference distillation module. The feature difference distillation module achieves global feature structural alignment via improved centered kernel alignment and performs local key feature alignment using joint spatial and channel-wise cosine similarity masks. The response difference distillation module constructs a dynamic classification mask and a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083412 - 23 July 2026
Abstract Accurate mapping of video imagery to physical space coordinates represents a fundamental challenge in dynamic target tracking and intelligent video analysis systems. Traditional methods struggle to maintain stable coordinate mapping in real-time video streams due to imaging distortion variations and changing environmental conditions. This paper presents a real-time coordinate mapping approach that integrates geometric constraints with online distortion correction to achieve stable pixel-to-target coordinate transformation for video target tracking applications. The proposed method introduces a planar geometric consistency constraint and an online distortion parameter update mechanism within a unified optimization framework, enabling adaptive adjustment of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026
（This article belongs to the Special Issue: Intelligent and Privacy-Preserving Malware Detection: Advances in Deep Learning, Memory Forensics, and Federated Security)
Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084246 - 23 July 2026
Abstract Automatic Number Plate Recognition (ANPR) is widely used in Intelligent Transportation Systems (ITS) and smart parking applications, but running deep learning-based ANPR directly on low-power edge devices remains difficult because of computation time, memory, and latency limitations. In this study, we develop an edge-oriented ANPR pipeline for an Internet of Things (IoT)-based sensor-triggered stop-and-go smart parking platform, targeting deployment on a resource-constrained edge device. The pipeline combines YOLOv8 for license plate detection, PaddleOCR for text recognition, and a rule-based normalization stage to reduce Optical Character Recognition (OCR) errors caused by spacing inconsistencies and plate-format variations.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084555 - 23 July 2026
Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083998 - 23 July 2026
Abstract Modern malware is increasingly employing polymorphism, packing, and metamorphism to evade traditional signature-based detection. Because of this, there is an urgency to have more reliable classification systems. Visual malware analysis, where binaries are converted into grayscale images, has demonstrated potential in revealing structural patterns of malware family classification. However, recent methods mostly rely on single-stream, lightweight Convolutional Neural Networks (CNNs). These models have a major blind spot. The visual representation textures can be heavily obscured without changing the underlying malicious code, causing severe performance drops on newer or even rare malware classes. This paper presents… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080190 - 23 July 2026
（This article belongs to the Special Issue: Large Language Models: Evaluation, Knowledge Integration, and Applications)
Abstract Faced with the surge of massive natural-language content, information retrieval systems must handle increasingly complex queries while filtering noisy information effectively. Although conventional approaches have made notable progress in matching efficiency and general adaptability, they still struggle to precisely model deep semantic associations between query intent and documents in real-world environments. Such limitations can lead to ranking deviations and omission of critical information. Motivated by recent advances in large language models (LLMs) and their capability to capture deep semantics, we propose DPR-FL, a Dual-Path Retrieval method that integrates fusion-based Filtering with structured LLM Feedback. It combines direct retrieval… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080596 - 23 July 2026
（This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)
Abstract The rapid growth of electric vehicle (EV) charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints. Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms, limiting their practical applicability in large-scale deployments. This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector. The edge component suppresses non-informative patterns, while the fog layer performs temporal modeling on selectively forwarded data. This design enables controllable reduction of fog-level processing load. Under corrected… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082577 - 23 July 2026
（This article belongs to the Special Issue: Advanced Security and Privacy in Blockchain Systems)
Abstract Recently, extensive research has focused on addressing the unique challenges of smart contract fuzzing. Nevertheless, existing fuzzers still struggle to generate adequate function call arguments that can explore the deep smart contract states. In this paper, we introduce novel classes of argument constraints that capture the inter-argument relationships required to exercise meaningful contract logic. We propose a static analysis algorithm to extract these constraints from Solidity source code. In addition, we design a constraint-aware argument mutation strategy that leverages the identified constraints to guide test case generation for smart contract fuzzing. We implement our approach More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082928 - 23 July 2026
Abstract The standardization of the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM, FIPS 203) creates urgent demand for efficient post-quantum cryptography on resource-constrained devices. In such deployments, twiddle-factor management in the Number Theoretic Transform (NTT) induces a practical trade-off: full tables reduce latency but consume read-only memory (ROM), while on-the-fly generation reduces ROM but increases arithmetic cost. This paper makes two contributions. First, we present a constant-time half-table strategy (
