Open Access

REVIEW

Milad Moradi^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086229 - 13 August 2026
（This article belongs to the Special Issue: Large Language Models: Foundations, Advances, and Emerging Applications)
Abstract Large Language Models (LLMs) have rapidly evolved into general-purpose systems with broad applicability across information access, reasoning, decision support, and human-computer interaction. Their growing deployment, however, has intensified concerns regarding safety, alignment, and robustness, especially as these models become integrated with external tools, retrieval systems, and increasingly agentic workflows. This review provides an analytical overview of the principal risks, technical advances, evaluation practices, and future directions in this area. It first clarifies the conceptual foundations of safety, alignment, robustness, and reliability in the context of LLMs. It then examines the major risk categories associated with More >

View

227

Download

43

Open Access

REVIEW

Lianpeng Li^1,*, Zhoujun Ruan¹, Zhichuang Wang², Haibo Zhang³, Hang Zhong⁴, Mingyang Li³, Chunpeng Kang⁵
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084092 - 13 August 2026
（This article belongs to the Special Issue: Robotics Vision and Thinking)
Abstract Endowing embodied intelligent robots with dexterous manipulation capabilities is paramount for executing complex, open-ended tasks. These capabilities are foundational to advancing true robotic autonomy, thereby facilitating precision assembly, seamless collaborative operations, and highly specialized maneuvers across diverse industrial and service sectors. Focusing on dynamic, unstructured environments where conventional programmed behaviors prove inadequate, this paper presents a systematic, quantitatively driven review of training methodologies and generation techniques for manipulation skill models within the domain of embodied artificial intelligence (AI). To provide rigorous trend validation, this study conducts a comprehensive bibliometric analysis and quantitative literature evaluation. By… More >

View

156

Download

31

Open Access

REVIEW

Mukesh Dalal^1,*, Anterpreet Kaur Bedi¹, Payal Mittal²
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086270 - 13 August 2026
（This article belongs to the Special Issue: Vision, LiDAR, and Sensor Fusion-Based SLAM for Autonomous Navigation)
Abstract Autonomous navigation poses a key challenge in Artificial Intelligence (AI), necessitating agents to plan and execute actions in complex, partially visible surroundings. Simultaneous Localization and Mapping (SLAM) facilitates autonomous navigation of robots and vehicle objects to construct an unfamiliar environment map while concurrently monitoring their inside position. This systematic review investigates the nascent convergence of agentic AI, defined by goal-oriented autonomy, with adaptive decision-making and reasoning, with SLAM-based navigation systems. This paper utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, which concentrated on peer-reviewed articles published in (2017–2026), particularly in SLAM-based intelligent… More >

View

159

Download

31

Open Access

REVIEW

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026
Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… More >

View

905

Download

124

Open Access

REVIEW

Xiruo Chen, Qi Ouyang^*, Sihong Meng, Yuke Meng
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086341 - 13 August 2026
Abstract Visual simultaneous localization and mapping (VSLAM) is a key technology for mobile robotics, autonomous driving, and embodied intelligence, enabling self-localization, environment reconstruction, and scene understanding. Although conventional geometric methods have achieved notable success, their performance often degrades in challenging conditions, such as low-texture scenes, severe illumination changes, dynamic interference, and long-term environmental variations. Recent advances in deep learning have created new opportunities to improve VSLAM through stronger feature representations, learned priors, semantic perception, and emerging map representations. At the same time, the increasing adoption of learning-based modules has raised important questions about integration strategies, generalization,… More >

View

191

Download

50

Open Access

REVIEW

Yaolei Wang¹, Wangyan Li^1,*, Guoliang Wei²
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085054 - 13 August 2026
Abstract With the rapid development of unmanned aerial vehicle (UAV) technologies, simultaneous localization and mapping (SLAM) has emerged as a key enabling paradigm for autonomous navigation and environmental perception. This paper presents a comprehensive survey of recent trends in UAV-based SLAM. First, we review the fundamental components of UAV-based SLAM systems, including commonly used onboard sensors and front-end odometry methods such as visual odometry, visual-inertial odometry, and LiDAR-inertial odometry, which provide reliable ego-motion estimation. Next, we summarize back-end methodologies that enhance estimation accuracy and global consistency, covering pose graph optimization, 3D reconstruction techniques, filter-based SLAM, fusion-based multi-UAV SLAM, More >

