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Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073883 - 10 February 2026
（This article belongs to the Special Issue: Next-Generation Cybersecurity: AI, Post-Quantum Cryptography, and Chaotic Innovations)
Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >

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Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

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Junnian Wang¹, Xiaoxia Wang¹, Zexin Luo¹, Qixiang Ouyang¹, Chao Zhou¹, Huanyu Wang^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074473 - 10 February 2026
Abstract Internet of Things (IoTs) devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location. However, The extensive deployment of these devices also makes them attractive victims for the malicious actions of adversaries. Within the spectrum of existing threats, Side-Channel Attacks (SCAs) have established themselves as an effective way to compromise cryptographic implementations. These attacks exploit unintended, unintended physical leakage that occurs during the cryptographic execution of devices, bypassing the theoretical strength of the crypto design. In recent times, the advancement of deep learning has provided SCAs with a… More >

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Tongcheng Geng^1,#, Zhiyuan Xu^2,#, Yubin Qu^3,*, W. Eric Wong⁴
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074081 - 10 February 2026
（This article belongs to the Special Issue: Large Language Models in Password Authentication Security: Challenges, Solutions and Future Directions)
Abstract Large language models (LLMs) have revolutionized AI applications across diverse domains. However, their widespread deployment has introduced critical security vulnerabilities, particularly prompt injection attacks that manipulate model behavior through malicious instructions. Following Kitchenham’s guidelines, this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape. Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks, achieving over 90% success rates against unprotected systems. In response, defense mechanisms show varying effectiveness: input preprocessing achieves 60%–80% detection rates and advanced architectural defenses More >

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3125

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Yu-Xuan Zhou¹, Kai-Qing Zhou^1,*, Wei-Lin Chen¹, Zhou-Hua Liao¹, Di-Wen Kang^1,2
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075099 - 10 February 2026
（This article belongs to the Special Issue: Advances in Bio-Inspired Optimization Algorithms: Theory, Algorithms, and Applications)
Abstract The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core… More >

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86

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REVIEW

Sangeeta Mittal¹, Chetna Gupta¹, Varun Gupta^2,3,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071599 - 10 February 2026
Abstract This survey presents a comprehensive examination of sensor fusion research spanning four decades, tracing the methodological evolution, application domains, and alignment with classical hierarchical models. Building on this long-term trajectory, the foundational approaches such as probabilistic inference, early neural networks, rule-based methods, and feature-level fusion established the principles of uncertainty handling and multi-sensor integration in the 1990s. The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering, Bayesian–Dempster–Shafer hybrids, distributed consensus algorithms, and machine learning ensembles for more robust and domain-specific implementations. From 2011 to 2020, the widespread… More >

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44

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REVIEW

Hui Yu¹, Jun Liu^1,*, Mingwei Lin^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074191 - 10 February 2026
Abstract Small object detection has been a focus of attention since the emergence of deep learning-based object detection. Although classical object detection frameworks have made significant contributions to the development of object detection, there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes. In particular, the YOLO (You Only Look Once) series of detection models, renowned for their real-time performance, have undergone numerous adaptations aimed at improving the detection of small targets. In this survey, we summarize the state-of-the-art YOLO-based small object detection More >

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53

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ARTICLE

Sooyong Jeong^1,#, Cheolhee Park^2,#, Dowon Hong^3,*, Changho Seo⁴
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072561 - 10 February 2026
（This article belongs to the Special Issue: AI-Driven Intrusion Detection and Threat Analysis in Cybersecurity)
Abstract With the growing complexity and decentralization of network systems, the attack surface has expanded, which has led to greater concerns over network threats. In this context, artificial intelligence (AI)-based network intrusion detection systems (NIDS) have been extensively studied, and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms. However, most existing works focus on individual distributed learning frameworks, and there is a lack of systematic evaluations that compare different algorithms under consistent conditions. In this paper, we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning (FL), Split… More >

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359

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ARTICLE

Zeyu Wang¹, Jian-Hong Wang^1,*, Kuo-Chun Hsu^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072881 - 10 February 2026
Abstract In daily life, keyword spotting plays an important role in human-computer interaction. However, noise often interferes with the extraction of time-frequency information, and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge. To address this, we propose a novel time-frequency dual-branch parallel residual network, which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module. The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features, effectively avoiding the potential information loss caused by serial stacking, while enhancing information… More >

