Open Access
ARTICLE
Yuteng Sun, Yang Su*, Xu An Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.089159
Abstract Event knowledge graphs support event-centric question answering by linking events to temporal, location, participant, and source-record information, but semantic relevance alone does not guarantee that a selected event satisfies every represented condition. We propose EviGraphRAG, a ranker-agnostic reliability layer that separates candidate ranking from exact verification and explicit abstention; EviGraphRAG-Struct is the default configuration. On the controlled oracle-normalized 4800-question benchmark, it preserves the unverified ranker’s positive-task outputs while attaining 0.9792 abstention accuracy. A separate 600-question set from machine-assisted drafting with deterministic construction and semantic-consistency checks shows realistic extraction noise: parser-based Macro Score is 0.7317 vs. More >
Open Access
ARTICLE
Mohammad J. M. Zedan1,2, Edwin Rangga Ardhana1, Siti Raihanah Abdani3, Mohd Asyraf Zulkifley1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087996
Abstract The widespread usage of social media has contributed to the global concern of a rapid rise in cyberbullying. Generally, cyberbullying can be defined as intentional and harmful behavior conducted through online communication channels that can cause severe psychological distress, depression, and emotional trauma among victims. Detecting cyberbullying content automatically remains a challenging task because abusive messages are frequently expressed in subtle and ambiguous forms. Consequently, there is an increasing need for intelligent detection systems that are capable of identifying cyberbullying incidents accurately and efficiently. Recent advances in deep learning have demonstrated considerable potential for cyberbullying… More >
Open Access
ARTICLE
Hongzhi Li1,*, Jiale Wu1, Dun Li2,*, Kezhong Lu1, Qishou Xia1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086477
Abstract The application of large language models (LLMs) to computer science (CS) education has great potential for intelligent lesson preparation, yet existing systems remain limited by weak textbook alignment, inconsistent pedagogical structures, and extensive manual post-editing. This paper presents the Fine-Tuned DeepSeek-driven Intelligent Lesson Preparation Assistant System (FT-DILPAS), a textbook-driven lesson preparation framework that integrates efficient model adaptation, structured textbook understanding, curriculum-aware prompting, and retrieval-augmented generation into a unified pipeline. Evaluations on 60 CS textbooks show that FT-DILPAS achieves parsing accuracies of 96.5% for standard layouts and 88.2% for complex layouts, significantly outperforming traditional parsing methods. More >
Open Access
ARTICLE
Bo Wei, Hongfeng Wang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088577
Abstract The Permutation Flow Shop Scheduling Problem (PFSP) is one of the most classic combinatorial optimization problems in manufacturing systems, with wide applications in semiconductor fabrication, textile processing, and steelmaking. The Permutation Flow Shop Scheduling Problem with Sequence-Dependent Setup Times (PFSP-SDST) is more representative of real-world production, where machine changeovers depend on the sequence of jobs. However, SDST destroys the optimal substructure of standard PFSP, substantially increasing solution difficulty. To address this challenge, we propose a meta-reinforcement learning method based on Model-Agnostic Meta-Learning (MAML) and Evolution Strategy (ES). Meta-training is conducted only on standard PFSP instances… More >
Open Access
ARTICLE
Jiao Wang1,*, Tingting Song2,*, Yunhui Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087751
（This article belongs to the Special Issue: AI-Driven Image Processing and Pattern Recognition: Advances in Algorithms, Models, and Applications)
Abstract In practical scenarios, multi-view data often contains missing entries caused by complicated data collection and transmission procedures, posing great challenges to clustering analysis. Existing incomplete multi-view clustering methods have two obvious limitations: (1) Imputation-based methods inevitably generate inaccurate information during data recovery; (2) Imputation-free methods struggle to balance cross-view consistency and complementarity. To tackle the above issues, this paper proposes a deep incomplete multi-view clustering approach based on subspace learning, which integrates latent feature extraction, K-nearest neighbor-based feature imputation, cross-view contrastive alignment, and attention-driven fusion within a unified deep learning framework. Specifically, we first leverage… More >
Open Access
REVIEW
Shuai Zhou1,2,3, Jun Wu3,4, Li Li2,5, Rong Tang1,2, Zhangjun Peng1,2, Mingfei Wan1,2,3, Zhiqiang Chen5, Zhigui Liu1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087447
Abstract Crop pests pose a major threat to agricultural production and hinder the sustainable development of agriculture. Accurate pest identification is a prerequisite for implementing sustainable pest control strategies and enabling precise and efficient pest management. This review examines the technological evolution of intelligent crop pest identification and analyzes the advantages and limitations of existing methods. First, it analyzes three types of crop pest data collection methods, with emphasis on sampling bias, existing bottlenecks such as long-tail distribution and domain shift, and the characteristics of typical datasets. Second, it discusses the application scenarios and limitations of… More >
Open Access
ARTICLE
Di Yao1, Yuling Chen1,*, Xiuzhang Yang1,*, Xuewei Wang2, Haiwei Sang3, Weijie Tan1,4, Zhi Ouyang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086755
Abstract In recent years, WebShell attacks have exploited web application scripting languages to enable persistent remote control, unauthorized command execution, and flexible post-exploitation operations, making them a widely adopted technique in phishing campaigns, advanced persistent threat (APT) operations, and Internet of Things (IoT) network environments. Existing WebShell detection methods mainly focus on benign–malicious binary classification, while fine-grained malicious behavior analysis remains insufficient. Moreover, heterogeneous scripting languages, code obfuscation, syntactic mutation, and dynamic function invocation further limit the robustness of traditional lexical, syntactic, or structural detection approaches. To address these challenges, we propose MVFN-FG, a novel WebShell… More >