View

299

Download

69

Open Access

REVIEW

Hyunbum Kim^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085773 - 13 August 2026
Abstract Recently, unmanned aerial vehicles (UAVs), or drones, have attracted considerable research interest across diverse fields, encompassing public and private domains, industrial and academic fields, transportation areas, disaster and harsh environments, reliable delivery services, digital twin-enabled space, and smart cities. In particular, UAVs play a critical role in surveillance and security applications. In this paper, we investigate recent advances in surveillance and intelligent security applications using UAVs. This study covers a wide range of practical tasks and missions including intelligent traffic monitoring, disaster environments, forests and national parks, large-scale events and patrols in public circumstances. Also, More >

View

128

Download

31

Open Access

REVIEW

Van-Thuan Nguyen^1,2, Van-Nui Nguyen², Van-Hung Le^3,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081249 - 13 August 2026
Abstract The vision-language models (VLM) combine the image and text to solve practical applications. Specifically, VLM leverages the results of computer vision in conjunction with natural language processing (NLP), like a large language model (LLM), to address real-world problems such as automating and improving the quality of medical examinations and treatments in healthcare, building autonomous driving systems, image captioning, and generating automated chatbots. To understand the development and application of VLM, we surveyed VLM, classifying it according to model architecture, learning methods, evaluation measures, datasets, challenges, and future development directions of VLM based on the model… More >

View

139

Download

31

Open Access

REVIEW

Bareera Anam, Muhammad Asim, Muhammad Nadeem Ali, Byung-Seo Kim^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084949 - 13 August 2026
Abstract The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport… More >

View

153

Download

29

Open Access

REVIEW

Hsiao-Chun Han¹, Der-Chen Huang^2,*, Chin-Ling Chen^3,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084216 - 13 August 2026
Abstract Semiconductors represent the most complex production activities and stand at the forefront of smart manufacturing. In particular, the yield of advanced processes is strongly influenced by coupling among engineering systems, material behavior, and computational infrastructure. Consequently, the integration of deep learning (DL) and digital twins has become essential for driving the next-generation transformation of smart manufacturing. However, existing reviews predominantly organize literature through algorithm-oriented taxonomies, while isolated AI paradigms alone remain insufficient to effectively capture system-level interactions and industry-driven technological evolution. Therefore, this study proposes a system-oriented and industry-driven review framework, termed the System-under-Industry Guided… More >

View

196

Download

47

Open Access

ARTICLE

Carlos Rosa-Remedios^*, Pino Caballero-Gil^*, Jezabel Molina-Gil
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084352 - 13 August 2026
（This article belongs to the Special Issue: Innovation in Quantum Computing for Cybersecurity Applications)
Abstract Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through… More >

View

207

Download

47

Open Access

ARTICLE

Jiayi Qiu¹, Youle Wang^1,*, Lei Zhang^1,2,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084061 - 13 August 2026
Abstract The design of steel and alloy materials is of critical importance across a wide range of industrial applications; however, effective intelligent agent-based assistants for this domain remain limited. To address this gap, we introduce STALAgent, a large language model (LLM)-based multi-agent system specifically tailored for intelligent and automated design of steel and alloy materials. STALAgent is centered on an LLM brain with several key agents (e.g., task assignment, semantic search, inverse design, and heat treatment simulation) that collectively form a closed-loop workflow from user query to material recommendation. This system leverages a CrewAI-based orchestrator to More >

View

135

Download

26

Open Access

ARTICLE

Xiyue Zhang¹, Feizhou Li^1,*, Zhihai Hu¹, Weiliang Zhang¹, Xindang He², Gexia Yuan¹, Yanwei Feng³, Yafeng Qi^4,5,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084077 - 13 August 2026
Abstract Three-dimensional (3D) braided composites are widely used in aerospace and automotive industries due to their superior mechanical properties. However, traditional 3D four-directional or five-directional braided composites exhibit limitations in multi-axial load-bearing capacity and structural stability under complex stress conditions. To address these challenges, we propose a novel 3D seven-directional braided composite structure, which enhances mechanical performance in both axial and transverse directions by incorporating additional reinforcement yarns. This structure consists of braiding yarns, axial yarns, six-directional yarns and seven-directional yarns, forming a more uniform and stable interlacing network. Based on the positional relationships between yarns, More >

View

192

Download

39

Open Access

ARTICLE

Gennadiy Lvov¹, Maria Tănase^2,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084754 - 13 August 2026
Abstract Vibrations occurring in the impellers of centrifugal compressors are among the primary factors that influence the reliability and service life of main gas pumping station units. A promising approach to improving the dynamic performance of centrifugal compressors is the use of modern metal-matrix composite materials for impellers. This article presents a prediction of the dynamic behavior of an impeller under actual operating conditions with the aim of eliminating resonance vibrations. Detailed geometric and finite-element modeling enabled an investigation of the prestressed state caused by centrifugal forces on the natural frequency spectrum of the impeller. To More >

