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Inam Ullah¹, Zeeshan Ali Haider², Omar Almomani³, Karamath Ateeq⁴, Chang Choi^1,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086072 - 15 September 2026
Abstract The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such… More >

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Mahbub Hassan¹, Md Kamrul Islam^2,*, Md Shafiul Alam³, Mohammad Bin Amin^4,5,*, M. M. Hafizur Rahman⁶, Md Ehtesamul Haque⁷, Zoltán Nagy⁸
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085434 - 15 September 2026
Abstract The integration of the Internet of Things (IoT) into Intelligent Transportation Systems (ITS) is transforming urban mobility through widespread sensing, real-time data exchange, and Artificial Intelligence (AI)-driven adaptive control. Although research in this domain has expanded rapidly, bibliometric analyses combined with critical thematic synthesis remain limited. This study addresses this gap through a two-stage analysis of 574 peer-reviewed articles indexed in Scopus from 2011 to 2024. Using performance analysis, keyword co-occurrence mapping, and co-authorship network visualization, the study maps global publication trends, institutional productivity, and collaboration patterns. The results show an annual growth rate of… More >

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Navod Neranjan Thilakarathne^1,*, Madhuka Priyashan Wedisinhage Don², Sharmi Malisha Dilshani³, Jamil Abedalrahim Jamil Alsayaydeh^4,*, Mohd Faizal Bin Yusof⁵, Rostam Affendi Bin Hamzah⁴
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082189 - 15 September 2026
Abstract With the growing world population and demand for agricultural goods, agriculture requires innovative technologies that make the best use of resource, reduce waste, and increase productivity. So, smart agriculture, which involves the use of innovative digital technologies to enhance the quality and quantity of harvests, has come into play, superseding traditional agriculture. In recent years, the concept of the digital twin has intertwined with smart agriculture to enable precise control of entire farms, facilitating virtual replications. Overall, the digital twin enables continuous monitoring of real-time conditions in the field, providing valuable insights into crop health,… More >

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Afzal Badshah^1,*, Ali Daud^2,*, Sachi Arafat³, Wafa Almukadi⁴, Riad Alharbey⁴, Hussain Dawood⁵
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.077836 - 15 September 2026
Abstract The rapid increase in vehicle numbers and resulting traffic congestion have amplified critical challenges related to safety, environmental impact, and transportation efficiency. Road accidents account for approximately 1.19 million deaths annually, with an additional 20 to 50 million people injured. Moreover, congestion leads to the loss of nearly 50 billion hours and around 3 billion gallons of fuel each year. These pressing issues necessitate innovative and integrated solutions that can enhance the overall performance of the road. This study investigates the integration of Emerging Computing Technologies (ECT) into smart road infrastructures as a potential response… More >

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Chao He^1,*, Dongfeng Fu¹, Xin Xie², Jinkui Zhang³, Sirui Zhang⁴, Zheng Zhang⁵
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085383 - 15 September 2026
（This article belongs to the Special Issue: Advanced Technologies and Intelligent Applications for Autonomous Vehicles)
Abstract With the rapid development of fifth generation (5G), 5G-Advanced, edge computing, and early sixth generation (6G) technologies, the Internet of Vehicles (IoV) is evolving toward highly connected, intelligent, and delay-sensitive transportation services. Nevertheless, ground infrastructure still faces coverage holes, blockage, overloaded roadside units (RSUs), limited backhaul, and weak service continuity in urban canyons, crowded intersections, long highway segments, rural roads, and emergency areas. Unmanned aerial vehicles (UAVs) can provide flexible aerial relay, mobile sensing, temporary coverage, and lightweight edge computing support for these scenarios. This survey reviews UAV-assisted IoV from an integrated sensing, communication, and… More >

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Shadi Melebari, Muhammad Atif Ur Rehman^*, Ali Kashif Bashir, Mohammed Al-Khalidi
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085515 - 15 September 2026
Abstract Trust plays an important role in shaping human behaviour in cybersecurity contexts, influencing how individuals interact with digital systems and respond to security practices. However, existing research on trust and cybersecurity behaviours remains fragmented, with different studies adopting varied definitions of trust and examining a wide range of behavioural outcomes. This scoping review aims to systematically map and synthesise the literature on trust and cybersecurity behaviours. Following the PRISMA-ScR guidelines, relevant studies were identified, screened, and analysed to examine how trust has been conceptualised, what types of behaviour have been studied, and which theoretical and… More >