Open Access
ARTICLE
Muhammad Hanzla1, Bayan Alabdullah2, Mohammad Shorfuzzaman3,*, Mohammed Alonazi4, Jasem Almotiri5, Ahmad Jalal1,6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086599
（This article belongs to the Special Issue: Edge AI and Intelligent Systems in IoT)
Abstract The increasing adoption of wearable Internet of Things (IoT) devices and wireless sensor networks has accelerated the demand for intelligent, low-latency human activity recognition and localization systems. However, dynamic environments, sensor noise, motion variability, and communication delays continue to hinder reliable real-time sensing. To address these challenges, this study presents an Edge-AI enabled multi-agent intelligence framework for human activity recognition and mobility localization within a 6G-enabled IoT edge-cloud continuum. The proposed framework employs Self-supervised Transformer-based Adaptive Sensor Encoding and learning-aware temporal windowing to generate robust representations from heterogeneous sensing modalities. Extracted locomotion and localization features More >
Open Access
ARTICLE
Lili Zhang, Jinming Cheng*, Yingyou Wen
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086285
Abstract Adverse weather conditions substantially degrade the performance of light detection and ranging (LiDAR) semantic segmentation because weather-induced interference often intensifies point-cloud noise, weakens the representation of long-range objects, and causes the loss of local structural details. These degradations make it difficult for segmentation models to maintain reliable perception across regions with different levels of uncertainty and complexity. To address these challenges, this paper proposes a reliability-guided Mamba–Transformer hybrid framework for adverse-weather LiDAR semantic segmentation. The proposed method first evaluates the reliability and difficulty of adverse-weather point clouds and constructs region-level priors to guide subsequent feature… More >
Open Access
ARTICLE
Venkata Mohit Tamanampudi1, Zaid Bin Faheem2, Jehad Ali3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086208
Abstract Intrusion Detection Systems (IDS) are an important tool for network security, however, machine learning based IDS and single-based models often fail to be beneficial in the context of high-dimensional data, redundant features, class distributions, and flexibility when network attack patterns change. To address these drawbacks, the present paper proposes a novel ensemble learning framework called CAF-Net (Context-Aware Fusion Network), which combines bagging, stacking, an attention-driven meta-learning and a confidence-aware soft voting mechanism based on the XGBoost ensemble learning approach. Although CAF-Net uses the same base learners as traditional ensemble methods, it differs in that it… More >
Open Access
ARTICLE
Menwa Alshammeri1,*, Khalid Haseeb2, Mamoona Humayun3, Mona Saleh Alzahrani1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085737
（This article belongs to the Special Issue: AI-Driven Optimization for Secure and Sustainable Edge IoT Services)
Abstract Advances in emerging technologies, such as the Internet of Things (IoT) and artificial intelligence, provide a wide range of real-time applications to the development of smart cities. The physical objects, along with IoT systems, not only provide seamless connectivity to the remote environment but also continuously gather the required data through a sensor-based network. They provide timely information to connected devices, meet their needs, and enhance the flexibility of automated IoT-driven systems. Despite improvements in network maintenance and communication robustness, most existing healthcare systems still encounter challenges in addressing congestion and scalability as data traffic… More >
Open Access
ARTICLE
Taha Bachir Ammour1,*, Mohammed Kaddi1, Mohammed Omari2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085627
Abstract Wireless Sensor Networks (WSNs) are the backbone of modern Internet of Things (IoT) deployments, but they share a critical bottleneck: limited battery life. Because of this, picking the right Cluster Heads (CHs) in an energy-aware way is absolutely essential for keeping the network alive. To tackle this challenge, this paper introduces the Longevity-Aware Crayfish Optimization (LACO) algorithm. It is a targeted upgrade to the standard Crayfish Optimization Algorithm (COA) that bakes explicit energy awareness directly into the clustering process. At its core, LACO relies on a three-layer Energy-State Adaptive Control (ESAC) mechanism. First, it dynamically… More >
Open Access
ARTICLE
Feng-Cheng Lin*, Jia-Yan Lin, Chun-Yu Hung, Wijaya Wijaya
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085098
Abstract Road defects, such as cracks and potholes, pose significant challenges to urban infrastructure management and transportation safety. Existing automated detection methods often suffer from computational inefficiency, sensitivity to environmental conditions, and a lack of severity assessment capabilities. Addressing these limitations, this study presents an integrated deep learning framework optimized for embedded platforms, designed to provide comprehensive, real-time road defect detection, classification, and tracking. The proposed framework utilizes the proposed FAST-UNet for efficient segmentation, an enhanced YOLOv11_SDIDC for robust detection, MobileNetV3 with SimAM attention for multi-level severity classification, and ByteTrack for stable tracking, optimized with a More >
Open Access
ARTICLE
Md. Nahian Suhaimee1, Farhan Shakil2, Md. Rifat Al Amin Khan3, Md. Omar Faruq4, Md. Jakir Hossen5,*, M. F. Mridha6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083705
