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REVIEW

Jing-Wen Gao^1,2, Yunan He^1,*, Jian Liu^1,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084503 - 23 July 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice, 2nd Edition)
Abstract The unique topological properties of the electronic band structures in topological materials have increasingly attracted attention in both fundamental research and next-generation technological applications. With the rise of machine learning, the connection between topological materials and machine learning has deepened significantly. This review systematically summarizes the interaction between these two fields, tracing the history of their mutual promotion and synergistic development. We further examine the transformative impact of machine learning across multiple domains of topological materials, with a particular focus on recent progress in inverse design and generation of topological materials, topological superconductivity, and the More >

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REVIEW

Ahmed Ismail Ebada^1,2, Yasmeen Abu-Seif^2,*, Hrushikesh Pardeshi^2,*, Nesma El-Sayed¹
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081804 - 23 July 2026
（This article belongs to the Special Issue: Machine Learning for Robotics: Algorithms, Applications, and Emerging Trends)
Abstract The integration of Deep Learning, Deep Reinforcement Learning, and massive Vision-Language-Action (VLA) foundation models has catalysed a profound paradigm shift in robotics, transitioning systems from rigid automation to dynamic, open-world autonomy. Despite transformative breakthroughs in fields such as healthcare, ranging from adaptive robotic rehabilitation to autonomous surgical manipulation and silver care, widespread real-world deployment remains severely bottlenecked. This limitation primarily stems from the “Reality Gap” inherent to sim-to-real transfer and a fundamental epistemological tension: the stochastic, “black-box” nature of unconstrained neural networks fundamentally conflicts with the deterministic, zero-violation safety guarantees demanded by physical robotics. To… More >

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107

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REVIEW

Qiwu Wu¹, Tao Yang^2,*, Yunchen Su², Lingzhi Jiang³, Tao Tong²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082757 - 23 July 2026
Abstract Deep reinforcement learning (DRL) has become an important method in Unmanned Aerial Vehicle(UAV) path planning, but the field still lacks a dedicated bibliometric review that summarizes its publication patterns, intellectual structure, and thematic evolution. This study analyzes 1402 Web of Science publications from 2010 to 2025 using CiteSpace, VOSviewer, and the Bibliometrix R package. Three main findings are reported. First, the bibliometric evidence suggests a four-phase evolution of the field—foundational exploration (2015–2016), continuous-control breakthrough (2017–2019), multi-agent collaborative coordination (2020–2022), and complex-scenario integration (2023–2025)—as reflected in publication trends, keyword bursts, and co-citation clusters. Second, co-citation and keyword More >

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52

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REVIEW

Ferenc Erdős^1,*, Vijayakumar Varadarajan^2,3,4, Viorel-Costin Banţa⁵, Stephen Afrifa^6,7
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081005 - 23 July 2026
Abstract Standard retrieval-augmented generation (RAG) can perform poorly in AI for IT Operations (AIOps) settings because it is topology-blind. Basic RAG retrieves isolated, flat text snippets without enforcing structural or causal constraints, causing large language models to generate explanations that contradict the running system’s actual dependency structure. To address this gap, we conducted a systematic review following PRISMA 2020, searching Scopus, IEEE Xplore, Web of Science, and Google Scholar (last searched 31 January 2026). We included empirical or systems-oriented studies applying graph-based retrieval to ground a generative model in an IT, cloud, or software-operations setting, and… More >

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72

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ARTICLE

Seung-su Lee¹, Young-Been Noh¹, HwaYoung Jeong², Kwang-il Hwang^1,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081251 - 23 July 2026
Abstract Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation, viewpoint inconsistency, translation drift, and temporal misalignment. Existing contrastive skeleton learning methods primarily address action recognition and rarely integrate explicit geometric canonicalization for retrieval-oriented metric learning. This paper proposes a spatial-temporal normalized contrastive embedding framework that unifies structured nuisance suppression with scalable similarity representation learning. A four-stage normalization pipeline—torso-scale normalization, pelvis-centered alignment, posture-axis alignment, and phase-synchronized temporal resampling—removes geometric and temporal distortions prior to embedding. The normalized sequences are encoded using an acausal dilated temporal convolutional network trained with a hybrid More >