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Rafif Aydin Ahmad¹, Ntivuguruzwa Jean De La Croix^2,3, Reynandriel Pramas Thandya¹, Tohari Ahmad^1,*, Kambombo Mtonga⁴, Mungwarakarama Irenee²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083445 - 15 September 2026
Abstract The Internet of Things (IoT) enables seamless interconnectivity among billions of smart devices, transforming industries through real-time sensing, data processing, and intelligent decision-making. As IoT systems manage large volumes of sensitive data, ensuring secure and covert communication has become critical. Steganography, which conceals confidential information within ordinary transmissions, has emerged as a promising approach to strengthen security and privacy in IoT environments. However, despite the growing body of work, existing surveys often address steganography in general contexts without systematically analyzing its adaptation to the unique constraints of IoT systems. This article addresses this gap by… More >

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Peng Shen¹, Tenglong Li^1,2, Yongpeng Sun^1,2, Hao Cui^1,2, Guoqing Zhang^3,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086207 - 15 September 2026
Abstract Agriculture plays an important role in food security and social development. With the rapid development of the Fourth Agricultural Revolution, also known as Agriculture 4.0, traditional weed control methods that rely on manual experience and uniform herbicide application can no longer meet the demands for efficient, precise, and environmentally friendly production. Farmland weeds compete with crops for light, water, and nutrients, thereby seriously affecting crop yield and quality. Therefore, the development of efficient weed detection and recognition technologies is of great significance. In recent years, the rapid progress of deep learning in computer vision has… More >

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57

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Jihoon Moon^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.089115 - 15 September 2026
（This article belongs to the Special Issue: Explainable and Accountable Natural Language Processing)
Abstract Large language models and retrieval-augmented generation (RAG) systems are increasingly employed to transform evidence into decision-facing briefings, alerts, and recommendations. In these settings, explainability cannot be evaluated merely by fluency, readability, or factual correctness. A briefing may be factually correct while still being unsafe if it cites sources that do not substantiate the claim, suppresses uncertainty, converts correlational evidence into causal language, recommends an unauthorized action, or leaves no auditable path for human review. This review synthesizes 104 sources spanning explainable natural language processing (NLP), faithful explanation, hallucination and factuality evaluation, RAG, citation faithfulness, uncertainty… More >

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Indre Grigaraviciute, Nikolaj Goranin^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084893 - 15 September 2026
Abstract Gamification has emerged as an effective approach to enhance user engagement and security awareness in the field of information security. This study presents a systematic literature review (SLR) of research published between 2012 and March 2026, analysing publications from two major databases, Scopus and Web of Science (WoS). The review was conducted following the PRISMA 2020 guidelines. A comprehensive set of seven keywords: “gamification”, “information security”, “cybersecurity”, “security awareness”, “business security and privacy training”, “security management”, and “incident response”, was used to retrieve relevant studies. A total of 1487 articles were initially identified, of which… More >

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Yong Zheng^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084278 - 15 September 2026
Abstract Recommender systems can alleviate information overload by producing item recommendations tailored to user preferences. The performance usually relies on rich user-item interaction data; however, missing entries introduce sparsity that substantially degrades performance. Early work primarily treated rating imputation as a preprocessing mechanism for mitigating sparsity and alleviating cold-start issues through explicit matrix completion. More recently, missing-data estimation has evolved beyond static preprocessing toward broader inference-oriented paradigms, including pseudo-label estimation, counterfactual inference, and debiasing mechanisms integrated directly into the learning objective. In this paper, we present a structured review of rating imputation and inference-oriented missing-data estimation… More >

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Adel Assiri^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085321 - 15 September 2026
Abstract The emergence of Large Language Models (LLMs) has transformed biomedical text summarization, shifting research beyond conventional extractive approaches toward increasingly generative and agentic reasoning paradigms. However, rapid advances in model capabilities have exceeded current understanding of their methodological rigor, evaluation adequacy, and clinical readiness. This systematic review conducts a structured exploratory audit of Transformer- and LLM-based biomedical summarization systems to characterize reporting quality, validation practices, and translational maturity. Following PRISMA 2020 guidelines and the Population–Concept–Context framework, we systematically reviewed 178 original English-language studies published between January 2017 and March 2026. A multidimensional evaluation pipeline was… More >