Abstract Insider threats in Intelligent Transportation Systems (ITS) pose significant risks to operational safety and service continuity, as malicious actions often originate from users with legitimate access and evade traditional signature-based detection methods. This study proposes a temporal and explainable machine learning framework that models activity as sequential patterns and provides interpretable insights for each detection decision. The proposed approach integrates recurrent neural networks and attention-based encoders to capture short- and long-term temporal dependencies. To enhance interpretability, a hybrid explanation module combines temporal attention, SHapley Additive exPlanations (SHAP), and counterfactual analysis to identify influential time steps,… More >
Open Access
ARTICLE
Saadaldeen Rashid Ahmed1,2,3, Fatima Abu Siryeh3, Mohammed Shamar Yadkar3, Oguz Bayat4, Abu Saleh Musa Miah5, Fahmid Al Farid6,7,*, Hezerul Abdul Karim7,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082782
（This article belongs to the Special Issue: Privacy-Preserving AI: Encryption and Differential Privacy)
Abstract Privacy-preserving federated learning (FL) has emerged as an effective paradigm for collaborative model training across distributed data sources while maintaining data confidentiality. However, protecting sensitive information during model aggregation remains a significant challenge in distributed deep learning environments. This paper introduces GuardML, a secure federated learning framework that integrates a Recurrent Neural Network (RNN) with Hybrid Homomorphic Encryption (HHE) to enable privacy-preserving learning on high-dimensional distributed datasets. In the proposed system, client nodes perform local model training using encrypted data representations, ensuring that raw data remains protected during the learning process. Encrypted model updates are… More >
Open Access
ARTICLE
Bader Alharbi1, Mohammed Balfaqih2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088106
Abstract The rapid growth of e-commerce and smart logistics has increased the need for secure, transparent, and tamper-resistant shipment monitoring systems, particularly for high-value, fragile, and sensitive goods. Traditional logistics platforms mainly provide location-based tracking and often lack trusted evidence about the physical condition of shipments when tampering, abnormal handling, product removal, or route violations occur. Although Internet of Things (IoT) systems can capture real-world shipment conditions and blockchain can provide immutable records, these capabilities are commonly implemented separately, leaving a gap between physical shipment monitoring and trusted on-chain verification. To address this gap, this paper… More >
Open Access
ARTICLE
Rajani Navoda Gunawardhana*, Thareendhra Keerthi Wijayasiriwardhane
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084610
Abstract In modern education, learning Science, Technology, Engineering, and Mathematics (STEM) has become an essential requirement. Physical computing, embedded systems, and Internet of Things (IoT) are widely used in STEM education, and the Arduino platform has emerged as a de facto standard for teaching and learning these technologies. However, with recent advancements in Artificial Intelligence (AI), students are tempted to over-rely on Large Language Models (LLMs). As a result, the auto-generation of software source codes by students using LLMs poses a significant threat to achieving the Intended Learning Outcomes (ILOs) of their course of study. Unlike conventional… More >
Open Access
ARTICLE
Abdullah Alharbi*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.089052
（This article belongs to the Special Issue: Deep Reinforcement Learning for Space-Air-Ground Integrated Edge Computing: Architectures, Algorithms, and Applications)
Abstract Space-Air-Ground Integrated Networks (SAGIN) are networks that combine the use of satellite, High-Altitude Platform Systems (HAPS), Unmanned Aerial Vehicles (UAVs), terrestrial base stations, and Internet of Things (IoT) devices to enable ubiquitous networking and distributed edge intelligence. Efficient semantic edge computing is, however, challenging due to resource heterogeneity, as well as the dynamism of the topology and the evolution of semantic information. This study proposes a novel Hierarchical Graph-Constrained Federated Multi-Agent Deep Reinforcement Learning (HGCF-MARL) framework for trustworthy semantic knowledge orchestration in SAGIN. The framework unifies the placement of semantic knowledge bases, task offloading, communication-computation… More >
Open Access
ARTICLE
Lijie Wang, Xiaoming Du*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088359
Abstract SiCp/Al particle-reinforced aluminum matrix composites exhibit significant strain-rate sensitivity under high-speed impact and dynamic forming conditions, and their mechanical response is further affected by the volume fraction and particle size of the reinforcement phase. To describe these coupled effects, this study developed a hybrid constitutive modeling framework combining microstructure-based finite element simulation, the Johnson–Cook constitutive model, and machine learning. First, three-dimensional microstructural models of SiCp/7075Al composites were established using DIGIMAT FE and ABAQUS, and dynamic compression simulations were conducted under different strain rates, SiC volume fractions, and particle sizes. The simulated stress–strain curves were processed… More >
Open Access
ARTICLE
Jialin Li*, Yihong Liu, Yang Yang, Shirong Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088293
Abstract Compared to traditional machine learning and signal processing techniques, deep learning has achieved remarkable results in industrial fault diagnosis. However, the difficulty of obtaining sufficient labeled fault acoustic data in real industrial environments has led to increased interest in few-shot learning methods. Existing methods are typically constrained by their reliance on convolutional architectures that mainly emphasize local patterns, limiting their ability to capture task-specific relationships across classes. To address this challenge, a novel meta-learning method with an improved relation-distribution module is proposed for industrial acoustic signals under small-sample data conditions. The framework integrates a hierarchical… More >
Open Access
ARTICLE
Zengqi Ma, Wendong Zhao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088063
