Open Access
ISSN:1546-2218 (print)
ISSN:1546-2226 (online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2024 Impact Factor 1.7; Scopus CiteScore (Impact per Publication 2024): 6.1; SNIP (Source Normalized Impact per Paper 2024): 0.675; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1-39, 2025, DOI:10.32604/cmc.2025.067293 - 29 August 2025
（This article belongs to the Special Issue: Computational Analysis of Micro-Nano Material Mechanics and Manufacturing)
Abstract Carbon nanotubes (CNTs), black phosphorus nanotubes (BPNTs), and graphene derivatives exhibit significant promise for applications in nano-electromechanical systems (NEMS), energy storage, and sensing technologies due to their exceptional mechanical, electrical, and thermal properties. This review summarizes recent advances in understanding the dynamic behaviors of these nanomaterials, with a particular focus on insights gained from molecular dynamics (MD) simulations. Key areas discussed include the oscillatory and rotational dynamics of double-walled CNTs, fabrication and stability challenges associated with BPNTs, and the emerging potential of graphyne nanotubes (GNTs). The review also outlines design strategies for enhancing nanodevice performance More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 41-64, 2025, DOI:10.32604/cmc.2025.067881 - 29 August 2025
Abstract Digital twins (DTs) are rapidly emerging as transformative tools in materials science and engineering, enabling real-time data integration, predictive modeling, and virtual testing. This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science. Using a five-stage methodology, the review examines publication trends, thematic areas, citation metrics, and keyword patterns. The results reveal exponential growth in scientific output, with Materials Theory, Computation, and Data Science as the most represented area. A thematic analysis of the most cited documents identifies four major research streams: foundational More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 65-88, 2025, DOI:10.32604/cmc.2025.068226 - 29 August 2025
（This article belongs to the Special Issue: Computational Modeling of Mechanical Behavior of Advanced Materials)
Abstract Shape memory alloys (SMAs) and shape memory ceramics (SMCs) exhibit high recovery ability due to the martensitic transformation, which complicates the fracture mechanism of SMAs and SMCs. The phase field method, as a powerful numerical simulation tool, can efficiently resolve the microstructural evolution, multi-field coupling effects, and fracture behavior of SMAs and SMCs. This review begins by presenting the fundamental theoretical framework of the fracture phase field method as applied to SMAs and SMCs, covering key aspects such as the phase field modeling of martensitic transformation and brittle fracture. Subsequently, it systematically examines the phase More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 89-119, 2025, DOI:10.32604/cmc.2025.064346 - 29 August 2025
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Due to the rapid advancement of information technology, data has emerged as the core resource driving decision-making and innovation across all industries. As the foundation of artificial intelligence, machine learning(ML) has expanded its applications into intelligent recommendation systems, autonomous driving, medical diagnosis, and financial risk assessment. However, it relies on massive datasets, which contain sensitive personal information. Consequently, Privacy-Preserving Machine Learning (PPML) has become a critical research direction. To address the challenges of efficiency and accuracy in encrypted data computation within PPML, Homomorphic Encryption (HE) technology is a crucial solution, owing to its capability to… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 121-180, 2025, DOI:10.32604/cmc.2025.065250 - 29 August 2025
Abstract Addressing plant diseases and pests is not just crucial; it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 181-219, 2025, DOI:10.32604/cmc.2025.066306 - 29 August 2025
（This article belongs to the Special Issue: Advanced Trends in Vehicular Ad hoc Networks (VANETs))
Abstract In the rapidly evolving landscape of intelligent transportation systems, the security and authenticity of vehicular communication have emerged as critical challenges. As vehicles become increasingly interconnected, the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential. On the other hand, using intelligence in the authentication system is a significant attraction. While existing surveys broadly address vehicular security, a critical gap remains in the systematic exploration of Deep Learning (DL)-based authentication methods tailored to these communication paradigms. This survey fills that gap by offering a comprehensive… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 221-247, 2025, DOI:10.32604/cmc.2025.068338 - 29 August 2025