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ARTICLE

Daniel Šolc^1,*, René Ivančák¹, Juraj Gazda¹, Eva Chovancová¹, Eugen Šlapák¹, Gabriel Bugár²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088224 - 15 September 2026
Abstract Autonomous mobile robots increasingly rely on three tightly coupled capabilities: low-latency wireless connectivity, edge-side compute acceleration, and simulation-based pre-training of perception and control models. Each has been studied extensively in isolation, but their joint deployment on a single platform remains rare. This paper presents an integrated framework combining a private 5G Stand-Alone (5G SA) access network, a Multi-Access Edge Computing (MEC) layer with adaptive offloading, and a digital-twin training pipeline in NVIDIA Omniverse Isaac Sim. It is realised on the Yahboom ROSMASTER R2 with an NVIDIA Jetson Orin NX, a Quectel RM530N-GL 5G modem in… More >

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Hao Sun, Xiaohong Ding^*, Min Xiong, Heng Zhang
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087308 - 15 September 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Optimization for Lightweight Materials and Structures)
Abstract Designing two-dimensional anisotropic mechanical metamaterial unit cells from prescribed effective properties remains a challenging inverse problem, particularly when directional stiffness and material usage need to be controlled simultaneously. In this work, a data-driven conditional diffusion framework is developed for generating unit-cell structures with target effective elastic moduli and volume fractions. A structure–property database containing 57,000 binary unit-cell images is first established through a random target-property-driven inverse homogenization method. The effective elastic moduli in the x and y directions, together with the volume fraction, are used as conditional labels, denoted as (E_x, E_y, V). A conditional denoising diffusion probabilistic… More >

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Haipeng Hu¹, Tao Hong², Junjie Zhu³, Xinjie Yao^4,*, Zhoupeng Guo^5,*, Dahai Xia^6,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086475 - 15 September 2026
Abstract Cold-start alloy-design campaigns often have too few labeled compositions to reliably locate phase boundaries for tree-structured composition-space Gaussian process regression (TCGPR). We study a controlled way to incorporate external CALPHAD-like boundary information into this partitioning step. The proposed MP-TCGPR method adds a Gaussian MAP penalty centered on a thermodynamic boundary estimate and uses an adaptive width ${\sigma }_j(N)={\sigma }_0\sqrt{1+N/N_{\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{s}\mathrm{s}}}$ to reduce prior influence as node-level data accumulate. The revised theory distinguishes asymptotic convergence from convergence rate: a fixed-width prior is also asymptotically negligible under local regularity, whereas the adaptive schedule accelerates finite-sample prior More >

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Yalong Liang¹, Xiaohui Yuan¹, Yuning Han², Pei Li^3,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086403 - 15 September 2026
Abstract NBT-based lead-free piezoceramics are promising alternatives to Pb-containing materials, yet their functional properties arise from complex and coupled composition–processing–structure–property relationships. Here, we develop a structure-informed machine learning framework to predict and interpret the piezoelectric coefficient d₃₃, depolarization temperature T_d, and relative dielectric permittivity ε_r. A database of 214 records from 34 publications was compiled, including 204 records for model development and 10 records for independent literature validation. The correlation analysis involved 38 variables, including 35 candidate input descriptors and three target properties. After Pearson correlation-based redundancy filtering, 30 nonredundant input descriptors were retained for model development. To… More >

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Gang Xu¹, Yeda Lian^2,*, Leike Yang^2,*, Hao Li², Yonggang Yang³, Lanjie Niu⁴
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087556 - 15 September 2026
Abstract Hot-section nickel-based single-crystal superalloy components under isothermal cyclic loading often exhibit systematic life shifts when datasets span different stress ratios and notch severities, making it difficult to maintain a globally consistent parameter set using conventional models. Because the effects of temperature, stress ratio, and stress concentration on cyclic response and damage evolution are typically nonlinear and coupled, this study proposes a multi-level equivalent driving force framework for fatigue life prediction, in which condition-induced life differences are represented as comparable shifts on a unified engineering driving-force scale. The proposed framework links the nominal cyclic response, the… More >

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Wasnaa Kadhim Jawad^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084636 - 15 September 2026
Abstract The accuracy of the electrode properties is important in the lithium-ion battery manufacturing process because the thickness variation is a direct influence on the compaction and structural uniformity, transport behavior and overall manufacturing quality. Of the different types of monitoring, ultrasonic frequency-domain relies on a non-destructive pathway for quality evaluation in a process-aware manner and is a promising approach; but, interpretable predictive modeling has been limited at the electrode level. In this study, an open-access database of ultrasonic frequency-domain data of lithium-ion battery electrodes under coating and calendering conditions was used to develop an artificial… More >