Abstract In large-scale monitoring scenarios with multiple unmanned aerial vehicles (UAVs) and offloading nodes, concurrent offloading of collected data to the same node within a short time window will cause excessive node load, prolonged processing delay, and degraded information timeliness. To address this issue, this paper considers the impact of users’ personalized demands on information timeliness and introduces the concept of Value of Information (VoI). A user demand-oriented dynamic VoI model is established to accurately describe and evaluate information timeliness. The multi-UAV offloading node selection problem is formulated as a semi-Markov decision process (SMDP), and a… More >
Open Access
ARTICLE
Yiying Zhang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087316
Abstract Backtracking search algorithm (BSA) is a very simple and efficient metaheuristic algorithm. However, BSA only relies on the random crossover vectors generated between the historical population and the current population to guide the search direction of the population, lacking purposefulness. In view of this, this paper proposes an improved backtracking search algorithm (IBSA) that designs three search strategies by creating two dynamic elite populations. Compared to BSA, IBSA has stronger purposefulness in the search process. To validate the effectiveness of the proposed algorithm, 30 challenging test functions and four complex constrained engineering design problems are More >
Open Access
ARTICLE
Yao Pu, Rou Zhou, Yuling Chen*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086553
Abstract Personalized federated learning (PFL) aims to address the insufficient adaptability of a single global model caused by Non-IID data. It improves client-specific performance by learning customized models for different clients. However, existing PFL methods based on similarity modeling often struggle to balance personalization performance and communication efficiency under Non-IID settings. To address this problem, this paper proposes FedCANA (Communication-Efficient Adaptive Neighborhood Aggregation), a communication-efficient personalized federated learning method for Non-IID data. First, FedCANA adopts Residual-Spatial Attention Collaborative (RSAC) module to strengthen the clients’ local representation ability, which provides a more stable model basis for subsequent… More >
Open Access
REVIEW
Hue T. Tran1,2, Nguyen Nhu Son3, Anh Nguyen Trong4, Anh Nguyen Tu5, Phat T. Nguyen6,*, Giang L. Nguyen3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086547
Abstract Over the past decade, rapid advances in artificial intelligence have substantially expanded applications in medical image analysis, computer-aided diagnosis, and clinical decision support. Among object detection architectures, the YOLO (You Only Look Once) family has emerged as one of the most widely adopted approaches due to its real-time inference capability. However, transferring YOLO from general computer vision to clinical settings still faces major challenges, including small lesion detection and the gap between algorithmic performance and real-world clinical utility. This topical review provides a structured narrative synthesis of YOLO applications in medical imaging from 2016 to… More >
Open Access
REVIEW
Essam H. Houssein1,*, Ibrahim E. Ibrahim2, Yaser M. Wazery3, Nagwan Abdel Samee4, Marwa M. Emam1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086392
Abstract Wireless Sensor Networks (WSNs) remain constrained by limited battery capacity, which restricts their operational lifetime in Internet of Things (IoT) applications. We present a PRISMA 2020-guided systematic review of 89 primary studies published during 2016–2026, focusing on metaheuristic, hybrid, and Artificial Intelligence (AI)-based methods for energy-efficient clustering and routing. A five-database search strategy yielded 876 records, followed by duplicate removal, title/abstract screening, full-text eligibility assessment, and structured quality appraisal. The synthesis indicates that Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) remain the most frequently studied individual algorithms, each appearing in 23.6% of the… More >
Open Access
ARTICLE
Fatimah Alhayan1, Muhammad Hanzla2, Hadeel Alsolai1, Bayan Alabdullah1, Ahmad Jalal2,3,*, Hui Liu4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086339
Abstract Human activity recognition (HAR) from wearable inertial sensors underpins IoT-based smart-health monitoring, yet practical deployments are constrained by noisy Inertial Measurement Unit (IMU) signals, inter-subject variability, temporal misalignment across sensor streams, and the limited compute available on edge nodes. Existing pipelines typically optimize recognition accuracy in isolation from these deployment constraints. This paper addresses both aspects through a distributed edge–cloud HAR framework. At the edge, IMU signals are denoised using Tukey filtering and segmented using Planck-Taper windowing before compact features are transmitted, reducing uplink overhead. At the cloud, an attention-based deep multiple-kernel learning (A-DMKL) adaptively… More >
Open Access
ARTICLE
Juhui Zhang, Zongyi Xing*, Chenxiao Cai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085491
Abstract As urban rail transit systems continue to expand, greater technical demands are placed on the reliability assessment and temperature monitoring of key bogie components under dynamic operating conditions. To address the complexity of state detection caused by motion blur in high-speed targets, this study proposes a motor temperature detection system based on dynamic infrared video analysis. The system evaluates motor operating conditions by rapidly screening motor images and applying super-resolution reconstruction techniques. First, the system’s configuration and operating principles are described. Next, an infrared image screening algorithm is developed based on temperature feature extraction and… More >
Open Access
ARTICLE
Ahmed Mazin Jalal1, Muhammad Asshad2, Amjed A. Ahmed3, Nidal A. Al-Dmour4, Mohammad Ahmed Alomari5,*, AbdulGuddoos S. A. Gaid6, Omar Almomani7, Taher M. Ghazal7,8,9,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087414