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22

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ARTICLE

Sara Tehsin¹, Tallha Akram^2,*, Syed Rameez Naqvi³, Meshal Alharbi⁴, Abdulrahman Alabduljabbar²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081304 - 23 July 2026
Abstract Transformers have become the dominant architecture for sequence modeling in natural language processing; however, their effectiveness critically depends on how positional information is encoded. Conventional positional encodings, while effective, may have limited structural flexibility for capturing complex global sequence relationships. Recent quantum-inspired approaches have sought to address this limitation, yet many either oversimplify quantum principles or introduce substantial computational or hardware overhead. We introduce a novel Quantum Fourier Transform (QFT)-inspired positional encoding scheme for transformers, motivated by the structured frequency representation of the QFT. Unlike prior approaches that either emulate quantum operations superficially or require… More >

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46

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ARTICLE

Junchen Huo¹, Song Wang^1,*, Enqing Chen¹, Yingqiang Ding¹, Shouyi Yang²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081658 - 23 July 2026
Abstract Multi-modal 3D object detection, which leverages the complementary strengths of LiDAR point clouds and camera RGB images, has emerged as a critical component of 3D perception in autonomous driving. As a critical challenge in multi-modal learning, modality alignment aims to establish accurate semantic correspondences across distinct modalities. However, existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured, such as in the presence of object occlusion or adverse environmental conditions, including illumination variations and inclement weather. To alleviate this issue, we present CG-MAE, a dual-branch Bird’s-Eye-View (BEV) masked autoencoder… More >

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25

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ARTICLE

Ziyi Ju¹, Ping Yu¹, Rui Song¹, Tonglin Chen^1,2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082060 - 23 July 2026
Abstract In modern high-performance chip design, achieving timing closure is essential to design success. With the increasing scale and complexity of modern chips, timing-driven placement has become increasingly important. Traditional placement methods primarily focus on minimizing wirelength, but lack timing optimization, making it difficult to meet the strict timing closure requirements of modern designs. Therefore, developing an efficient timing-driven placement method has become a critical challenge in modern chip design. This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder (VGAE) with a nonlinear mixed-size placement optimizer. The framework identifies timing-violation paths More >

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52

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ARTICLE

Zhuodong Liu¹, Xiangyu Li^2,*, Zhihao Zhang¹
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.085409 - 23 July 2026
（This article belongs to the Special Issue: Advanced Privacy Computing for Intelligent Distributed Networks and Systems)
Abstract Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU methods are designed for the FedAvg paradigm, where all clients share a single global model. In practice, personalized federated learning (pFL) methods such as FedPer, FedRep, Ditto, and FedBN have become widely adopted due to their superior handling of non-IID data. These methods decompose the model into shared global layers and client-specific personalized layers, fundamentally altering the semantics of unlearning, yet this setting has… More >

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ARTICLE

Jing Liu^1,2, Weijie Tan^1,2,3,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083401 - 23 July 2026
Abstract Due to its low hardware cost and ease of deployment, WiFi fingerprinting has become a prominent research direction in indoor positioning. However, traditional methods based on Received Signal Strength (RSS) still face three critical challenges: susceptibility to noise interference, low retrieval efficiency as fingerprint databases scale up, and trajectory instability in dynamic environments. These challenges are inherently rooted in the stochastic fluctuation of RSS signals, the high-dimensional and non-Euclidean nature of fingerprint space, and the unpredictability of user movement patterns. To address these limitations, an adaptive trajectory-assisted dynamic indoor positioning algorithm based on RSS fingerprinting,… More >

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ARTICLE

Shih-Lin Lin^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084571 - 23 July 2026
Abstract Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU More >

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26

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ARTICLE

Amir Ijaz^*, Hashem Haghbayan, Abdul Malik, Ethiopia Nigussie, Juha Plosila
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083797 - 23 July 2026
Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >

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49

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ARTICLE

Yuxuan Feng, Lili Liang^*, Guanglu Sun, Yanrui Wei
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081876 - 23 July 2026
Abstract In doped two-dimensional nanomaterials, magnetism is one of the important physical properties. By introducing foreign doping atoms or molecules, the electronic structure of the material can be effectively regulated, leading to changes in magnetic behavior. Currently, magnetic property prediction has achieved considerable results with the help of traditional CNNs, but there are still obvious limitations: (1) The feature extraction of dopant sites is constrained by fixed receptive fields, making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions; (2) CNNs lack the capability… More >