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Jianwei Zhang^1,2,*, Ran Shen¹, Qianqi Zhang¹, Yuanxin Li^1,*, Shengchao Chen^3,4,*, Minghao Zhao^2,3, Lubing Shi⁴, Bing Wang⁵
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084790 - 15 September 2026
Abstract Non-equibiaxial residual stresses are prevalent in engineering components such as welding, additive manufacturing, and surface strengthening, making their accurate detection critical for ensuring structural integrity. This paper proposes a novel method capable of simultaneously identifying two principal stress components (${\sigma }_{\mathrm{x}}^{\mathrm{R}}$, ${\sigma }_{\mathrm{z}}^{\mathrm{R}}$) using only an individual instrumented indentation. First, the normalized total indentation work variation W_norm and the residual indentation ellipticity λ are extracted as sensitive features from the indentation responses through dimensional analysis. Subsequently, a finite element (FE) simulation database comprising 2400 datasets was established to train three types of neural networks: the… More >

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Hassaan Idrees^1,*, Pattabhi Ramaiah Budarapu², Marco Paggi^1,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085634 - 15 September 2026
Abstract Hail impacts on photovoltaic laminates generate strongly nonlinear contact forces whose polynomial restoring form and coefficients govern the resulting damage pattern. Predicting the dynamic response across a range of impact velocities, and inferring substrate properties from post-event vibration measurements, are two tasks that classical time-integration schemes do not address in a unified manner. This work develops a physics-informed neural network (PINN) framework that handles both. For the forward problem, the network is conditioned on the initial velocity and trained simultaneously at four representative hail-impact speeds, i.e., $v_0\in \{2,3,4,6\}$ m/s, so that it learns… More >

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Nashat Nawafleh^*, Faris M. Al-Oqla
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087255 - 15 September 2026
Abstract In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by More >

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Lingtao Zhan¹, Jiale Zhu¹, Tingting Wang¹, Xiongbai Cao¹, Xiaoyu Hao^1,2, Cesare Grazioli³, Quanzheng Zhang¹, Huixia Yang¹, Teng Zhang^1,*, Yeliang Wang¹
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083413 - 15 September 2026
Abstract Scanning tunneling microscopy (STM) images are frequently affected by low-frequency vibrations, substrate-induced background variations, and bias-dependent contrast changes, which degrade molecular feature responses and hinder reliable segmentation. To address this challenge, we develop an STM-oriented Feature Pyramid Network (FPN) + Dual-Path Intensity Calibration (DPIC) framework by adapting a DPIC module, originally derived from a cloud-noise calibration mechanism, to the specific characteristics of STM molecular images. In this framework, DPIC is reformulated as a noise-aware feature calibration module that suppresses low-response background interference while preserving foreground molecular contours. We integrated DPIC into the FPN architecture and… More >

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Usman Mohyud Din Chaudhary¹, Humaira Arshad^1,*, Sajid Iqbal^2,*, Abdullah A. Alaulamie², Muhammad Ahsan Raza³, Abid Iqbal⁴
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084774 - 15 September 2026
（This article belongs to the Special Issue: Advanced Security and Privacy in Blockchain Systems)
Abstract Multisignature (multisig) wallets are fundamental to institutional-grade asset security on the Ethereum blockchain, yet Security Operations Centers (SOCs) currently rely on manual threshold rules to flag anomalous executions. Existing anomaly detection approaches suffer from three methodological deficiencies: (i) reliance on random train-test splits that leak future information, (ii) inclusion of post-hoc execution features unavailable at prediction time, and (iii) absence of cross-architectural benchmarking to justify algorithmic choices. This paper addresses all three gaps through a rigorous LightGBM-based framework that automates and explains SOC heuristics. We frame anomaly detection as post-execution forensic triage, where the model… More >

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Fei Bu¹, Zheng Wang^2,3,*, Yong Pan⁴, Zhaomin Wu¹, Yuchen Liang¹, Zhongshan Zhu⁴, Tengfei Tu⁵
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083956 - 15 September 2026
Abstract Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge,… More >

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Fangfang Shan^*, Yuhang Liu^*, Lulu Fan, Zhuo Chen, Yifan Mao, Peixue Wang
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085467 - 15 September 2026
Abstract The engineering of Federated Learning (FL) systems faces significant challenges in balancing two critical non-functional requirements: ensuring robust system utility and maintaining high privacy protection standards under non-independent and identically distributed (Non-IID) data environments. Existing software architectures often struggle to achieve an optimal trade-off between these competing demands. This paper proposes SAM-ADPFL, a novel architectural framework designed to improve the engineering and management of privacy-preserving distributed machine learning systems. First, we design a geometry-aware adaptive aggregation component that dynamically reallocates aggregation weights based on local landscape properties, guiding the global model to effectively suppress model More >