Abstract The Internet of Vehicles (IoV) is moving towards sixth-generation (6G) communication technologies to enable intelligent transportation to transmit data at ultra-low latency and high speed. Effective vehicle navigation and environmental sensing are key to providing instantaneous decision-making in dynamic driving conditions. This paper introduces a Forward Federated Learning (FFL)-based navigation aid system for 6G-enabled IoVs to provide precise sensing and navigation assistance amid changing environmental conditions. The suggested framework collects environmental data, such as neighboring vehicles, road signs, collision distance, and road conditions, using distributed sensing devices. Perceived data is used to aid navigation and… More >
Open Access
ARTICLE
Antonio Candelieri1,*, Francesco Archetti2, Iman Seyedi2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086040
Abstract This paper addresses black-box optimization over multiple information sources whose fidelity and query cost change across the search space. We propose an approach that uses: (i) an Augmented Gaussian Process as a single model of the objective function over the search space and sources, and (ii) a Gaussian Process to model the location-dependent cost of each source. The former is used in a Confidence Bound-based acquisition function to select the next source and location to query, while the latter is used to penalize the value of the acquisition depending on the expected query cost for… More >
Open Access
ARTICLE
Xiangmin Liu1,2, Weizhi Xiong1,*, Wei Zhang1, Taojian Luo1, Xiu Yao1, Jian Hu1, Peng Peng2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087484
Abstract Spectral clustering (SC) effectively captures non-convex and graph-structured cluster patterns, but its application to large-scale attributed and heterogeneous graphs is hindered by the high costs of similarity graph construction and Laplacian eigendecomposition, as well as by insufficient use of relation semantics. To address these challenges, we propose Relation-aware Adaptive Anchor Spectral Clustering (RAASC). RAASC first identifies relation-aware local components in relation-specific subgraphs and allocates anchor budgets according to component difficulty. It then constructs an adaptive, load-aware sample-anchor graph by jointly considering node uncertainty, relation compatibility, and anchor-load balance. Using the resulting sparse sample-anchor matrices, RAASC… More >
Open Access
REVIEW
Quang-Vinh Dang1, Dat Le2,*, Minh Ngoc Dinh3, Ngoc-Son-An Nguyen4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087848
（This article belongs to the Special Issue: Privacy-Preserving AI: Encryption and Differential Privacy)
Abstract Deploying machine learning on sensitive data has made privacy a first-order design constraint. Over the past five years privacy-preserving machine learning (PPML) has matured from isolated proofs of concept into an ecosystem of cryptographic and statistical techniques, each occupying a distinct point in the trade-off space among confidentiality, integrity, utility, cost and trust. This review surveys that ecosystem for a research audience. We examine homomorphic encryption, including several currently available bootstrapping styles and encrypted transformer inference; differential privacy in deep learning, analytics and the private fine-tuning of large language models; privacy budget management through composition… More >
Open Access
ARTICLE
Linh Nguyen Thi My1,2,*, Nhat Trinh Ngoc Minh2, Tham Vo1, Vinh Truong Hoang3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087689
Abstract Automated pill identification is a safety-critical task whose hardest cases involve visually near-identical medications that differ only in their printed or debossed imprint code. The widely used ePillID benchmark explicitly identified the reliable reading of such imprints as the most important open problem, but left it unsolved because the optical character recognition available at the time was not dependable on small, low-contrast pill surfaces. We revisit this problem with modern vision–language models (VLMs) and propose MIRA-Pill (Multimodal Imprint Reading and Confidence-Gated Re-Ranking), a framework that couples (i) a visual metric-learning branch (ResNet50 with Compact Bilinear… More >
Open Access
ARTICLE
Yanjun Li1,2, Yiping Lin2,*, Yuting Ni2, Lixian Zhang2, Shanshan Huo1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087189
Abstract This paper presents an exact security evaluation of a closed modulo- implementation variant of the lightweight Add–Rotate–XOR (ARX) block cipher named MBRISI. We first resolve an arithmetic ambiguity in the original specification: modulo-65,537 addition on 16-bit words may produce the unrepresentable value 65,536, while representing its outputs by 16-bit overflow is non-injective. We construct distinct plaintext pairs that merge after the first round and consequently produce identical ciphertexts. In contrast, modulo- addition is closed and bijective over the 16-bit word space; therefore, all subsequent weak-key and equivalent-key results apply exclusively to this implementation variant.… More >
Open Access
ARTICLE
Hua-Yu Zhu, Weijie Mao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086986
Abstract Achieving secure and energy-efficient connectivity-maintaining consensus is a quintessential challenge in multi-agent systems (MAS), given the complex coupling between global connectivity constraints, power limitations, and adversarial attacks. This paper introduces a resilient closed-loop framework that synergizes topology maintenance and consensus control across two time-scales, incorporating real-time fault detection and post-attack autonomous survival protocols. To reconstruct secure and energy-efficient topologies, we develop a dimensionality-adaptive reinforcement learning (RL) scheme, which adaptively deploys pre-trained RL topology reconstruction policies tailored to the spatial dimensions of the current operating environment. To obtain these multi-dimensional policies, we propose an efficient topology More >
Open Access
ARTICLE
Yu Tong, Kaina Xiong, Jun Liu*, Xinyue Fan, Guixing Cao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086756