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49

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ARTICLE

Samir Dahmane¹, Mohammed Hadj Meliani¹, Mohamed Belabbas², Ismail Ouadha³, Mohammed Traiche¹, Noureddine Bouteldja^1,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083127 - 23 July 2026
Abstract Zinc substitution in Cd_1−xZn_xTe (CZT) alloys emerges as a powerful strategy for engineering their structural, elastic, mechanical, acoustic and thermal properties, thereby enhancing their potential for high-performance optoelectronic and radiation detection applications. In this work, a comprehensive first-principles investigation based on Density Functional Theory, within both the Generalized Gradient Approximation and the Local Density Approximation, is conducted to systematically explore the composition-dependent behavior of CZT across the full concentration range (0 ≤ × ≤ 1) in the cubic zinc-blende phase. The calculated elastic constants satisfy the Born stability criteria for all compositions, confirming the intrinsic… More >

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669

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ARTICLE

Jiafeng Liu^1,2, Junhao He¹, Binbin Deng¹, Jie Sun¹, Chenyu Shi^1,3, Shunhang Liang¹, Zicong Zhou^1,4, Guangsheng Feng¹, Jie Zhang^1,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083128 - 23 July 2026
Abstract The ability to achieve sufficient grasping force while maintaining conformal contact with objects is highly attractive for bio-inspired flexible robotic hands and grippers. In this paper, a flexible robotic hand design is developed inspired by the human hand, where the fingers have an embedded rigid phalanx wrapped in soft silicone rubber materials. A rigid-soft numerical model is developed to investigate the static contact behavior of a fingertip with a rigid flat using finite element (FE) analysis. The Ogden constitutive model is adopted to characterize the hyper-elastic behavior of the silicone rubber material and its parameters… More >

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ARTICLE

Haojie Zhu¹, Yuyang Zhang^1,2, Huiling Zhou¹, Yanxin Qiao^1,*, Haibing Zhang^3,*, Chengtao Li^4,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083975 - 23 July 2026
（This article belongs to the Special Issue: Mechanical Behavior of Materials with Advanced Modeling and Characterization)
Abstract In this study, the effects of temperature and corrosion product porosity on the micro-galvanic corrosion behavior of the β-Li phase in Mg-8Li alloy are systematically investigated using COMSOL Multiphysics numerical simulations. A two-dimensional micro-galvanic corrosion model incorporating mass transport, electrochemical reactions, and level set-based interface tracking is established to simulate the corrosion evolution over 72 h under varying temperature and porosity levels. The results indicate that temperature can significantly accelerate the corrosion process and the exchange current density increases exponentially. As the temperature increases from 35°C to 55°C, the electrolyte potential shifts negatively, and the maximum… More >

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0

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ARTICLE

Riya Sharma^1,*, Ashima Singh¹, Anju Bala¹, Mukesh Singh²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082720 - 23 July 2026
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional… More >

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143

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ARTICLE

Shasha Tian^1,2, Zhengyang Chen^1,3, Kai Ren^1,2, Na Li^1,2, Chongwei Ruan⁴, Zhijia Cui^1,3, Mian Wu^4,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081556 - 23 July 2026
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications, 2nd Edition)
Abstract To address the issues of low exploration efficiency and “geometric myopia” caused by the lack of high-level environmental structure modeling for mobile robots in complex indoor environments, this paper proposes an active SLAM object navigation method based on Situational Semantic Augmented Graph (SSAG). Unlike methods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association, this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions. First, an online room segmentation algorithm is employed to transform unstructured sensory data into a… More >