View

176

Download

34

Open Access

ARTICLE

Elena S. Kartashynska^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082702 - 13 August 2026
Abstract The discovery of graphene and its unique physicochemical properties has catalyzed intensive research into alternative two-dimensional (2D) materials, with a view to their prospective applications in diverse fields of physics, chemistry, and materials science. In this context, there is a notable scientific interest in developing computationally efficient theoretical approaches capable of reliably estimating key parameters of organic films deposited on 2D surfaces. This objective necessitates a rigorous selection and validation of appropriate computational methods, ensuring an optimal balance between computational cost and predictive accuracy. This study presents a method for evaluating the thermodynamic and structural… More >

View

139

Download

41

Open Access

ARTICLE

Zulfiqar Ali, Azhar Mahmood^*, Shaheen Khatoon, Seyed Ali Ghorashi
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084386 - 13 August 2026
（This article belongs to the Special Issue: Secure and Scalable Blockchain–IoT Architectures for Next-Generation Distributed Systems)
Abstract Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and… More >

View

163

Download

34

Open Access

ARTICLE

David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026
（This article belongs to the Special Issue: Deep Learning for Emotion Recognition)
Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >

View

319

Download

51

Open Access

ARTICLE

Jiyeong Park¹, Sercan Yeşilköy¹, Doyeon Lim¹, Huiryeong Park¹, Eunseo Lee¹, Mohsen Ali Alawami^1,*, Ki-Woong Park^2,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081091 - 13 August 2026
Abstract The recent increase in deepfake content has significantly increased cyber threats. Although numerous deepfake detection technologies have achieved high accuracy, there are limits to clarifying the rationale behind their detection decisions. To bridge the gap, in our study, we leverage the combination of Explainable Artificial Intelligence (XAI) and Large Language Models (LLMs) to deliver clear, consistent, and understandable interpretations of deepfake detection outcomes. To do that, we integrate XAI and LLMs to visually represent detection rationales and automatically generate coherent natural-language explanations. During the implementation of our method, we developed a multi-task learning framework based… More >

View

423

Download

92

Open Access

ARTICLE

Nourah Fahad Janbi^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084270 - 13 August 2026
Abstract Automatic pain assessment from speech is an emerging approach for non-invasive and objective healthcare monitoring. However, many existing methods are evaluated on single datasets or under controlled conditions, which limits their robustness under diverse real-world conditions. This paper presents a lightweight, end-to-end deep learning framework for speech-based pain level classification that explicitly targets multi-dataset robustness assessment. The proposed approach uses log-Mel spectrograms with an EfficientNetV2B0 backbone to learn discriminative acoustic features without relying on handcrafted feature engineering or multi-stage pipelines. The model is evaluated on heterogeneous datasets, including clinical recordings, controlled experimental data, and a More >

View

251

Download

51

Open Access

ARTICLE

Maher Alharby^1,*, Ali Alssaiari^2,3
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084639 - 13 August 2026
Abstract Large Language Models (LLMs) are increasingly being used to generate source code. However, a substantial proportion of their output contains security vulnerabilities. Existing defenses typically apply uniform analysis to all code fragments, irrespective of their risk profiles. This study presents RAVE-Code, a three-layer framework that calibrates the verification effort based on the risk associated with each detected weakness. The Detection layer employs Bandit for pattern-based static analysis, annotating findings with their respective Common Weakness Enumeration (CWE) classes. The Risk Scoring layer calculates a composite risk score for each weakness instance by integrating the Common Vulnerability… More >

View

269

Download

58

Open Access

ARTICLE

Guan Yang¹, Shiyan Kang¹, Bo Chen^2,3, Yu Wang^4,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084499 - 13 August 2026
Abstract Web honeypots serve as foundational technologies for active deception, attracting attackers and extracting actionable threat intelligence. To address the challenges associated with manual and labor-intensive frontend construction, this paper presents the HFG framework, a security-oriented frontend generation framework designed for the large-scale deployment of heterogeneous Web-service decoy nodes. HFG utilizes a vision-to-code architecture integrating a PVT-CoT visual encoder, multi-scale adaptive fusion, visual token compression, a visual prefix bridge, and a Qwen2-LoRA code decoder. The model is trained on WebSight-derived data and evaluated on both the WebSight-derived test set and the Design2Code benchmark. General reconstruction metrics,… More >