Abstract Fault tolerance is essential for reliable and sustainable smart city infrastructure. Interconnected IoT systems must function under frequent faults, limited resources, and complex conditions. Existing research covers various fault-tolerant methods. However, current reviews often lack system-level critique and multidimensional analysis. This study provides a structured review of fault tolerance strategies across layered IoT architectures in smart cities. It evaluates fault detection, containment, and recovery techniques using specific metrics. These include fault visibility, propagation depth, containment score, and energy-resilience trade-offs. The analysis uses comparative tables, architecture-aware discussions, and conceptual plots. It investigates the impact of fault… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 249-300, 2025, DOI:10.32604/cmc.2025.067750 - 29 August 2025
（This article belongs to the Special Issue: AI Industry Security with Large Language Models)
Abstract Large language models (LLMs) represent significant advancements in artificial intelligence. However, their increasing capabilities come with a serious challenge: misalignment, which refers to the deviation of model behavior from the designers’ intentions and human values. This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview. We define the concept of misalignment and elaborate on its various manifestations, including generating harmful content, factual errors (hallucinations), propagating biases, failing to follow instructions, emerging deceptive behaviors, and emergent misalignment. We explore the multifaceted causes of misalignment,… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 301-329, 2025, DOI:10.32604/cmc.2025.064777 - 29 August 2025
Abstract Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace. Network intrusion detection is usually regarded as an efficient means of dealing with security attacks. Many ways have been presented, utilizing various strategies and focusing on different types of visitors. Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development. Despite extensive research on anomaly-based network detection, there is still a lack of comprehensive literature reviews covering current methodologies and datasets. Despite the substantial research into anomaly-based network intrusion detection algorithms, there is a dearth of More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025
（This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)
Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 367-481, 2025, DOI:10.32604/cmc.2025.066481 - 29 August 2025
Abstract To enable proper diagnosis of a patient, medical images must demonstrate no presence of noise and artifacts. The major hurdle lies in acquiring these images in such a manner that extraneous variables, causing distortions in the form of noise and artifacts, are kept to a bare minimum. The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality, while indirectly attacking the effectiveness of the diagnostic process. It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise. The solution to these challenges… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 483-502, 2025, DOI:10.32604/cmc.2025.066578 - 29 August 2025
Abstract Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 503-518, 2025, DOI:10.32604/cmc.2025.068516 - 29 August 2025
Abstract As data analysis often incurs significant communication and computational costs, these tasks are increasingly outsourced to cloud computing platforms. However, this introduces privacy concerns, as sensitive data must be transmitted to and processed by untrusted parties. To address this, fully homomorphic encryption (FHE) has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service (MLaaS), enabling computation on encrypted data without revealing the plaintext. Nevertheless, FHE remains computationally expensive. As a result, approximate homomorphic encryption (AHE) schemes, such as CKKS, have attracted attention due to their efficiency. In our previous work, we proposed RP-OKC, a CKKS-based clustering… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 519-537, 2025, DOI:10.32604/cmc.2025.066754 - 29 August 2025
Abstract Efficient edge caching is essential for maximizing utility in video streaming systems, especially under constraints such as limited storage capacity and dynamically fluctuating content popularity. Utility, defined as the benefit obtained per unit of cache bandwidth usage, degrades when static or greedy caching strategies fail to adapt to changing demand patterns. To address this, we propose a deep reinforcement learning (DRL)-based caching framework built upon the proximal policy optimization (PPO) algorithm. Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 539-556, 2025, DOI:10.32604/cmc.2025.067266 - 29 August 2025
Abstract Ti-Hf-Zr-Nb-Ta refractory high-entropy alloys (RHEAs) exhibiting a dual-phase structure resulting from martensitic transformation offer significant ductility enhancement, but their design requires precise control of the phase stability between body-centred cubic (BCC) and hexagonal close-packed (HCP) phases. This study establishes a comprehensive thermodynamic database for the Ti-Hf-Zr-Nb-Ta system using the 3rd-generation Calculation of Phase Diagrams (CALPHAD) model. The reliability of the database is validated by the strong agreement between the calculated thermodynamic properties and phase equilibria and the experimental data for pure element, as well as for binary and ternary systems. Utilizing this database, the phase… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 557-580, 2025, DOI:10.32604/cmc.2025.067761 - 29 August 2025
（This article belongs to the Special Issue: Optimization Design for Material Microstructures)