Abstract Small-object detection in aerial remote-sensing imagery is constrained by the cascaded low-pass behaviour induced by consecutive spatial down-sampling, which progressively attenuates the high-frequency cues of weak targets. Recent detection transformers, whose self-attention operators behave—under commonly used temperature and rank regimes—as adaptive spatial low-pass filters, tend to inherit rather than mitigate this spectral bias, while existing remedies are typically obtained at the cost of considerable parameter and floating-point overhead. To bridge this gap, this paper proposes a Frequency-Aware Lightweight Network for Small-Object Detection (FLD-RTDETR), built upon the Real-Time Detection Transformer (RT-DETR), which augments this baseline along… More >
Open Access
ARTICLE
Dongmin Zhang1,#, Chao Sun1,#, Yikun Zhang2,*, Runyao Yin2, Chen Chen1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086570
Abstract Multi-view tabular data poses challenges due to heterogeneous feature spaces, divergent distributions, and implicit cross-view relationships. Traditional methods further struggle because their hand-crafted fusion strategies cannot adequately capture complex nonlinear interactions among views. To address this issue, this paper proposes a genetic programming (GP)-based multi-view feature fusion method. It employs a multi-tree GP framework: each view is assigned a dedicated tree for intra-view feature selection and construction, and a fusion tree combines their outputs for cross-view feature-level fusion. An enhanced feature construction strategy further enriches the final representation by exploiting subtree information. Together with decision-level More >
Open Access
ARTICLE
You Yang1,2, Bo Chen1,2,*, Weiqi Liu3, Zekai Ma1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086073
Abstract This paper proposes a non-contact intelligent method using machine vision and super-resolution (SR) reconstruction. The method uses artificial targets, a high-order degradation model, and camera-based acquisition for displacement calculation. Building upon the (Real-Enhanced Super-Resolution Generative Adversarial Networks) Real-ESRGAN framework, this paper introduces a feature fusion attention mechanism to improve the Real-ESRGAN network and generator, enabling the reconstruction of image contours and fine details to enhance displacement calculation accuracy. Quantitative laboratory validation on a benchmark shake-table dataset demonstrates improved displacement-monitoring accuracy, while hydraulic-structure imagery is used only to demonstrate reconstruction performance. The results indicate potential applicability More >
Open Access
ARTICLE
Zihao Jia1, Jingrui Zhang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085939
Abstract Accurate partial discharge (PD) pattern recognition is essential for assessing power cable insulation, yet it remains challenging due to complex discharge features and limited data availability in practical engineering. To address these issues, this paper first establishes a high-voltage experimental platform with four carefully designed physical defect models to construct a highly authentic Phase-Resolved Partial Discharge (PRPD) dataset. Subsequently, an improved Res-Swin fusion framework is proposed. It integrates a ResNet branch to decouple fine-grained local discharge textures and a Swin Transformer branch to model global phase-amplitude topologies. To alleviate overfitting in data-scarce scenarios, a multi-stage… More >
Open Access
REVIEW
Jiaqi Mi1, Congcong Ma2, Xinrui Li1, Sixu Huang1, Kunpeng He1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085901
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems, 2nd Edition)
Abstract In underwater environments where global navigation satellite systems are unavailable, autonomous underwater vehicles and other underwater vehicles require autonomous navigation methods, among which matching navigation based on seafloor topography, gravity anomalies, and geomagnetic anomalies can effectively mitigate the cumulative errors of inertial navigation systems. To exploit the complementary characteristics of different geophysical fields, multi-source data fusion has become a key technique for improving the robustness and accuracy of underwater matching navigation. This review systematically examines recent advances in underwater geophysical-field data fusion. First, existing methods are categorized into three levels: map-level, feature-level, and decision-level fusion,… More >
Open Access
ARTICLE
Makhkamov Bakhtiyor Shukhratovich1, Akmalbek Abdusalomov1,2,3,4, Akhmedova Nodira Aminjanovna1, Botirov Sokhibjon Rustam Ugli1, Jasur Sevinov2,5, Alpamis Kutlimuratov6, Boburjon Vafoev7, Yodgorkhon Ilkhamova7, Young Im Cho8,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.085065
Abstract This paper presents field-programmable gate array (FPGA)-based engineering optimizations of the Ascon lightweight cryptographic algorithm, targeting real-time edge AI and embedded systems. The work systematically evaluates how established design strategies, such as selective round unrolling and pipelined permutation stages, scale across heterogeneous FPGA platforms. Two architectures are explored, each optimized for different design objectives, including throughput (TP), latency, and hardware efficiency. The designs are implemented on two FPGA families, Xilinx Kintex UltraScale and the 7-Series Spartan, to evaluate scalability across both high-performance and resource-constrained platforms. Experimental results show that selective round unrolling in Architecture 2 More >
Open Access
ARTICLE
Josh Dean1, Yu-Zheng Lin1, John Paul Martin Encinas1, Ibrahim Almazyad1, Qinxuan Shi2, Zhanglong Yang2, Shalaka Satam1, Tingjun Lei2, Jielun Zhang2, Sicong Shao2,*, Salim Hariri1, Pratik Satam1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084915
（This article belongs to the Special Issue: Advances in Intrusion Detection and Prevention Systems)
Abstract Rapid technological change is transforming our society with the emergence of new fields such as Autonomous Vehicles and Smart Manufacturing, posing new research questions and challenges in system design, operations, security, and training. Researchers rely on testbeds to create experimental scenarios to solve these research challenges. These testbeds aid in data collection, analysis, and observation of the research problem, and in measuring the efficacy of the proposed solution. However, the rapid pace of modern innovation makes it challenging and cost-prohibitive for these testbeds to represent state-of-the-art scenarios, which require expensive upgrades and the addition of… More >