View

250

Download

84

Open Access

ARTICLE

Ying Yan¹, Yongqiang Yang², Cong Jiang³, Bin Suo³, Kai Sun^4,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084652 - 13 August 2026
Abstract Degradation data in practical reliability engineering are often scarce and heterogeneous, originating from multiple sources with varying degrees of uncertainty and conflict. Accordingly, this study proposes a hybrid framework that integrates Dempster–Shafer (D-S) evidence theory with the Wiener process for small-sample reliability assessment using multi-source heterogeneous data. First, a probabilistic non-uniform sampling method regularizes varied data sources and computes basic probability assignments (BPA). Second, a weight synthesis mechanism is constructed, where prior weights derived from prior knowledge are updated by evidence similarity quantified through the Expectation–Width (EW) distance, yielding posterior weights. Quantile sequences from each More >

View

197

Download

45

Open Access

ARTICLE

Shunran Duan, Meijuan Yin^*, Xiangyang Luo, Lunchong Cui, Chenyu Wang
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084290 - 13 August 2026
Abstract Few-shot relation classification aims to identify semantic relations between entity pairs under limited annotated data. Although recent prompt learning-based methods have achieved promising performance, they often rely on manually crafted, domain-specific prompt templates, which restrict their transferability across domains. In this paper, building upon the multi-task prompt transfer paradigm of MPT, we propose a Confidence-guided Orthogonality-constraint Prompt Adaptation framework for few-shot relation classification, named COPA. The proposed framework learns a shared domain-invariant prompt matrix together with domain-specific low-rank prompt matrices via multi-domain soft prompt tuning, enabling the transfer of domain-invariant relational knowledge across domains. Unlike… More >

View

256

Download

58

Open Access

ARTICLE

Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026
Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

View

132

Download

29

Open Access

ARTICLE

Jixin Xu, Xingxin Li^*, Senlin Zhu, Qingqing Song
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083290 - 13 August 2026
Abstract Complex procedural workflows in maintenance and parametric design often fail due to violated long-range dependencies, unsatisfied preconditions, and inconsistent parameter bindings. In such workflows, an early structural deviation may propagate silently and eventually induce global failure. Existing workflow auditing methods, particularly those based on large language models (LLMs) or sequence matching, often rely on implicit reasoning, exhibit unstable outputs, and provide limited support for reproducible error localization. To address these limitations, we propose a verification-centric auditing framework that couples an explicit typed dependency graph with an executable state transition system. The dependency graph represents data… More >

View

250

Download

60

Open Access

ARTICLE

Peixuan Wang¹, Lingyun Yuan^1,2,*, Yi Xiang¹, Tianyu Xie^1,2, Haochen Bao¹, Kexin Wang^1,2
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083887 - 13 August 2026
Abstract With the development of the Internet of Things (IoT), there is a rising demand for ciphertext retrieval. However, existing searchable encryption schemes mainly support single-modal retrieval, while current cross-modal searchable encryption methods often suffer from high computational overhead and lack reliable result verification. To address these problems, we propose a cross-modal searchable encryption scheme with result verification (VCMSE). First, we design a cross-modal hash extraction method that combines contrastive learning with a residual similarity matrix to generate encryption-friendly binary features with enhanced semantic consistency. Second, we designed a lightweight garbled circuit-based matching mechanism that enables More >

View

212

Download

55

Open Access

ARTICLE

Chao Zhang, Yunfeng Dong^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083353 - 13 August 2026
Abstract The missile warning constellation is fundamental to national territorial security and has significant strategic and military value. This study proposes a two-stage, nested co-optimization framework with adaptive evolutionary operators, termed TNC-A, to address challenges in genetic representation, evaluation distortion, the curse of dimensionality, and search inefficiency within the co-optimization of component-level constellation morphology and mission planning. A hybrid encoding scheme combining tree-structured and real-valued vector representations was adopted to encode all optimization variables, including constellation configuration, component-level unified platform information, and mission planning parameters. Second, a multi-stage optimization strategy integrated with a double-nested structure was More >

View

258

Download

62

Open Access

ARTICLE

Guan Yang¹, Haozhen Wang², Yu Wang^3,*, Weiguang Liu⁴, Bo Chen^5,6
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083419 - 13 August 2026
Abstract The rapid proliferation of encrypted communication technologies, such as TLS, VPNs, and Tor, has significantly limited the effectiveness of traditional traffic classification methods that rely on port numbers or deep packet inspection. While handcrafted statistical features provide partial solutions, they often lack robustness and generalization in complex traffic scenarios. Although deep learning models such as CNNs and RNNs can capture local and sequential patterns, they typically overlook higher-order structural dependencies among bytes. To address these challenges, we propose DVG-GNN, a Dual-View Graph representation learning framework for encrypted traffic classification. The framework decomposes each packet into More >