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Junjie Wu, Yang Liu, Danyi Sheng, Shiwei Cheng^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085839 - 15 September 2026
Abstract With the development of brain-computer interfaces (BCI), more and more studies are using electroencephalography (EEG) for emotion recognition. Traditional emotion recognition often uses 2D videos and pictures to stimulate emotions, which do not provide an immersive feeling. Virtual reality (VR) can provide a more immersive and realistic experience, and recent studies are beginning to utilize EEG for emotion recognition in VR. However, due to the limited information on single-modal features, it is not possible to fully recognize individual emotions. To address this problem, we proposed a multi-modal approach in VR, which utilized a VR scene… More >

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Shengxuan Dong, Xiongwei Li^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026
Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >

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Chun-Hsiu Yeh^1,*, Xian-Zhong Lin^1,*, Yi-Teng Lin¹, Yung-Chen Chou², Wei-Cheng Shen¹
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087068 - 15 September 2026
Abstract Printed circuit board (PCB) defect detection is critical for industrial quality control, where detection models must identify small and irregular defects while satisfying real-time inspection requirements. However, conventional deep learning-based detectors often require substantial computational resources, making deployment on edge devices difficult. To address this issue, FGE-YOLO is proposed as a deployment-oriented lightweight object detection model based on YOLOv8. The proposed model integrates a FasterNet-based backbone, a GhostConv-Based Neck, and an Efficient Channel Attention (ECA) mechanism. In the backbone, standard convolutions are retained in the shallow P1 and P2 stages to preserve low-level spatial details,… More >

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Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026
Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

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Weijia Liu¹, Junwen Deng², Hao Li³, Zhenyong Zhang^3,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086022 - 15 September 2026
Abstract With the rapid development of artificial intelligence technologies in machine learning-as-a-service (MLaaS), deep learning-based intelligent models have demonstrated high value in applications such as trend prediction and risk assessment. However, MLaaS data are typically highly sensitive and contain private information. In cross-institutional collaborative modeling scenarios, different departments and local centers often hold partial, heterogeneous data resources on MLaaS platforms. Given data security, privacy, and compliance requirements, raw data are difficult to share directly, which makes it challenging to apply centralized model training methods. This paper proposes FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training More >

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Jaekyung Lee^1,2, Byungsung Ko², Jiwon Lee², Jaeheon Park², Taewon Kim², Seoktae Kim², Wonhee Kim^3,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085203 - 15 September 2026
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract This study proposes the Artificial Intelligence-integrated Inspection Ecosystem (AIIE) as an autonomous condition monitoring platform to enable Prognostics and Health Management (PHM) for underground infrastructure facilities at the Korea Electric Power Corporation (KEPCO) power Information and Communication Technology (ICT) center. To address the environmental dependency of conventional systems, which necessitate extensive control logic redesigns upon changes in target facilities or environments, a three-layer abstraction architecture separating directive, orchestration, and execution roles is established, integrating a quadrupedal robot with heterogeneous sensors into a unified control structure. To overcome the limitation of relying on one general-purpose model… More >

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Rong Ma¹, Jin Ren¹, Shaobing Shen¹, Yunhe Li^1,*, Man Hu²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086288 - 15 September 2026
（This article belongs to the Special Issue: Large Language Models: Foundations, Advances, and Emerging Applications)
Abstract Large language models (LLMs) have emerged as a transformative foundation across natural language processing and intelligent systems, yet their security, robustness, and responsible deployment remain critical open challenges. In particular, the multi-dimensional evaluation of toxicity and bias in Chinese LLMs remains limited, posing significant risks for real-world applications that demand trustworthy AI. In this paper, we propose TrustEval, a dataset- and model-agnostic evaluation framework that provides a systematic assessment of Chinese LLMs from the perspectives of toxicity, bias, and robustness. Unlike existing benchmarks that focus primarily on capability, TrustEval explicitly targets model security and reliability… More >