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52

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ARTICLE

Tomás Bernal-Beltrán¹, Ronghao Pan¹, José Antonio García-Díaz¹, María del Pilar Salas-Zárate², Mario Andrés Paredes-Valverde², Rafael Valencia-García^1,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073629 - 10 February 2026
Abstract The malicious dissemination of hate speech via compromised accounts, automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern. Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources. In this paper, we compare two predominant AI-based approaches for the forensic detection of malicious hate speech: (1) fine-tuning encoder-only models that have been trained in Spanish and (2) In-Context Learning techniques (Zero- and Few-Shot Learning) with large-scale language models. Our approach goes beyond binary classification, proposing a comprehensive, multidimensional evaluation that labels each… More >

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67

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ARTICLE

Bassam Al-Shargabi^1,*, Mohammed Abbas Fadhil Al-Husainy², Abdelrahman Abuarqoub¹, Omar Albahbouh Aldabbas³
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074550 - 10 February 2026
Abstract The advent of 5G technology has significantly enhanced the transmission of images over networks, expanding data accessibility and exposure across various applications in digital technology and social media. Consequently, the protection of sensitive data has become increasingly critical. Regardless of the complexity of the encryption algorithm used, a robust and highly secure encryption key is essential, with randomness and key space being crucial factors. This paper proposes a new Robust Deoxyribonucleic Acid (RDNA) nucleotide-based encryption method. The RDNA encryption method leverages the unique properties of DNA nucleotides, including their inherent randomness and extensive key space,… More >

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54

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ARTICLE

Zhendong Du^*, Hanliu Wang, Kenji Hashimoto
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074520 - 10 February 2026
Abstract Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models (LLMs) possess genuine structural reasoning capabilities beyond lexical memorization. When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures, existing direct generation approaches exhibit severe performance degradation. This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement. The system implements a complete generate-verify-repair cycle through six core processing components: semantic comprehension extracts structural constraints, language planner generates text plans, symbol translator performs structure-preserving mapping,… More >

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43

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ARTICLE

Cosmin Dumitru, Emanuel Pantelimon, Alexandru Guzu, Georgian Nicolae^*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072749 - 10 February 2026
Abstract Unmanned aerial vehicles (UAVs), especially quadcopters, have become indispensable in numerous industrial and scientific applications due to their flexibility, low cost, and capability to operate in dynamic environments. This paper presents a complete design and implementation of a compact autonomous quadcopter capable of trajectory tracking, object detection, precision landing, and real-time telemetry via long-range communication protocols. The system integrates an onboard flight controller running real-time sensor fusion algorithms, a vision-based detection system on a companion single-board computer, and a telemetry unit using Long Range (LoRa) communication. Extensive flight tests were conducted to validate the system’s More >

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27

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ARTICLE

Tianxia Zou^1,*, Yilin Sun², Fuhao Fan¹, Zhen Zheng¹, Yanjin Xu², Baoshuai Han²
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074121 - 10 February 2026
Abstract Ultra-high-strength aluminum alloy profile is an ideal choice for aerospace structural materials due to its excellent specific strength and corrosion resistance. However, issues such as uneven metal flow, stress concentration, and forming defects are prone to occur during their extrusion. This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U, multi-cavity thin-walled structure. Firstly, hot compression experiments were conducted at temperatures of 350°C, 400°C, and 450°C, with strain rates of 0.01 and 1.0 s⁻¹, to investigate the plastic deformation behavior of the material. Subsequently, a 3D coupled thermo-mechanical extrusion simulation model was established… More >

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ARTICLE

Thi-Xuyen Bui^1,2, Yu-Sheng Lu¹, Yu-Sheng Liao¹, Te-Hua Fang^1,3,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074862 - 10 February 2026
（This article belongs to the Special Issue: Computational Approaches for Tribological Materials and Surface Engineering)
Abstract The mechanical performance of exceedingly soft materials such as Ag is significantly influenced by various working conditions. Therefore, this study systematically investigates the effects of crack geometry, substrate crystal orientation, and indenter shape on crack propagation. The mechanical response of Ag is analyzed using the quasi-continuum (QC) method. A pre-crack with a predefined depth and angle was introduced to initiate fracture behavior. The results show that when the pre-crack height is 50 Å, the crack propagates rapidly as the imprint depth increases from 0 to 7 Å, grows steadily up to 15 Å, and then… More >