View

239

Download

73

Open Access

ARTICLE

Muhammad Mujahid¹, Fatima Alshannaq¹, Shaha Al-Otaibi², Tanzila Saba^1,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084273 - 13 August 2026
（This article belongs to the Special Issue: Advances in Intrusion Detection and Prevention Systems)
Abstract Cybersecurity threats in Internet of Things (IoT) networks have escalated, enabled by rapid advancements in wireless communication and edge computing technologies. These advancements expose networks to a wide range of sophisticated and evolving threats and increasingly complex research challenges. Traditional Intrusion Detection and Prevention Systems (IDS/IPS) often fail to provide reliable performance regarding the flexibility and scalability required to handle evolving attack patterns. This study proposes an APENet approach to detect cyberattacks from a real-world cybersecurity dataset, and incorporated a contextual dependency mechanism. The approach captures both local transition dependencies and global relational interactions within… More >

View

298

Download

61

Open Access

ARTICLE

Hao Chu, Xibin Xiao, Song Gao, Chao Zheng, Fei Wang^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083466 - 13 August 2026
Abstract In recent years, embodied intelligence has become an important research direction. Slip detection is a critical challenge in dexterous robotic manipulation, especially in manipulating deformable objects such as soft fruits. Visual–tactile fusion can provide rich sensory information, but it also introduces significant modality differences. We propose STR-CMFNet, a novel network framework for visual–tactile slip detection. The framework adopts a two-stage design. It consists of Spatio-Temporal Rectification (STR) and Cross-Modal Fusion (CMF). The STR applies temporal attention weights to recalibrate key-frame features. It also refines spatial feature maps. Based on the corrected features, the CMF module… More >

View

193

Download

56

Open Access

ARTICLE

Weijun Gao, Ziyang Zhang^*, Maotang Su
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085382 - 13 August 2026
Abstract Multimodal sentiment analysis (MSA) has made significant progress in integrating heterogeneous information from text, speech, and vision. However, real-world multimodal data often suffer from modality noise, semantic inconsistency, and incomplete modality information, which can weaken cross-modal fusion and reduce the reliability of sentiment prediction. To address these challenges, this paper proposes RUAL, a robust uncertainty-aware learning framework for multimodal sentiment analysis. Specifically, RUAL first employs a Gathered Multi-Head Attention Pooling (GMHA) module to aggregate intra-modal features and estimate modality uncertainty based on attention entropy. Then, an Uncertainty-Aware Cross-Modal Coupled Layer (UACCL) is introduced to dynamically More >

View

300

Download

57

Open Access

ARTICLE

Tallha Akram^1,*, Sadiq Ahmad^2,*, Meshal Alharbi³
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081458 - 13 August 2026
Abstract Scalability limitations, privacy risks, and lack of adaptability remain key challenges in centralized medical digital win (MDT) architectures. While federated learning (FL) mitigates the need to share raw data, it often lacks adaptability to dynamic clinical environments and does not fully integrate formal privacy guarantees into the learning process. To address these challenges, this paper proposes a decentralized, federated, multi-agent reinforcement learning (F-MARL) framework to coordinate MDTs in the presence of partial observability. The framework is formulated as a multi-agent partially observable Markov decision process (MA-POMDP), enabling distributed policy optimization in heterogeneous and uncertain clinical… More >

View

214

Download

48

Open Access

ARTICLE

Rui Yan¹, Botao Fan², Xueqi Qu², Cuihuan Ren^1,*, Xu Zhou^1,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085749 - 13 August 2026
Abstract Accurate prediction of coal spontaneous combustion (CSC) temperatures is crucial for safe coal mine production. To further improve the accuracy of CSC temperature prediction and the interpretability of the model, this study proposes an interpretable Chebyshev chaotic mapping-Lévy flight-Enhanced pinhole imaging inverse learning-Adaptive weighted Osprey Optimization Algorithm optimized Bidirectional Long Short-Term Memory (CLEA-OOA-BiLSTM) framework for predicting CSC temperatures. First, we optimized the Osprey Optimization Algorithm (OOA) by incorporating the Chebyshev chaotic map, Lévy flights, enhanced pinhole imaging backpropagation, and an adaptive weighting strategy, thereby developing the CLEA-OOA algorithm. Through comparative experiments using eight benchmark test… More >