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Rui Huang¹, Ziwei Zhang^2,*, Kari Tusongjiang¹, Bowen Zhang³, Ning Yang³, Xiaowei Li¹, Aimudula Maierdan¹
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086232 - 15 September 2026
Abstract The accuracy of partial discharge (PD) pattern recognition is essential for assessing the insulation condition of gas-insulated switchgear (GIS). However, in practical recognition tasks, phase-resolved partial discharge (PRPD) patterns often exhibit complex feature distributions, and key discharge characteristics may be weakened during feature extraction. This study proposes an improved sparrow search algorithm (ISSA)-optimized attention-enhanced ConvNeXt model for GIS PD pattern recognition. A multi-criterion grayscale evaluation scheme is first employed to select the most suitable grayscale conversion for PRPD patterns, aiming to preserve informative discharge regions and reduce redundant color interference. Subsequently, an attention-enhanced ConvNeXt model… More >

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Shigan Ding^1,2, Zihe Qin^1,3,*, Feng Zhang^1,3, Mao Zheng², Bowen Lin²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086097 - 15 September 2026
Abstract To address the challenge of precise station-keeping for underactuated unmanned surface vehicles (USVs) in unknown current environments, our team previously proposed a solution based on a “virtual anchoring” method. However, field tests revealed that the inherent “virtual anchor line” constraint limits positioning accuracy. This work introduces a novel control strategy to overcome the aforementioned issue, which enables accurate unmanned surface vehicle (USV) station-keeping by significantly reducing the distance constraint inherent to traditional virtual anchoring. The core innovation lies in a Dynamic Virtual Guide-Point, whose position is updated based on a real-time estimate of the current… More >

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Sajjad Rabbani¹, Rao Muhammad Asif ¹, Heba G. Mohamed², Adnan Yousaf^1,*, Ateeq Ur Rehman^3,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084783 - 15 September 2026
Abstract The management of demand response (DR) in smart grids increasingly relies on low-latency, reliable, and scalable communication, yet traditional DR signaling methods fail in dense network environments. In this paper, a communication-aware clustered demand response architecture (CCA-DR) for smart grids based on 5G technology is proposed, in which DR users are clustered based on service-area density and assigned to the closest communication-aware cluster. It is linked via directional antenna pairs to reduce cumulative signal attenuation. The model incorporates the properties of the 5G broadband channels, such as path loss, interference, latency, and reliability constraints, into… More >

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Xinglong Cao, Cong Wang^*, Jie Yan, Songcan Yu, Mingze He
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086163 - 15 September 2026
（This article belongs to the Special Issue: Recent Advances in Malware Detection)
Abstract There is currently a lack of systematic research on the fine-grained detection of multi-language and multi-type exploit scripts. To address this gap, this study proposes a model named ASTBertX (AST + BERT + XGBoost) for identifying the specific exploit types of malicious scripts; the model organically integrates code sequence semantics with structural semantics. First, the model utilizes the pre-trained model GraphCodeBERT to extract contextual semantic representations of the scripts; simultaneously, it introduces semantic enhancement nodes into the Abstract Syntax Tree (AST) and employs GATv2 to learn the AST’s structural representation. These two representations are mapped… More >

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Ao Nie¹, Wei Zhou¹, Yi Yu¹, Wan Xu^1,2,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083871 - 15 September 2026
Abstract Currently, mobile robot path planning in unstructured forest environments remains a hot research topic in the robotics field. Studies applying the Greylag Goose Optimization (GGO) algorithm to multi-robot path planning under such scenarios are limited, and these approaches still face significant challenges, such as insufficient trajectory smoothness, frequent coordination conflicts, and relatively slow convergence to optimal solutions. To address these issues, this paper proposes an Evolutionary Game-Theoretic Improved GGO algorithm (EG-IGGO), designed to optimize path quality while ensuring robust obstacle avoidance capabilities. Specifically, two novel strategies—the population alignment strategy and the dual-source adaptive guidance strategy—are… More >

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Mohammed Alonazi¹, Muhammad Adeel Ahmed Tahir², Adnan Ahmed Rafique², Maha Abdelhaq³, Raed Alsaqour⁴, Ahmad Jalal^5,6, Jeongmin Park^7,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086550 - 15 September 2026
（This article belongs to the Special Issue: Complex Network Approaches for Resilient and Efficient Urban Transportation Systems)
Abstract Accurate vehicle detection, tracking, and counting are fundamental components of Intelligent Transportation Systems (ITS) and urban traffic surveillance. However, real-world deployment remains challenging due to domain shifts, illumination variations, occlusions, dense traffic conditions, and heterogeneous data distributions. Existing studies often address detection, tracking, and counting as independent tasks, resulting in limited cross-domain generalization and inconsistent performance in complex traffic environments. To overcome these limitations, this paper proposes a unified cross-domain framework that jointly integrates vehicle detection, tracking, and lane-aware counting within a single intelligent traffic analytics pipeline. The proposed framework begins with image enhancement using… More >