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95

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Hend Alshaya^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080758 - 23 July 2026
（This article belongs to the Special Issue: Advancing Action Recognition: Privacy, Explainability, and Optimization)
Abstract Video representation learning faces very challenging goals, including spurious temporal correlations, confounding visual features, and failure to learn real causal relationships between video events. Current transformer-based approaches learn statistical relationships rather than causal interactions, leading to weak generalization and high sensitivity to distribution changes. The current paper proposes a new Counterfactual Transformer Network, named CauFormer-V, that combines causal inference concepts with temporal representation learning for video. The framework was proposed and includes three main innovations, (1) a Causal Temporal Attention (CTA) mechanism, a mechanism that specifically models causal dependencies among video frames via do-calculus intervention,… More >

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ARTICLE

Xiaoyun Yuan^1,2,3,*, Zhengge Yi^1,2, Jingxi Zhang^1,2, Hairui Zhang³
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.072275 - 23 July 2026
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract Website Fingerprinting (WF) has emerged as a promising technique for identifying user access patterns to Hidden Services (HS). Despite growing interest in WF for HS, the absence of well-established foundations and systematic guidelines for feature selection undercuts the robustness of WF techniques in the face of concept drift. To address this gap, we present an empirical study focusing on feature resilience under concept drift in WF for HS. Specifically, we categorize features into network-specific and network-agnostic groups and quantify their information leakage potential via mutual information. We further assess each feature’s resilience to concept drift More >

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ARTICLE

Nusrat Yasmin Nadia¹, Md Habibul Arif², Habibor Rahman Rabby³, Md Iftekhar Monzur Tanvir¹, Md Jakir Hossen^4,*, M. F. Mridha⁵
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.074236 - 23 July 2026
Abstract Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand–Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)–based demand forecasting module with a mixed-integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost More >

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ARTICLE

Xiaorong Feng^1,2, Ying Gao^1,*, Leyu Shi²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082481 - 23 July 2026
Abstract Open source software has become a fundamental component of modern software ecosystems, supporting a wide range of critical applications in operating systems, cloud services, embedded systems, and security-sensitive infrastructures. However, the rapid growth of open source projects also brings increasingly serious security challenges. Many widely used C/C++ components still contain hidden vulnerabilities, and attackers are no longer limited to exploiting traditional memory-related bugs such as buffer overflows or use-after-free errors. In recent years, non-memory logic flaws, including improper authentication, incorrect state transitions, flawed boundary checks, and insecure API usage, have become more prevalent and more… More >

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128

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ARTICLE

Ying Cao¹, Penghui Zhao¹, Xinyu Qiao¹, Ningfan Zhan¹, Xiaomei Zou^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026
Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >

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Shaohuang Bian^1,#, Qinxiu Gao^1,#, Shan Su¹, Weifeng Wang¹, Feng Huang^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081818 - 23 July 2026
Abstract Tomato, as a globally important crop, its freshness directly affects postharvest quality, market value, and consumer acceptance. Traditional tomato freshness evaluation mainly relies on manual inspection and experience-based judgment, which is time-consuming, labor-intensive, and inefficient. Meanwhile, plasma technology has shown promising potential in agricultural preservation due to its safety and effectiveness, making the evaluation of tomato freshness after plasma treatment particularly important. In recent years, with the rapid development of deep learning technology, non-destructive detection methods based on image analysis have become important tools for agricultural product quality assessment. This study proposes an improved YOLOv8n-based… More >

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Akmalbek Abdusalomov¹, Kudratjon Zohirov², Azizbek Khojamurotov³, Furkat Safarov^3,4, Alpamis Kutlimuratov⁵, Jasur Sevinov^6,7, Zavqiddin Temirov⁸, Abror Buriboev^5,9,10, Heung Seok Jeon^11,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079237 - 23 July 2026
Abstract Medical texts are often complex and difficult to understand for non-specialists, creating barriers to effective communication in the clinical and rehabilitation fields. Although recent advances in natural language processing (NLP) have enabled automated text simplification, existing approaches often struggle to maintain medical accuracy and frequently result in factual inconsistencies or distortions. To address these issues, we propose the Neuro-Semantic Clinical Filter (NSCF), a novel NLP-based framework designed for clinically accurate simplification of medical texts. The proposed method integrates a Medical Concept Graph Encoder (MCGE) to incorporate structured domain knowledge, a Neuro-Symbolic Transformer (NSTR) for supervised… More >