View

246

Download

58

Open Access

ARTICLE

Tahani Alsubait^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083550 - 13 August 2026
（This article belongs to the Special Issue: Intelligent Anomaly Detection Solutions for Advanced Environments)
Abstract The recent sophistication of contemporary cyber threats, such as advanced persistent threats (APTs), zero-day exploits, and polymorphic malware, has revealed serious limitations of traditional rule-based and shallow machine learning detection systems. This paper introduces a new self-managed cyber threat detection and response model, CyberSentinel-LLM, that leverages a fine-tuned large language model (LLM) and a multi-agent reinforcement learning system. The framework employs a LoRA-adapted LLaMA-3-8B backbone (fine-tuned on domain-specific cybersecurity log data using Low-Rank Adaptation with rank r = 16) for contextual log analysis, semantic threat classification, and automated incident response through four specialised agents: Detection,… More >

View

213

Download

53

Open Access

ARTICLE

Yuanzhao Shang¹, Xin Liu^1,2,*, Fengbiao Zan¹
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084688 - 13 August 2026
Abstract Multivariate time-series anomaly detection is widely used to identify abnormal operating patterns in complex monitoring systems. Exogenous or contextual inputs can provide useful information for anomaly detection, but their effects on endogenous variables may appear with temporal delays. Direct synchronous modeling is therefore insufficient for capturing delayed response patterns. This study proposes EMS-uDTWAD, a multi-scale anomaly detection framework that combines explicit lag alignment with uncertainty-aware normal-pattern matching. First, the time-series data are divided into fine-grained and coarse-grained windows. At the coarse-grained scale, a lag alignment module estimates delayed responses from exogenous or contextual inputs to… More >

View

196

Download

40

Open Access

ARTICLE

Sayfudin Sayfudin^1,2, Deris Stiawan^3,*, Ferdiansyah Ferdiansyah⁴, Rahmat Budiarto⁵
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084177 - 13 August 2026
Abstract Tightening global regulation of digital toxicity demands hate-speech detection that is accurate, explainable, traceable, and forensically usable. The challenge intensifies in multilingual and code-mixed settings such as Indonesian social media, where linguistic variation and informal expressions cause feature sparsity and reduce machine learning (ML) effectiveness. Most prior work emphasizes text classification while neglecting actor profiling and the network structures through which hate speech propagates. We propose Dynamic Lexicon-Driven Network (DyLex-Net), an integrated framework for profiling actors who disseminate hate speech, combining dataset-driven dynamic-lexicon analysis, classical ML ensemble validation, and ego-network analysis under a forensic-readiness orientation.… More >

View

217

Download

46

Open Access

ARTICLE

Isil Karabey Aksakalli^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083166 - 13 August 2026
Abstract Open-source platforms and issue tracking systems such as GitHub and Jira generate large volumes of issue reports and code changes, making effective bug identification a challenging task. This study investigates software bug prediction by integrating various feature extraction methods, including Word2Vec, TF-IDF, FastText, GloVe, and Doc2Vec, with several lightweight ML algorithms. Hybrid feature sets are further enhanced using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) and empirical results indicate that Word2Vec and Multi-Layer Perceptron (MLP) provide comparatively stronger performance. The study proposes a Hybrid Attention-Residual Multilayer Perceptron (HAR-MLP) model to automatically classify software… More >

View

232

Download

42

Open Access

ARTICLE

Kaisen Li¹, Yunwei Zhang^1,*, Guoying Sun¹, Bin Li^2,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084755 - 13 August 2026
Abstract With the development of vision-language pre-trained models, effectively exploiting high-level semantics and precisely controlling bitrate in learned image compression remains a challenging problem. Existing methods mainly rely on image feature modeling alone, making it difficult to jointly preserve fine-grained details and semantic consistency under a given bitrate budget. To address this issue, this paper proposes a learned image compression framework that integrates text-semantic guidance with content-aware bitrate control. The framework combines Bootstrapping Language-Image Pre-training (BLIP) and Contrastive Language-Image Pre-training (CLIP) to extract image semantic information, and performs conditional modulation on multi-scale visual features through feature-wise… More >

View

238

Download

43

Open Access

ARTICLE

Abdulhamid Victor Ibrahim, Haoyuan Li, Bingyang Guo, Ruiyun Yu^*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083186 - 13 August 2026
Abstract The detection of small objects in low-resolution aerial imagery presents a persistent challenge in computer vision, where hardware constraints, imaging altitude, and scene complexity collectively degrade spatial detail to the point where standard detection frameworks fail. Existing super-resolution methods offer partial remedies but are limited by substantial computational costs and by feature discrepancies between Generative Adversarial Network-enhanced and real high-resolution images that degrade downstream detection accuracy. This paper presents YOLO-Flex, a unified framework that addresses these challenges through the co-design of a super-resolution module and a task-adapted object detection network, jointly optimized through a shared… More >