Open Access
ARTICLE
Zarnab Kausar1, Shaheryar Najam2, Hadeel Alsolai3, Bayan Alabdullah3, Fatimah Alhayan3, Ahmad Jalal4,5,*, Hui Liu6,7,8,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084514
（This article belongs to the Special Issue: Robotics Vision and Thinking)
Abstract Hand gesture recognition (HGR) is essential for Human–Robot Interaction (HRI) but remains challenging due to variations in hand shape, motion, viewpoint, illumination, and background, while vision-based methods often suffer from sensitivity to skin tone, occlusions, deformations, and limited interpretability. To address these issues, we propose a unified framework integrating deep learning, geometry-driven analysis, and temporal motion modeling. We introduce Z-HandSegNet framework, involving a U-Net with a ResNet-34 encoder for robust hand segmentation, and the Ellipse-Guided Geometric Finger Segmentation and Keypoint Extraction (EG-FSKE) method, which decomposes hand silhouettes into palm and finger regions using distance transforms,… More >
Open Access
ARTICLE
Hamish Alsop1,*, Leandros Maglaras1,2,*, Naghmeh Moradpoor1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082500
Abstract A substantial body of research has focused on formalising what constitutes a “secure” messaging system, recognising that end-to-end encryption alone is insufficient to capture the full range of security, privacy, and usability properties that are expected by modern users. Several solutions have been proposed recently, each with their own drawbacks, making the need for a direct, infrastructure-light secure messaging system a continuing concern. This paper presents Ember, a serverless peer-to-peer messaging system providing end-to-end encrypted communication over a decentralised IPv6 mesh network. Ember combines an Extended Triple Diffie–Hellman (X3DH) initial key agreement with the Double Ratchet… More >
Open Access
ARTICLE
Munam Ali Shah1,*, Shazil Gul2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081738
（This article belongs to the Special Issue: Cloud Computing Security and Privacy: Advanced Technologies and Practical Applications)
Abstract The edge cloud-based Internet of Things (IoT) devices are diverse in size, type, and the function it performs. The tremendous increase in the number of cyberattacks, coupled with the adverse technologies, has rendered current security measures increasingly ineffective. Addressing security and privacy issues in edge cloud networks using machine learning and deep learning-based solutions is effective up to a certain extent; however, these intrusion detection solutions (IDS) rely heavily on the quantity and quality of data. This dependency results in high inaccuracy and high false positive rates in identifying malware. Moreover, the existing machine learning-based… More >
Open Access
ARTICLE
Md. Shujan Shak1, Nabila Rahman2, Fuad Mahmud3, Ashim Chandra Das1, M. F. Mridha4, Md. Jakir Hossen5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081102
（This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)
Abstract Early detection of neurological disorders is critical for effective treatment planning and improved quality of life. This study proposes a multi-input deep neural network that integrates vocal biomarkers and clinical sleep-related features to improve diagnostic accuracy. The model processes each modality through separate neural branches before combining high-level representations for final classification. We evaluate the approach using two public datasets: a Parkinson’s disease dataset containing 1195 voice samples and a sleep-disorder dataset with 80 patient records. Experimental results show that the proposed model outperforms classical machine learning baselines and single-modality deep learning models, achieving an More >
Open Access
ARTICLE
Ohsung Kwon1, Kyoung-Soub Lee2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083882
（This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)
Abstract Traffic congestion incurs massive social and economic costs. Accordingly, the importance of transportation management and planning is gradually expanding. In response, the establishment of intelligent transportation systems utilizing advanced technology is increasing, as are global attempts to predict traffic. However, predicting traffic volume time series is challenging because they are influenced by various environmental, social, and economic factors, and thus exhibit high nonlinearity and stochasticity. Accurate prediction of highway traffic, which forms the backbone of a nation’s transportation and logistics, is crucial. Therefore, this study examined whether meteorological, environmental, and economic covariates improve nationwide highway… More >
Open Access
ARTICLE
Yuan Feng1, Xiaotong Li2, Xinhua Hu3, Jianwei Zhang4, Zengyu Cai2,*, Liang Zhu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087978
（This article belongs to the Special Issue: AI-Driven Computational Networks and Cyber-Physical Systems: Models, Optimization, and Applications)
Abstract Wireless network traffic prediction enables operators to anticipate network trends, proactively develop network management strategies, and intelligently allocate network resources, thereby improving network service quality and enhancing users’ internet experience. Existing centralized network traffic prediction methods require transmitting large volumes of traffic data, which consumes significant network resources, incurs extra communication costs, and faces difficulties in full data sharing due to privacy concerns. Federated learning is a distributed learning method for multi-client joint training without sharing between clients, which can effectively solve such problems. However, the network traffic prediction method based on federated learning has… More >
Open Access
ARTICLE
Linh Nguyen Thi My1,2,*, Lap Thai Viet2, Hoai Truong Hieu2, Tham Vo1, Vinh Truong Hoang3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086877
（This article belongs to the Special Issue: Advances in Object Detection and Recognition)