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Yuanzhen Wang¹, Hongxin Zhang^2,3,*, Shaofei Sun¹, Yaqi Zhang², Xing Fang⁴, Zhi Sun²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081308 - 23 July 2026
Abstract The effectiveness of profiling deep learning side-channel attacks relies on the assumption that training and attack data follow the same distribution. However, when the profiling device differs from the target device, process-voltage-temperature (PVT) variations and clock jitter countermeasures cause distribution shifts in power traces, rendering models trained on the source device ineffective on the target. Existing domain adaptation methods typically rely on a single distributional constraint without jointly constraining kernel mean embeddings and covariance structure, thus limiting their effectiveness against strong defenses such as clock jitter. We propose Robust Feature Alignment for Side-Channel Analysis (RFA-SCA),… More >

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Israt Jahan¹, Afsana Begum¹, Bibhas Roy Chowdhury Piyas^1,*, Fahmid Al Farid^2,3,*, Fatama Jannat Tisha¹, Shahrin Islam¹, Abu Saleh Musa Miah⁴, Hezerul Abdul Karim^3,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081631 - 23 July 2026
Abstract Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Over-sampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We… More >

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Spandana Saggurthi¹, Anand Nayyar², Sk Hasane Ahammad¹, Sumendra Yogarayan^3,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080058 - 23 July 2026
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA (Low noise amplifier) in 22 nm FDSOI technology using NSGA-II and MOPSO algorithms. The objectives of the paper include simultaneous minimization of noise figure (NF) and power consumption while maximizing gain under matching and stability constraints. Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology, an optimization framework was created in Python, with the passive components LG, LS, LD, LOUT, and COUT chosen to be the variables optimized. More >

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Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026
Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

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Linyu Dong¹, Tao Li², Hao Li^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083042 - 23 July 2026
Abstract While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an S2 spatial injection path to recover critical high-resolution structural anchors, and introduces a frequency-selective interaction module to decouple target saliency from background noise. Experimental results More >

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Rashid Jahangir^1,*, Muhammad Asif Nauman², Oumaima Saidani³, Faisal Ramzan²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >

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Yun Liu¹, Jinghua Zhao¹, Liang Ma¹, Zijie Huang^2,3,*, Lizhi Cai^2,3, Jianxin Ge^2,3
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084179 - 23 July 2026
Abstract Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms, which often suffer from premature convergence and poor recall in sparse, complex API mapping spaces. To address this, we propose QIMIG, a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering. QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima. Simultaneously, its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings. Evaluated on 9 real-world migration rules derived from 57,447 open-source projects, QIMIG statistically significantly outperforms state-of-the-art baselines More >

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Pei Xie¹, Xiaoying Yang^1,*, Bo Li¹, Zhijie Pei¹, Fenghai Yang²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083100 - 23 July 2026
Abstract To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization, this paper develops a dynamic parallel machine scheduling model that accounts for stochastic machine failures and order priorities, thereby more accurately reflecting the uncertainties and complexities of real-world production environments. A dual-objective optimization framework is adopted to minimize both the makespan (maximum task completion time) and the variance of task completion times, aiming to improve the coordination and reliability of intra-order task delivery. An adaptive weighted reward function is designed to balance overall scheduling efficiency with consistency… More >

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Yasemin Onal^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082593 - 23 July 2026
Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >

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MengDie Hu^#, Na Wang^*, XueHui Du^#, BaiDong Huang^#, KaiYuan Wang^#
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082658 - 23 July 2026
Abstract With the rapid development of the Internet of Things (IoT) and edge intelligence, the volume of data generated by edge devices has grown explosively. Federated learning (FL), characterized by the paradigm of “data remaining local while models are shared,” has emerged as a key approach for adapting to the distributed architecture of edge computing, breaking down data silos, and enabling privacy preservation. However, its practical deployment in edge computing environments still faces significant challenges, including limited device resources and pronounced data heterogeneity. Existing pruning strategies for federated learning are predominantly based on static and single-design… More >