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Ahmad Raza^1,*, Abdul Basit^1,*, Syed Muqtar Ahmed², Zeeshan Ahmad Arfeen^3,*, Muhammad I. Masud⁴, Muhammad Farid Zamir⁵, Mehreen Kausar Azam⁶, Touqeer Ahmed Jumani⁷
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084902 - 15 September 2026
Abstract The development of deepfake technologies is a threat to digital media authentication and cybersecurity infrastructure. The current paper proposes a method for detecting manipulated images of faces based on the Vision Transformer architecture. We fine-tune a pre-trained ViT-Base-Patch16-224 model based on this well-curated dataset of 12,137 face images, which includes an almost equal number of real and synthetic face images using a variety of different generation methods. The data set contains real-life photographs of CelebA and FFHQ, along with artificial samples of the publicly available Kaggle repositories (FaceForensics++, Celeb-DF, and DFDC) and 600 self-collected photos… More >

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Ibtisam A. Taqi^*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.082352 - 15 September 2026
Abstract Multimedia technology and social media platforms like Facebook, Telegram, Viber, and WhatsApp, as well as numerous industries like communications, banking, and the military, depend heavily on images. Therefore, the biggest issue these days is safeguarding the image from theft or hacking during storage or transmission over the internet. This study suggests a novel approach of grayscale image encoding that uses a two-dimensional (2D) logistic map and the Secure Hash Algorithm (SHA). First, convert a color image to grayscale. Second, use the recently proposed equations to calculate the initial states of the Two-Dimensional Logistic Map (2DLM).… More >

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Lasseni Coulibaly^1,*, Damien Hanyurwimfura¹, Evariste Twahirwa¹, Abubakar Diwani²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087148 - 15 September 2026
Abstract Intelligent transportation systems, a critical pillar for smart cities, enable real-time vehicular communications to prevent human errors and improve traffic safety and efficiency. However, the open and highly dynamic nature of vehicular networks exposes them to various security threats, including message tampering, impersonation, and privacy violations. Several authentication and key agreement (AKA) protocols have been proposed to mitigate these risks, but often fail to maintain future-proof security against emerging quantum threats or introduce significant latency that affects real-time applications by relying on computationally expensive public-key cryptography, blockchain or centralized architectures. This paper proposes a new… More >

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Yu Chen¹, Panpan Chen¹, Jun Wu^1,2,3, Shuai Guo¹, Jiahui Huang¹, Xinyi Zhu¹, Qun Zhang^1,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087279 - 15 September 2026
Abstract Multimodal Emotion Recognition in Conversations (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they apply shared or coarse-grained graph construction rules across modalities, overlooking their distinct dependency patterns; (2) they rely on fixed-activation MLPs for feature transformation, limiting nonlinear representation capacity in complex emotional scenarios; (3) they focus predominantly on contextual modeling while underexploring local emotion discrimination between related utterances. To address these issues, we propose Joint Differentiated Modality-Aware Heterogeneous Graph and Local Emotion Difference Supervision for Multimodal Emotion Recognition in Conversations (DMHG-LEDS), a novel MERC framework. Specifically, modality-aware… More >

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Hui Zhang¹, Mangang Xie^1,*, Baozhen An², Jing Wei¹
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026
Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >

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Chung-Pao Lin¹, Yi-You Hou^2,*, Teh-Lu Liao¹
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085788 - 15 September 2026
Abstract Smart grids (SG) integrate multiple network entities to achieve automation, but their interconnected nature also exposes communication networks to various security threats, such as replay, tampering, and man-in-the-middle (MITM) attacks. Existing encryption frameworks for smart grid edge devices often suffer from high computational complexity or lack of dynamic key management, leading to key leakage risks and communication bottlenecks. To address these challenges, this research proposes a lightweight end-to-end secure communication architecture specifically designed for smart grid power monitoring. This framework employs the Message Queuing Telemetry Transport (MQTT) protocol as the asynchronous communication backbone, effectively alleviating… More >