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81

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ARTICLE

Naresh Kumar¹, Anuj Kumar^1,*, Abhishek K. Mishra^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074430 - 10 February 2026
Abstract Density functional theory (DFT) calculations were employed to investigate the adsorption behavior of NH₃, AsH₃, PH₃, CO₂, and CH₄ molecules on both pristine and mono-vacancy phosphorene sheets. The pristine phosphorene surface shows weak physisorption with all the gas molecules, inducing only minor changes in its structural and electronic properties. However, the introduction of mono-vacancies significantly enhances the interaction strength with NH₃, PH₃, CO₂, and CH₄. These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects. The defective phosphorene sheet also exhibits enhanced adsorption energies, along with favorable sensitivity and recovery characteristics, highlighting its potential More >

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ARTICLE

Zhanglei Wang^1,2, Shuo Yan^1,2, Jingyu Gao^1,2, Haoyu Wu^1,2, Baili Wang^1,2, Xiuying Zhao^1,2,*, Shikai Hu^1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075667 - 10 February 2026
（This article belongs to the Special Issue: Machine Learning Methods in Materials Science)
Abstract The glass transition temperature (T_g) of styrene-butadiene rubber (SBR) is a key parameter determining its low-temperature flexibility and processing performance. Accurate prediction of T_g is crucial for material design and application optimisation. Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency, cost, and accuracy, this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy. Based on the constructed high-quality dataset containing 96 SBR samples, nine machine learning models were employed to predict the T_g of SBR and… More >

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ARTICLE

Evgenii V. Fomin^1,2, Ilya A. Bryukhanov¹, Natalya A. Grachyova², Alexander E. Mayer^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.071952 - 10 February 2026
Abstract It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements, including shock wave loading. However, the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed. In this work, we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions ([100], [110], [111], [112], [102], [114], [123], [134], [221] and [401]) of shock compression using molecular dynamics (MD) simulations. The results showed a high pressure (>160 GPa for Cu and >40 GPa for Al) of the FCC-to-BCC transition.… More >

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Nataliya Kazantseva^1,2,*, Nikolai Saharov¹, Denis Davydov^1,2, Nikolai Popov², Maxim Il’inikh¹
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073078 - 10 February 2026
（This article belongs to the Special Issue: Perspective Materials for Science and Industrial: Modeling and Simulation)
Abstract Because of the developed surface of the Triply Periodic Minimum Surface (TPMS) structures, polylactide (PLA) products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery. For implants, mechanical properties are key performance characteristics, so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure. The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research. Two TPMS topologies, the Schwarz Diamond and Gyroid architectures, were used for the sample construction by… More >

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ARTICLE

Yingli Liu^1,2, Weiyong Tang^1,2, Xiao Yang^1,2, Jiancheng Yin^3,*, Haihe Zhou^1,2
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074681 - 10 February 2026
Abstract Quantitative analysis of aluminum-silicon (Al-Si) alloy microstructure is crucial for evaluating and controlling alloy performance. Conventional analysis methods rely on manual segmentation, which is inefficient and subjective, while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data. Furthermore, existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data. To address these issues, this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation. First, we introduce a Rotational Uncertainty Correction Strategy (RUCS). This strategy employs multi-angle rotational perturbations… More >

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Muhammad Ishtiaq¹, Yeon-Ju Lee², Annabathini Geetha Bhavani³, Sung-Gyu Kang^1,*, Nagireddy Gari Subba Reddy^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075957 - 10 February 2026
（This article belongs to the Special Issue: M5S: Multiphysics Modelling of Multiscale and Multifunctional Materials and Structures)
Abstract Lithium manganese silicate (Li-Mn-Si-O) cathodes are key components of lithium-ion batteries, and their physical and mechanical properties are strongly influenced by their underlying crystal structures. In this study, a range of machine learning (ML) algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory (DFT) data obtained from the Materials Project database. The dataset comprised 211 compositions characterized by key descriptors, including formation energy, energy above the hull, bandgap, atomic site number, density, and unit cell volume. These features were utilized to classify the materials into monoclinic… More >

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ARTICLE

Viorel Mînzu^1,*, Iulian Arama²
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075619 - 10 February 2026
（This article belongs to the Special Issue: Machine Learning in the Mechanics of Materials and Structures)
Abstract This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive… More >