Abstract Matching physical pills with drug names on a prescription is a safety-critical task for preventing medication errors. The pioneering PIMA (PIll-prescription MAtching) framework addressed this problem by aligning convolutional features (ResNet50) with textual embeddings (BERT) using a direction-agnostic GraphSAGE operator and a margin-based contrastive loss. While effective on controlled datasets, PIMA’s real-world deployment is hindered by its reliance on manual ground-truth annotations, sensitivity to background clutter, and a simplistic cosine similarity fusion that lacks selective attention. In this paper, we propose a redesigned, fully multimodal framework featuring five coordinated improvements: (i) two interchangeable visual encoders… More >
Open Access
ARTICLE
Van-Hai Pham1,2, Quang-Thai Pham1, Van-Vu Luyen1, Duy-Anh Nguyen1,*, Thanh-Toan Dao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086710
Abstract Deploying deep Spiking Neural Networks (SNNs) on resource-constrained Field-Programmable Gate Arrays (FPGAs) for Structural Health Monitoring (SHM) is hindered by the persistent storage of neuronal membrane potentials across timesteps, creating a substantial on-chip memory requirement that standard Convolutional Neural Network (CNN) acceleration techniques do not directly address. We propose a Hardware–Software Co-Design Framework that enables deployment of a Spiking ResNet-18 for automated crack detection on a Xilinx KV260 edge FPGA. The framework formalizes the Membrane Memory Floor (MMF) as a lower bound on on-chip memory for the target deployment architecture, introduces Proportional Channel Scaling (PCS)… More >
Open Access
ARTICLE
Salam Al-E’mari1,*, Yousef Sanjalawe2,*, Budoor Allehyani3, Ahd Aljarf4, Fares Alharbi5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.086552
Abstract Cyber-Physical Systems (CPS), including Industrial Internet of Things (IIoT) environments and smart infrastructures, are increasingly vulnerable to sophisticated cyberattacks due to their heterogeneous architectures, large-scale deployments, and strict real-time operational constraints. Conventional security mechanisms often struggle to provide low-latency threat detection, continuous monitoring, and reliable forensic traceability under resource-constrained conditions. This paper presents a lightweight Digital Twin (DT)-driven cybersecurity framework that integrates synchronized DT state modeling, hybrid anomaly detection, and tamper-evident audit logging for real-time CPS threat detection. Within this framework, the DT is realized as an operational, data-driven twin whose role is continuous bidirectional… More >
Open Access
ARTICLE
Qun Zhang1,2, Yusheng Mei1, Ziyue Wang1, Yingzhe Liu1, Zhuofeng Zhao1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.088700
（This article belongs to the Special Issue: Advanced Networking Technologies for Intelligent Transportation and Connected Vehicles)
Abstract Accurate road-level traffic flow prediction remains challenging in heterogeneous urban networks, where traffic interactions extend beyond direct road connectivity. Existing graph-based models can exploit road topology or vehicle trajectories but often overlook geometric continuity and dependencies between non-adjacent yet functionally related road segments. To address this gap, this study proposes a Proximity-Aware Road Network–Integrated Trajectory Graph Neural Network (PA-RNTrGNN). The model integrates trajectory-derived movement transitions, ordered road geometry, proximity-aware structural relations, and bidirectional traffic status information. Specifically, map-matched trajectories are converted into transition matrices to preserve directional movement causality; road geometries are encoded as ordered… More >
Open Access
ARTICLE
Xu Liu1, Fa Zhu2, Osama Alfarraj3, Fahad Alblehai3,*, Shakir Khan4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079640
Abstract Mobile laser scanning (MLS) plays a crucial role in urban vegetation monitoring by enabling the precise identification of roadside trees. However, in cluttered environments where tree crowns intermingle with various objects, methods relying solely on raw intensity data face substantial accuracy limitations. To address this issue, a dedicated calibration method is proposed, which models the relationship between acquisition geometry and intensity values through polynomial fitting. Specifically, calibration models were first established for a given LiDAR sensor using reference data from a standard diffuse reflection panel. A customized MLS platform equipped with this sensor was then More >
Open Access
ARTICLE
Jichao Xie1,*, Xinlei Liu1, Tong Duan1, Baolin Li1, Zhen Zhang1, Peng Yi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087314
Abstract In few-shot object detection scenarios involving emerging, rare, or specialized objects, the lack of diverse training samples severely constrains model performance. Recently, data augmentation methods leveraging 2D text-to-image generative models have gained traction; however, they exhibit limited capabilities in generating fine-grained categories and under-represented objects. To address these limitations, we propose 3DGe-Aug, a 3D generative model-based data augmentation framework for few-shot object detection. Our framework constructs 3D assets from limited 2D images via a 3D generative pipeline, thereby overcoming the rendering limitations of 2D generative models for fine-grained objects. Building upon these 3D assets, multi-perspective… More >
Open Access
ARTICLE
Hui Xu, Yonglei Yang*, Shuang Qu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.087053
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications, 2nd Edition)
Abstract With the widespread deployment of Software-Defined Networking (SDN), its centralized control architecture faces increasingly severe security threats despite improving network flexibility and programmability. Due to the high dimensionality, redundancy, and nonlinearity of SDN traffic data, existing intrusion detection methods often suffer from high computational cost, unstable feature selection, and limited generalization ability. To address these challenges, this paper proposes an SDN intrusion detection model based on an Adaptive Chinese Pangolin Optimizer (ACPO), termed ACPO-IDM. Unlike the original Chinese Pangolin Optimizer (CPO), ACPO integrates hierarchical initialization, adaptive inertia weight, elite local search, and precomputation-based caching strategies.… More >
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