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Jeng-Shyang Pan^1,2,3, Cuijing Cao⁴, Shu-Chuan Chu^2,*, Lingping Kong⁵, Xingsi Xue⁶, Jia Zhao⁷
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085803 - 15 September 2026
Abstract This study proposes a Genetic Programming-based Search Strategy Generation Framework (GP-SSGF) and a novel variant of the tumbleweed algorithm, the genetic programming-based tumbleweed algorithm (GPTA). The framework automates the evolution of search formulas within metaheuristic algorithms, reducing reliance on manually designed update rules and enhancing adaptability. The GPTA algorithm, developed within this framework, employs evolved position-update formulas to improve search efficiency and convergence. Through extensive experiments on the CEC2017 benchmark suite across multiple dimensions, GPTA demonstrates superior solution quality and stability compared with other metaheuristic algorithms. Its practical effectiveness is further validated in emergency material More >

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Yunis Torun^1,*, Burak Seckin¹, Rukiye Karakis²
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085198 - 15 September 2026
Abstract Marble classification has traditionally relied on human visual inspection, where operators assess color, texture, and pattern alignment to determine quality. However, this manual process is subjective, inconsistent, and inefficient for large-scale industrial applications. To address these limitations, this study proposes DeepMarbleVision, a texture-aware ensemble deep learning framework with energy-layer-based feature fusion for marble quality classification. A real-world dataset was created using the MarbleVision system, including three marble quality classes acquired from an industrial marble classification environment. The proposed approach integrates energy-layer-based feature fusion into TCNN variants of AlexNet, ResNet, and DenseNet, which were initialized through… More >

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Rihem Farkh¹, Ghislain Oudinet², Alaeddine Moussa³, Yasser Fouad^4,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086607 - 15 September 2026
Abstract Autonomous driving systems must reason not only about the current scene but also about how the environment may evolve under alternative actions. Although predictive world models can generate future latent rollouts, these rollouts are often consumed directly by planners or explanation modules without an explicit and auditable interpretation stage. This paper presents a predictive and explainable driving framework centered on a Future Interpretation Module (FIM), which transforms action-conditioned future rollouts into structured descriptors, including risk trend, peak risk, time-to-critical, minimum clearance, predicted collision, dominant predicted event, and confidence. An aligned latent interface, trained with feature-alignment… More >

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Yu-Chi Jiang^1,2,*
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083187 - 15 September 2026
（This article belongs to the Special Issue: Next-Generation Optimization: Quantum and Hybrid Classical Computing for Real-World Applications)
Abstract Quantum logic reversible synthesis is a fundamental operation in quantum computing. One of the most challenging issues in this field resides in navigating the immense search space to synthesize the most compact circuit configurations, which are critical for realizing reliable, noise-free, and error-free quantum computing systems. To address this challenge, this study proposes a novel hypercube-encoded quantum-inspired optimization framework to formulate the synthesis task as a trajectory-finding process. This structure-informed domain knowledge transformation delivers exceptional search direction guidance, moving away from blind, black-box exploration. Specifically, by mapping the reversible functions onto the hypercube architecture, the… More >

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Hui Xu, Shuang Qu^*, Pan Hu
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087054 - 15 September 2026
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications, 2nd Edition)
Abstract With the rapid growth in the number of end devices in the Internet of Things (IoT), network traffic has become increasingly complex and redundant, while multiple attack types often coexist, posing major challenges to traffic anomaly detection. Traditional machine learning-based methods for IoT traffic anomaly detection often suffer from severe feature redundancy, high computational complexity, and low detection efficiency, making it difficult to simultaneously achieve high detection accuracy and computational efficiency. To address this issue, metaheuristic algorithms are often introduced in the feature selection stage to reduce feature redundancy and improve detection efficiency. However, the… More >

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Mohammed Atoum¹, Malik Al-Essa^1,*, Yazeed Alsarhan², Ahmad K. Al Hwaitat¹, Muhammad Imran³
CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085403 - 15 September 2026
（This article belongs to the Special Issue: Advanced and Interpretable Malware Detection in Modern Cyber Environments)
Abstract With the urgent need for Intrusion Detection Systems (IDS) to protect digital infrastructure, eXplainable Artificial Intelligence (XAI) has become an important supporting layer. The integration of XAI and IDS can rank influential features that affect IDS decisions, yet these outputs often remain difficult to translate into operational security actions. In this work, we propose LEXIS (LLM-Enhanced eXplainable Intrusion detection System), an LLM-enhanced explainable IDS that converts sample-level explanations into structured report drafts that organize feature attributions into candidate response actions for analyst review, through an evidence-bounded reporting process. Given a network trace, the classifier generates… More >

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