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Huihui Zhu¹, Fei Tang^2,*, Chunhua Jin³, Ping Wang¹
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073550 - 10 February 2026
（This article belongs to the Special Issue: Privacy-Enhancing Technologies for Secure Data Cooperation and Circulation)
Abstract The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication. Although various authentication and key agreement protocols have been developed, current approaches are constrained by homogeneous cryptosystem frameworks, namely public key infrastructure (PKI), identity-based cryptography (IBC), or certificateless cryptography (CLC), each presenting limitations in client-server architectures. Specifically, PKI incurs certificate management overhead, IBC introduces key escrow risks, and CLC encounters cross-system interoperability challenges. To overcome these shortcomings, this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client More >

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Roshni Khedgaonkar¹, Pravinkumar Sonsare², Kavita Singh¹, Ayman Altameem³, Hameed R. Farhan⁴, Salil Bharany⁵, Ateeq Ur Rehman^6,*, Ahmad Almogren^7,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072651 - 10 February 2026
（This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)
Abstract Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN… More >

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ARTICLE

Jiaxi Wang¹, Wenbo Sun², Ziyuan Zhou¹, Shihua Wu¹, Jiang Xu¹, Shan Ji^3,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073237 - 10 February 2026
（This article belongs to the Special Issue: Recent Advances in Blockchain Technology and Applications)
Abstract Lightweight nodes are crucial for blockchain scalability, but verifying the availability of complete block data puts significant strain on bandwidth and latency. Existing data availability sampling (DAS) schemes either require trusted setups or suffer from high communication overhead and low verification efficiency. This paper presents ISTIRDA, a DAS scheme that lets light clients certify availability by sampling small random codeword symbols. Built on ISTIR, an improved Reed–Solomon interactive oracle proof of proximity, ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication. This paper formalizes opening consistency and prove security… More >

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Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074416 - 10 February 2026
Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >

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Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072840 - 10 February 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

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Leyu Dai^1,2,3, Jindong Wang^1,2,3, Ming Zhou^1,2,3, Song Guo^1,2,3, Hengwei Zhang^1,2,3,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072449 - 10 February 2026
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract In recent years, with the rapid advancement of artificial intelligence, object detection algorithms have made significant strides in accuracy and computational efficiency. Notably, research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images (ORSIs). However, in the realm of adversarial attacks, developing adversarial techniques tailored to Anchor-Free models remains challenging. Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures. Furthermore, the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks. This study presents… More >

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Noura Mohammed Alaskar¹, Muzammil Hussain², Saif Jasim Almheiri¹, Atta-ur-Rahman³, Adnan Khan^4,5,6, Khan M. Adnan^7,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074041 - 10 February 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data… More >

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Ming Chen^1,*, Guoqiang Ma², Ping Qi¹, Fucheng Wang¹, Lin Shen³, Xiaoya Pi¹
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072623 - 10 February 2026
Abstract In the image fusion field, fusing infrared images (IRIs) and visible images (VIs) excelled is a key area. The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image. Accordingly, efficiently combining the advantages of both images while overcoming their shortcomings is necessary. To handle this challenge, we developed an end-to-end IRI and VI fusion method based on frequency decomposition and enhancement. By applying concepts from frequency domain analysis, we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information… More >

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Haiyan Long¹, Gang Chen^2,*, Hai Chen^3,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073243 - 10 February 2026
Abstract The performance of deep recommendation models degrades significantly under data poisoning attacks. While adversarial training methods such as Vulnerability-Aware Training (VAT) enhance robustness by injecting perturbations into embeddings, they remain limited by coarse-grained noise and a static defense strategy, leaving models susceptible to adaptive attacks. This study proposes a novel framework, Self-Purification Data Sanitization (SPD), which integrates vulnerability-aware adversarial training with dynamic label correction. Specifically, SPD first identifies high-risk users through a fragility scoring mechanism, then applies self-purification by replacing suspicious interactions with model-predicted high-confidence labels during training. This closed-loop process continuously sanitizes the training More >