Graphic Abstract

View

146

Download

27

Open Access

ARTICLE

Yuwei Lu^1,2, Jia Liu^1,2,*, Qiya Wang^1,2, Yujie Liu^1,2, Peng Luo^1,2
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084506 - 13 August 2026
Abstract Existing deep-learning-based steganography methods are typically designed for single-modality cover data and often rely on modality-specific network structures, which limits their cross-modal adaptability. To address this limitation, this paper proposes a multimodal implicit neural representation (INR) steganographic framework based on a point-cloud intermediate representation. The framework first fits the cover data as a carrier INR and samples the fitted carrier into a noisy point cloud. A pre-shared noise seed and secret key are then used to reproduce the carrier-derived point cloud and select a key-dependent point subset as the secret point cloud. Finally, a separate… More >

View

168

Download

42

Open Access

ARTICLE

Muhammad Naeem Zafar¹, Yunfei Yin^1,*, Junaid Abbas², Bayan Alabdullah³, Khaled Alnowaiser⁴, Yunyoung Nam⁵, Zepa Yang^5,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085136 - 13 August 2026
Abstract Accurate medical image classification increasingly relies on the joint modeling of global contextual semantics and fine-grained local structural cues, since many lesions are only reliably recognized when subtle local details are interpreted within their broader anatomical context. However, most recent hybrid CNN–Transformer and global–local frameworks still extract these features in separate streams and merge them only through late-stage static fusion, without explicit bidirectional interaction during representation learning. As a result, global context cannot effectively guide the refinement of subtle local structures, and local discriminative cues cannot recalibrate higher-level semantic reasoning before classification, which limits reciprocal… More >

View

284

Download

73

Open Access

ARTICLE

Diana Maria Popa, Simona-Vasilica Oprea^*, Adela Bâra
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084792 - 13 August 2026
Abstract This paper investigates how generative-artificial intelligence (AI) is influencing job requirements, skill compositions and sectoral dynamics across global labor markets. It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive component in the workplace. A large-scale, multi-source corpus of over 150,000 English-language job postings (2018–2025) is compiled from twelve open-access datasets and one public API. The analytical framework integrates lexical skill extraction, semantic framing, topic modeling and time-series forecasting. Skill mentions are categorized into five dimensions: AI_Data, Routine, Soft_Meta, Domain_Specific and Leadership,… More >

View

300

Download

53

Open Access

ARTICLE

Tuan Nguyen Kim^1,*, Son Doan Trung¹, Nguyen Minh Nhut Pham²
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084268 - 13 August 2026
（This article belongs to the Special Issue: Malware Analysis, Forensics, and Detection Using Artificial Intelligence)
Abstract Static Windows Portable Executable (PE) malware detection remains a significant challenge due to the growing use of packing, obfuscation, and code reuse techniques, which gradually reduce the effectiveness of signature-based and manually engineered feature approaches. Recent deep learning models that operate directly on binary code or static features have achieved encouraging results; however, most still rely on global file-level representations. Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions. Moreover, many multi-view methods process different feature sources independently, lacking mechanisms to enforce semantic consistency across views. This… More >

View

238

Download

47

Open Access

ARTICLE

Jiaying Li¹, Xiujuan Wang^1,*, Shuhan Han², Liya Xu¹, Changxing Wang¹
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082631 - 13 August 2026
Abstract Deep neural networks are widely applied in computer vision tasks but remain highly vulnerable to adversarial attacks. Tiny and imperceptible perturbations can cause severe model misclassification. Most existing defense methods improve robustness but significantly reduce model accuracy on clean examples. To address this issue, we propose a defense framework combining pixel value transformation and spatial transformation. The proposed method divides the input image into two complementary regions. Feature compression is applied to one region to reduce model sensitivity to subtle perturbations. Intense reversible pixel transformation is applied to the other region to disrupt the spatial… More >

View

190

Download

43

Open Access

ARTICLE

Qisen Jin^1,2, Xiaoping Wang¹, Feng Zhang², Yu Zeng², Jia Guo^3,4,5,*, Jiacheng Li^6,*
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080077 - 13 August 2026
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract This paper presents a Sine-Lucas Oscillation Particle Swarm Optimization (SLOPSO) XGBoost algorithm for landslide susceptibility prediction. SLOPSO extends canonical PSO by introducing an oscillation factor constructed from a sine function and the Lucas number sequence into the position update, which raises swarm diversity and improves the global search. SLOPSO is then applied to tune six XGBoost hyperparameters using K-fold cross-validation accuracy as the fitness function. In experimental evaluation, SLOPSO-XGBoost reaches a mean test Accuracy of 0.8113, F1 of 0.8182, and AUC of 0.8856 over 10 independent runs, outperforming standard PSO-XGBoost and the default Random Forest, More >