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Qiuhao Xu^1,2, Chen Wang^1,3,*, Xi Wen¹, Lurong Jiang¹, Wenying Zheng^4,*, Zhengkui Chen¹
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082310 - 23 July 2026
（This article belongs to the Special Issue: GenAI/AI in Biometric Recognition: Theoretical Foundations, Applications, and Emerging Challenges)
Abstract The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare. It enables more adaptive and intelligent human–machine interactions. Epilepsy, a common neurological disorder affecting millions worldwide, relies heavily on electroencephalography (EEG) signals for diagnosis and monitoring. Wearable consumer devices with EEG sensors support continuous physiological data collection. However, transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks. Federated learning (FL) provides a distributed training framework that keeps raw data on local devices. Despite this advantage, existing FL methods remain vulnerable to gradient leakage attacks, where adversaries may infer More >

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98

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Saken Tleuberdin¹, Dina Satybaldina^2,*, Raikhan Muratkhan³, Gulsipat Abisheva^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081906 - 23 July 2026
Abstract We perform a cross-layer penetration testing on one of the most popular Wi-Fi smart locks (Tuya 902V). The methodology combines wireless traffic analysis using an Alfa AWUS036AXML adapter, forced re-association via deauthentication to make Wi-Fi Protected Access 2 (WPA2) 4-way Extensible Authentication Protocol over LAN (EAPOL) handshake visible with Airodump/Aireplay, offline dictionary attack with Aircrack-ng, Android app reverse engineering using Apktool, Jadx, and MobSF; denial-of-service experiment (DoS) executed by hping3; Near-Field Communications (NFC)/Radio-Frequency Identification (RFID) key-clone attempt by Flipper Zero. Handshake is empirically captured but no Wi-Fi passphrase found under 14M dictionary entries; DoS test… More >

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Mohammad Q. Al-Jamal¹, Mahmoud Al Jamal², Bashar S. Khassawneh^3,*, Ayoub Alsarhan^4,5, Amina Salhi⁶, Tahani Alsubait⁷
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079984 - 23 July 2026
Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >

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Suchang Yang, Hongtao Yu^*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026
（This article belongs to the Special Issue: Dynamics, Control and Optimization in Complex Networks)
Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >

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Li Peng^1,2, Xiangbing Li^1,2, Kun Zou¹, Yong Liu^1,2,*, Haibo Huang¹
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082629 - 23 July 2026
（This article belongs to the Special Issue: Deep Learning for Next-Generation Cybersecurity: Architectures, Robustness and Applications)
Abstract In recent years, the transferability of adversarial examples has attracted significant attention. To improve the effectiveness of black-box attacks, a frequency-domain decay constraint is introduced, inspired by weight decay and regularization techniques commonly employed during model training. By treating adversarial perturbations as inputs in an optimization process, this constraint aims to mitigate the excessive reliance on low-frequency components during adversarial example generation, thereby enhancing transferability. Fourier heatmaps are utilized to analyze the sensitivity of input samples, enabling a decomposition of the frequency spectrum into low-frequency and high-frequency components. Based on this analysis, low-frequency attenuation is More >

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Atef Ibrahim^1,*, Fayez Gebali²
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081651 - 23 July 2026
Abstract The ongoing expansion of the Internet of Things (IoT) fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks. Nonetheless, vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption, a dilemma severely intensified by impending quantum computing capabilities. Defending these networks demands the integration of post-quantum cryptographic primitives; yet, the severe hardware constraints characterizing peripheral IoT components complicate practical deployment. Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations, largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance. Consequently,… More >

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51

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Olzhas Olzhayev¹, Bakhytzhan Kulambayev^2,*, Azizah Suliman³
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082618 - 23 July 2026
Abstract Automated road damage detection is a critical component of intelligent transportation systems, enabling efficient infrastructure maintenance and improved traffic safety. However, existing approaches often suffer from limited contextual understanding, insufficient segmentation accuracy, and suboptimal real-time performance. This study presents TCR-RoadNet, a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments. The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement (TCR) module to capture both fine-grained structural details and long-range spatial dependencies across feature scales. To further enhance performance, a Decoupled Detection Head (DDH)… More >