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081311 - 23 July 2026
（This article belongs to the Special Issue: Secure and Intelligent Intrusion Detection for IoT and Cloud-Integrated Environments)
Abstract The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082406 - 23 July 2026
（This article belongs to the Special Issue: Next-Generation Recommender Systems: Multimodality, Generative Models, and Trustworthy Personalization)
Abstract In the era of information overload, cross-domain recommendations offer a promising solution by leveraging user preferences across domains to improve recommendation accuracy and relevance. This study proposes a novel approach to cross-domain recommendations based on cognitive similarity derived from user-based features. We construct comprehensive user profiles across multiple domains by defining cognitive similarity based on user interaction data, including ratings, reviews, and genre preferences. We employ advanced feature extraction techniques, including TF-IDF for textual data and matrix factorization for latent factors, to quantify similarities in user preferences across domains. These cognitive similarity measures are then More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078696 - 23 July 2026
Abstract Path planning is a critical component for enabling autonomous navigation in mobile robots. Sampling-based planners are widely adopted due to their strong generality, yet they rely heavily on uniform sampling, which often leads to unstable performance and high computational cost in complex environments. To address this issue, recent studies feed free-space point clouds into neural networks to infer a set of guidance states near the optimal path, thereby enabling non-uniform sampling; however, the accuracy of the guidance set becomes a key bottleneck for further improvement. In this paper, we propose an improved point-cloud neural RRT*… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081803 - 23 July 2026
Abstract Cloud-based Business Intelligence (BI) systems operate under highly dynamic analytical workloads, including bursty OLAP queries, concurrent aggregations, and real-time microservice interactions, where static resource allocation leads to latency spikes and inefficient resource utilization. This paper proposes a decentralized adaptive Pareto-based multi-agent decision model for real-time resource coordination in cloud BI microservice environments. The agent placement problem is formulated as a multi-criteria decision process that minimizes service response latency, improves computational resource utilization, and preserves Quality-of-Service (QoS) stability. Instead of constructing a centralized global optimization policy, the proposed framework relies on decentralized locally Pareto-efficient decisions combined More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082460 - 23 July 2026
（This article belongs to the Special Issue: Generative Artificial Intelligence and Large Language Models: Methods, Architectures, and Applications)
Abstract Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets. Existing retrieval-augmented generation (RAG) methods typically retrieve document snippets independently, which can fragment narratives and harm temporal dependencies. We propose ChronoRAG, a retrieval framework for narrative question answering that first converts sequential document chunks into concise relation descriptions and then retrieves relevant units together with their adjacent chronological context. This design preserves retrieval precision while providing the generator with coherent local narrative structure. Experiments on NarrativeQA and GutenQA show that ChronoRAG More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083112 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract Incomplete multimodal sentiment analysis has attracted increasing research interest in recent years. Existing methods attempt to recover missing modalities through generative reconstruction and text-enhanced fusion, but these approaches may be limited in preserving sentiment-relevant information and fully leveraging complementary and hierarchical cross-modal interactions, particularly under noisy or incomplete conditions. To address these challenges, we propose TC-DSC, a text-centric hierarchical dual-stream interaction framework for incomplete multimodal sentiment analysis. Rather than reconstructing raw signals, TC-DSC performs semantic alignment and consistency modeling in the feature space through structured interactions between a text-centric stream and auxiliary audio-visual streams. A More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081449 - 23 July 2026
Abstract Neural operators provide a data-driven framework for learning mappings between function spaces and have shown strong performance in scientific computing and surrogate modeling. Existing architectures, however, typically rely on a single representation of the input function—either purely pointwise, as in DeepONet, or purely spectral, as in Fourier Neural Operators—which limits their ability to simultaneously capture local variability and global structure. In this work, we propose NOASLRR, a neural operator that integrates three complementary branches within a unified DeepONet-style formulation: a pointwise MLP embedding, a spectral branch based on Chebyshev polynomial coefficients, and a low-rank linear… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082353 - 23 July 2026
（This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
Abstract Traditional Mamba-UNet integrations employ four-stage architectures, replacing conventional five-stage UNets with VMamba blocks for global dependency modeling. Unlike Transformers, which suffer from quadratic complexity and high memory consumption in self-attention, Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling. This paper proposes TriLVM-UNet, a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge (SAB) module inspired by UltraLight VM-UNet. The model incorporates a Lightweight Vision Mamba (LVM) layer for high-resolution feature extraction, alongside multi-scale dilated convolution (MSDC) and convolutional block attention module (CBAM) for enhanced More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078029 - 23 July 2026