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Nouman Ahmad^1,*, Changsheng Zhang¹, Uroosa Sehar^2,3,4
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073029 - 10 February 2026
（This article belongs to the Special Issue: Utilizing and Securing Large Language Models for Cybersecurity and Beyond)
Abstract Although Named Entity Recognition (NER) in cybersecurity has historically concentrated on threat intelligence, vital security data can be found in a variety of sources, such as open-source intelligence and unprocessed tool outputs. When dealing with technical language, the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques. We introduce a three-phase approach for improved NER in multi-source cybersecurity data that makes use of large language models (LLMs). To ensure thorough entity coverage, our method starts with an identification module that uses dynamic prompting techniques. To lessen hallucinations, the extraction module uses… More >

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Xiaoyu Li^1,2, Jie Zhang³, Wen Shi^1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074005 - 10 February 2026
（This article belongs to the Special Issue: Advances in Large Models and Domain-specific Applications)
Abstract The emergence of large language models (LLMs) has brought about revolutionary social value. However, concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse. Consequently, a crucial research question arises: How can we differentiate between AI-generated and human-authored text? Existing detectors face some challenges, such as operating as black boxes, relying on supervised training, and being vulnerable to manipulation and misinformation. To tackle these challenges, we propose an innovative unsupervised white-box detection method that utilizes a “dual-driven verification mechanism” to achieve high-performance detection, even in the presence of obfuscated… More >

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Chih-Chieh Chang^*, Khairul Izyan Bin Anuar, Yu-Hwa Liu
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073177 - 10 February 2026
Abstract Small datasets are often challenging due to their limited sample size. This research introduces a novel solution to these problems: average linkage virtual sample generation (ALVSG). ALVSG leverages the underlying data structure to create virtual samples, which can be used to augment the original dataset. The ALVSG process consists of two steps. First, an average-linkage clustering technique is applied to the dataset to create a dendrogram. The dendrogram represents the hierarchical structure of the dataset, with each merging operation regarded as a linkage. Next, the linkages are combined into an average-based dataset, which serves as… More >

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Longcang Wang, Yongbing Gao^*, Xinguang Wang, Xin Liu
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072889 - 10 February 2026
（This article belongs to the Special Issue: Sentiment Analysis for Social Media Data: Lexicon-Based and Large Language Model Approaches)
Abstract Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods, this paper proposes the IPKE-MoE framework, which consists of three parts, namely, a sensitive word variant extraction framework, a sensitive word variant knowledge enhancement layer and a mixture-of-experts (MoE) classification layer. First, sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models (LLMs). Next, the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models. Specifically, after locating variants via n-gram… More >

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Ye-Chan Park¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn³, Jaesung Lee^4,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074141 - 10 February 2026
Abstract Legal case classification involves the categorization of legal documents into predefined categories, which facilitates legal information retrieval and case management. However, real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains. This leads to biased model performance, in the form of high accuracy for overrepresented categories and underperformance for minority classes. To address this issue, in this study, we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms from the perspective of the legal domain. This approach enhances More >

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Xiaorui Zhang^1,2,*, Yingying Wang², Wei Sun³, Shiyu Zhou², Haoming Zhang⁴, Pengpai Wang¹
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072626 - 10 February 2026
（This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)
Abstract Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition. However, existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds, small target objects, and limited training data, leading to poor recognition. Fine-grained images exhibit “small inter-class differences,” and while second-order feature encoding enhances discrimination, it often requires dual Convolutional Neural Networks (CNN), increasing training time and complexity. This study proposes a model integrating discriminative region localization and efficient second-order feature encoding. By ranking feature map channels via a fully connected layer, it selects high-importance channels to generate an More >

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Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026
Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >

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Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

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Danyang Zheng^*, Jinchen Xv, Xin Zhou, Zheng Shan
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073860 - 10 February 2026
Abstract The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity. In order to meet the needs of an increasing number of researchers, it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment. In this paper, we propose a novel quantum computing paradigm, Virtual QPU (VQPU), which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity. The proposal introduces More >

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Shahidatul Sadiah^1,#, Toru Nakanishi^2,#,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073774 - 10 February 2026
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract V2X communication enables vehicles to share real-time traffic and road-condition data, but binding messages to persistent identifiers enables location tracking. Furthermore, since forged reports from malicious vehicles can distort trust decisions and threaten road safety, privacy-preserving trust management is essential. Lu et al. previously presented BARS, an anonymous reputation mechanism founded on blockchain technology to establish a privacy-preserving trust architecture for V2X communication. In this system, reputation certificates without a vehicle identifier ensure anonymity, while two authorities jointly manage certificate issuance and reputation updates. However, the centralized certificate updates introduce scalability limitations, and the authorities… More >