View

199

Download

40

Open Access

ARTICLE

Caiyun Li^1,#, Xiaowen Liu^1,#, Binhui Tang^1,*, Tingting Lu², Daibo Xiao³, Li Chen³
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086522 - 13 August 2026
Abstract Unmanned aerial vehicle (UAV) communication links in low-altitude operations support mission control, status feedback, and data transmission. Abnormal communication states may affect mission continuity and risk response. However, conventional anomaly detection usually determines only whether a communication anomaly exists, making it difficult to support risk-state assessment and alert-threshold decision-making. To address this problem, this paper proposes Risk Stratification and Task-cost-aware Decision via Knowledge Distillation (RSTD-KD), a task-cost-aware UAV communication risk warning approach that integrates risk stratification, probability calibration, and threshold decision-making. RSTD-KD aggregates fine-grained communication records into window-level behavior samples, constructs low-, medium-, and high-risk… More >

View

200

Download

39

Open Access

ARTICLE

Ye Lu¹, Haoyang Hu^1,*, Wenyi Chang¹, Wanbin Liu¹, Wenyuan Zhang²
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084490 - 13 August 2026
Abstract Fine-grained multiclass intrusion detection over flow-level traffic remains difficult, largely because class boundaries are often entangled, temporal dependence is non-negligible, and the label distribution is heavily long-tailed. In this study, a compact temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-TinyTransformer framework is developed to bring these issues into a single modeling pipeline: the TCN branch focuses on short-range anomalous patterns, the BiGRU branch captures bidirectional temporal structure, and the TinyTransformer branch complements them with broader contextual interaction learning. To reduce the bias induced by extreme imbalance, training is not driven by a single correction mechanism, More >

View

157

Download

34

Open Access

ARTICLE

Ahtisham Waheed¹, Yunfie Yin^1,*, Abu Fatema Mohammad Abdun Noor², Md Imam Ahasan¹, Kah Ong Michael Goh^3,*, S. M. Hasan Mahmud^2,*, Umar Rashid⁴
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083456 - 13 August 2026
Abstract Robust lane detection is a fundamental perception task for autonomous driving and Advanced Driver Assistance Systems. However, it remains challenging in real-world environments due to degraded lane markings, complex road topologies, occlusions, and adverse illumination conditions. This work aims to improve lane detection robustness by explicitly modeling lane geometric properties while preserving end-to-end efficiency. We propose a direction-curvature aware lane detection framework that integrates a novel Direction-Curvature Aware (DCA) attention module into an anchor-based architecture. The DCA module enables tangent-aligned feature aggregation guided by learned direction fields and curvature-consistent attention. In addition, we introduce a More >

View

237

Download

40

Open Access

ARTICLE

Canan Batur Şahin^1,*, Siti Fatimah Abdul Razak^2,*, Arif Ullah², Ali Fatih Gündüz¹, Nazri Mohd Nawi³
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080874 - 13 August 2026
Abstract Unmanned Aerial Vehicle (UAV) networks face escalating cybersecurity threats, especially from zero-day attacks that exploit previously unknown vulnerabilities. To address this, we present HADAR-UAV (Hybrid Anomaly Detection with Adaptive Risk-calibration for UAV). This novel intrusion detection framework integrates masked autoencoder representation learning with Deep Support Vector Data Description (Deep SVDD) under conformal prediction guarantees to calibrate risk. Our method overcomes three critical limitations of existing approaches: (i) over-reliance on attack signatures, (ii) lack of statistical guarantees on false alarm rates, and (iii) insufficient robustness in feature extraction under partial observation. Using a rigorous Leave-Two-Attack-Families-Out (L2AFO)… More >

View

235

Download

72

Open Access

ARTICLE

Mian Muhammad Kamal^1,*, Tianjun Ma^1,*, Mohammed K. Alzaylaee², Husam S. Samkari^3,4, Mohammed F. Allehyani³, Omar Almomani⁵, Heba G. Mohamed^6,7
CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085412 - 13 August 2026
Abstract The cyber-physical network (CPS) combines sensing, communication, and control in physical processes, making them very susceptible to sophisticated cyber-attacks that may cause safety-critical effects. There are two core shortcomings to existing intrusion detection systems (IDS): generative-only models have little transparency of decision-making, while explainable-only models have low robustness in the presence of imbalanced and zero-day attacks. This paper presents a sequentially integrated trustworthy intrusion detection (ID) framework that combines generative learning and explainable AI (XAI) to boost robustness and transparency. The generative module enhances training data diversity, while the explainability module provides post-hoc interpretations during… More >

View

323

Download

61