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Jialing Tao, Song Huang^*, Changyou Zheng^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080119 - 23 July 2026
Abstract Background: Jailbreak attacks, which use crafted prompts to bypass safety alignments of Large Language Models (LLMs) and generate harmful content, pose a significant security threat. Existing methods often optimize for a single objective (e.g., attack success rate), neglecting critical factors like query efficiency, which limits their practicality and generalization. Methods: We propose a Componentized Multi-Objective Optimization Framework (CMOOF), which introduces a paradigm shift: it searches for generalizable and query-efficient attack strategy templates within a structured, component-based strategy space. CMOOF leverages the NSGA-II algorithm to explicitly co-optimize two first-class objectives: Attack Success Rate (ASR) and Query More >

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Longhu Huang, Sheng Zheng^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082622 - 23 July 2026
Abstract In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation… More >

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Adnan Hnaif^1,*, Hanadi Al-Shawabkah², Ayman Alqafaan², Mohammad Alia¹
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082998 - 23 July 2026
Abstract Many fast pattern-matching mechanisms are used in NIDS (Network Intrusion Detection Systems) to filter higher volumes of network traffic prior to invoking expensive rule verification stages. This filtering phase in signature-based engines, such as Snort, needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware. Here, we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm (WEMA). WEMA performs the most relevant matching based on deterministic ordered indexing of category units, which eliminates chaotic control flow (which occurs with automata… More >

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67

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Mubariz Khan¹, Hafeez Ur Rehman Siddiqui², Adil Ali Saleem², Muhammad Amjad Raza^2,3, Lázaro Javier Hernández Rodríguez^4,5,6,7, Pablo Herrero García^4,8,9, Isabel de la Torre Díez^10,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026
Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >

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72

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Chien-Hao Tseng¹, Min-Yu Chen¹, Meng-Wei Lin¹, Jyh-Horng Wu¹, Chung-I Huang^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083337 - 23 July 2026
Abstract Context-aware driving assistance must do more than detect objects: it has to identify the cues that materially affect risk, separate observable evidence from inference, and produce recommendations that humans can audit. This paper presents a grounded multi-agent multimodal large language model (MLLM) framework for interpretable risk assessment in driving scenes. The framework decomposes reasoning into four stages—context relevance evaluation, visual interpretation, factual verification with anomaly extraction, and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description. We evaluate the… More >

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Betül Şenyayla¹, Aytuğ Onan^2,*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081260 - 23 July 2026
Abstract Large Language Models (LLMs) have become a cornerstone of modern natural language processing, achieving strong performance across diverse tasks. Despite these advances, their tendency to generate hallucinated or factually unsupported content remains a critical challenge for reliable deployment. Existing evaluation approaches predominantly rely on single-task settings and aggregate performance metrics, implicitly assuming that hallucination behavior is uniform across tasks. However, this assumption is fundamentally flawed, as hallucination characteristics vary significantly depending on task formulation, linguistic context, and evaluation criteria. To address these limitations, this paper proposes HalluBench, a task-aware multi-LLM benchmarking framework designed for systematic… More >

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71

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Jeonghyun Park, Hwanhee Lee^*
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081336 - 23 July 2026
Abstract Given a multi-turn conversational context and a raw user query, the goal of Conversational Query Reformulation (CQR) is to transform the query into a de-contextualized form that maximizes retrieval effectiveness for a downstream passage retriever. Conversational search seeks to retrieve relevant passages for the given questions in a conversational question answering system. Conversational Query Reformulation (CQR) improves conversational search by refining the original queries into de-contextualized forms to address issues such as omissions and coreferences. Previous CQR methods focus on imitating human-written queries, which may not always yield meaningful search results for the retriever. In… More >

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57

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Hangyu Hu¹, Liangrui Zhang¹, Xiaowei Huang¹, Xingmiao Yao^1,2,*, Youyang Qu³, Xia Wu¹, Guangmin Hu^1,2
CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081752 - 23 July 2026
Abstract With the evolution of information technology toward more advanced intelligence and automation, Security Orchestration, Automation, and Response (SOAR) has become a critical foundation for security incident handling, owing to its intelligent orchestration capabilities. Security playbooks, as the core mechanism for automated response in SOAR, require well-designed workflows and precise action matching to ensure efficient and accurate alert handling. However, with the rising sophistication of attacks and the expanding scale of security alerts, traditional expert-driven playbook recommendation approaches often degrade in recommendation quality or completely fail when existing playbook repositories cannot adequately cover unknown or novel… More >

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