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Zhen Wu^1,2, Hao Liu³, Linlin Zhang⁴, Zehui Zhang^5,*, Jie Wu¹, Haibin He¹, Bin Zhou⁶
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075156 - 10 February 2026
（This article belongs to the Special Issue: Integrating Split Learning with Tiny Models for Advanced Edge Computing Applications in the Internet of Vehicles)
Abstract Recently, Internet of Things (IoT) has been increasingly integrated into the automotive sector, enabling the development of diverse applications such as the Internet of Vehicles (IoV) and intelligent connected vehicles. Leveraging IoV technologies, operational data from core vehicle components can be collected and analyzed to construct fault diagnosis models, thereby enhancing vehicle safety. However, automakers often struggle to acquire sufficient fault data to support effective model training. To address this challenge, a robust and efficient federated learning method (REFL) is constructed for machinery fault diagnosis in collaborative IoV, which can organize multiple companies to collaboratively More >

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Enzo Hoummady^*, Fehmi Jaafar
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074897 - 10 February 2026
（This article belongs to the Special Issue: Intelligence and Security Enhancement for Internet of Things)
Abstract With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The… More >

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Jianing Wu¹, Yufeng Chen^1,*, Li Yin¹, Huajun He², Panshuan Jin²
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072937 - 10 February 2026
（This article belongs to the Special Issue: Intelligent Perception, Decision-making and Security Control for Unmanned Systems in Complex Environments)
Abstract To conduct marine surveys, multiple unmanned surface vessels (Multi-USV) with different capabilities perform collaborative mapping in multiple designated areas. This paper proposes a task allocation algorithm based on integer linear programming (ILP) with flow balance constraints, ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions. In the established grid map, a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme. It uses the greedy algorithm and the A* algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory More >

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Su Zhang¹, Song Deng^1,*, Qingsheng Liu²
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026
Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >

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Abdu Salam¹, Fahd M. Aldosari², Donia Y. Badawood³, Farhan Amin^4,*, Isabel de la Torre^5,*, Gerardo Mendez Mezquita⁶, Henry Fabian Gongora⁶
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073342 - 10 February 2026
（This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)
Abstract Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple… More >

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Fei Yu^1,2,3,*, Zhenya Diao^1,2, Hongrun Wu^1,2,*, Yingpin Chen^1,3, Xuewen Xia^1,2, Yuanxiang Li^2,3,4
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072593 - 10 February 2026
Abstract Feature selection serves as a critical preprocessing step in machine learning, focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms. Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges. However, there are inherent limitations in Particle Swarm Optimization, such as the delicate balance between exploration and exploitation, susceptibility to local optima, and suboptimal convergence rates, hinder its performance. To tackle these issues, this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization, tailored for wrapper-based feature selection. The… More >

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Gang Xu^1,2, Lefeng Wang¹, Yuwei Huang², Yong Lu³, Xin Liu⁴, Weijie Tan⁵, Zongpeng Li⁶, Xiu-Bo Chen^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074256 - 10 February 2026
Abstract The Intrusion Detection System (IDS) is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities. Clustering algorithms are often incorporated into IDS; however, conventional clustering-based methods face notable drawbacks, including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions. To overcome the performance limitations of existing methods, this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm (SC-QGA) and an improved quantum artificial bee colony algorithm hybrid K-means (IQABC-K). First, the SC-QGA algorithm is constructed based on… More >

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Noor Mueen Mohammed Ali Hayder^1,2, Seyed Amin Hosseini Seno^2,*, Hamid Noori², Davood Zabihzadeh³, Mehdi Ebady Manaa^4,5
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073236 - 10 February 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract Distributed Denial of Service (DDoS) attacks are one of the severe threats to network infrastructure, sometimes bypassing traditional diagnosis algorithms because of their evolving complexity. Present Machine Learning (ML) techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times. However, such techniques sometimes fail to capture complicated relations among various traffic flows. In this paper, we present a new multi-scale ensemble strategy given the Graph Neural Networks (GNNs) for improving DDoS detection. Our technique divides traffic into macro- and micro-level elements, letting various GNN models to get… More >

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Yunhao Yu¹, Fuhua Luo¹, Zhenyong Zhang^2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074277 - 10 February 2026
Abstract This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is… More >

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