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications, 2nd Edition)
Abstract Radio Frequency Identification (RFID) has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals. Most existing RFID Network Planning (RNP) methods are primarily based on either heuristic or metaheuristic approaches. While heuristic approaches are computationally efficient and converge rapidly, they often suffer from premature convergence and suboptimal network configurations. Conversely, metaheuristic algorithms provide stronger global search capabilities and improved solution quality, but they typically require higher computational effort and may still exhibit stagnation in local optima when applied to complex hospital layouts. To overcome these limitations while utilizing… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080580 - 23 July 2026
Abstract Industrial surface defect detection requires accurate localization of small and weak-boundary defects under tight runtime constraints for on-line inspection. This paper presents an efficient DETR-style defect detector with three components. First, we build a hybrid feature extractor by coupling a ConvNeXt-T backbone with a lightweight Feature Pyramid Network (FPN) to strengthen multi-scale representations for small and subtle defects, thereby improving detection performance in challenging industrial environments. Second, to address the high computational cost of original DETR, we adopt multi-scale deformable attention to replace the quadratic-cost global self-attention mechanism, substantially improving efficiency. Third, to improve per-class… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081775 - 23 July 2026
Abstract The rapid growth of the Industrial Internet of Things (IIoT) has become a cornerstone of high-quality global economic development. By integrating sensor networks, edge computing, and cloud intelligence, IIoT has emerged as a key enabler for smart manufacturing and digital transformation across industries. However, this technological advancement introduces significant cybersecurity challenges that render traditional intrusion detection systems inadequate for IIoT environments. To address this critical gap, we propose a deep spiking Q-network (DSQN)-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP). The system employs a hierarchical More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084659 - 23 July 2026
（This article belongs to the Special Issue: Advances in Cybersecurity for Digital Ecosystems)
Abstract With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity–based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082713 - 23 July 2026
Abstract Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083306 - 23 July 2026
（This article belongs to the Special Issue: Intelligent IoT for Smart Cities and Sustainable Energy Systems)
Abstract The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082726 - 23 July 2026
Abstract Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) enables infrastructure-free indoor positioning and requires heading-drift compensation associated with gyroscope bias. This paper proposes a stationary-window-based Deep Belief Network (DBN) framework that learns a gyroscope bias representation from z-axis angular-velocity and sampling-interval sequences observed during stationary intervals and applies it to heading correction during walking. The learned representation captures the residual angular-velocity offset under stationary conditions and serves as an adaptive correction term for subsequent heading integration. Experiments on short-term, three-lap long-term, and complex indoor paths show that stationary-window-based DBN bias estimation is particularly effective in accumulated-drift More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.076965 - 23 July 2026
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract Wireless Sensor Networks (WSNs) are important infrastructure for smart-city applications, such as environmental monitoring, public safety, and smart transportation. However, finding effective sensor locations is an NP-hard problem because a deployment must satisfy sensing coverage and communication connectivity while minimizing the number of sensors. Following the basic framework of a previous study, this study replaces the original optimization algorithm with the Amplitude-Ensemble Quantum-inspired Tabu Search (AEQTS) algorithm and retains the same entanglement-like initialization strategy, resulting in the proposed AEQTSwE (AEQTS with Entanglement) framework for the WSN deployment problem. AEQTSwE uses a quantum-inspired search mechanism and More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083976 - 23 July 2026
（This article belongs to the Special Issue: Architectural Innovations and Algorithmic Optimization in Learning Models: From Machine Learning to Swarm Intelligence)
Abstract This study proposes a Multi-Stage Sparrow Search Algorithm (MS-SSA) for precise structural damage identification. Initially, the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula, and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty. Subsequently, MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification. In the localization phase, a constrained narrow-bound search space is predefined to identify potential damage regions. Leveraging this feedback, the sensitivity equations are condensed, and the search boundaries are adaptively refined for the quantification phase, where SSA is reapplied to… More >
Open Access
CORRECTION
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.087222 - 23 July 2026
Abstract This article has no abstract. More >
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