Abstract Simultaneously, reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic. Considering the law of mass, traditional lightweight acoustic metamaterials make it difficult to control noise efficiently in real-life applications. In this study, a novel optimization-driven design scheme is developed to obtain lightweight acoustic metamaterials with a strong sound insulation capability for additive manufacturing. In the proposed design scheme, a topology optimization method for an acoustic metamaterial in the acoustic-solid interaction system is implemented to obtain an initial cross-sectional topology of the acoustic microstructure during the conceptual design phase. Then, More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 581-595, 2025, DOI:10.32604/cmc.2025.065746 - 29 August 2025
Abstract Rubberized concrete is one of the most studied applications of discarded tires and offers a promising approach to developing materials with enhanced properties. The rubberized concrete mixture results in a reduced modulus of elasticity and a reduced compressive and tensile strength compared to traditional concrete. This study employs finite element simulations to investigate the elastic properties of rubberized mortar (RuM), considering the influence of inclusion stiffness and interfacial debonding. Different homogenization schemes, including Voigt, Reuss, and mean-field approaches, are implemented using DIGIMAT and ANSYS. Furthermore, the influence of the interfacial transition zone (ITZ) between mortar… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025
（This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)
Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 613-635, 2025, DOI:10.32604/cmc.2025.068756 - 29 August 2025
（This article belongs to the Special Issue: Optimization Design for Material Microstructures)
Abstract Inspired by natural biomimetic structures exemplified by femoral bones, the shell-infill composite design has emerged as a research focus in structural optimization. However, existing studies predominantly focus on uniform-thickness shell designs and lack robust methodologies for generating high-resolution porous infill configurations. To address these challenges, a novel topology optimization framework for full-scale shell-filled composite structures is developed in this paper. First, a physics-driven, non-uniform partial differential equation (PDE) filter is developed, enabling precise control of variable-thickness shells by establishing explicit mapping relationships between shell thickness and filter radii. Second, this study addresses the convergence inefficiency… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 637-655, 2025, DOI:10.32604/cmc.2025.065674 - 29 August 2025
（This article belongs to the Special Issue: Computational Modeling and Simulation of Energy and Environmental Materials)
Abstract Covalent organic frameworks (COFs) are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures. Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks. Ionic covalent organic frameworks (ICOFs) are a subclass that incorporates ions in positive, negative, or zwitterionic forms into the frameworks. In particular, spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs. Unlike electroneutral COFs, ICOFs can be tailored by adjusting the types and arrangements of ions, influencing their formation mechanisms and physical properties. This study focuses on analyzing the graph-based structural… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 657-679, 2025, DOI:10.32604/cmc.2025.068242 - 29 August 2025
（This article belongs to the Special Issue: Computational Analysis of Micro-Nano Material Mechanics and Manufacturing)
Abstract Microstructure topology evolution during severe plastic deformation (SPD) is crucial for understanding and optimising the mechanical properties of metallic materials, though its prediction remains challenging. Herein, we combine discrete cell complexes (DCC), a fully discrete algebraic topology model—with finite element analysis (FEA) to simulate and analyse the microstructure topology of pure copper under SPD. Using DCC, we model the evolution of microstructure topology characterised by Betti numbers (, , ) and Euler characteristic (). This captures key changes in GBNs and topological features within representative volume elements (RVEs) containing several hundred grains during SPD-induced recrystallisation.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 681-694, 2025, DOI:10.32604/cmc.2025.064191 - 29 August 2025
Abstract Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems. However, existing methods struggle with small target sizes, complex backgrounds, low-quality image acquisition, and interference from contamination. To address these challenges, this paper proposes the Real-time Cable Defect Detection Network (RC2DNet), which achieves an optimal balance between detection accuracy and computational efficiency. Unlike conventional approaches, RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids, multi-level feature fusion, and an adaptive weighting mechanism. Additionally, a boundary feature enhancement module More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 695-727, 2025, DOI:10.32604/cmc.2025.066606 - 29 August 2025
（This article belongs to the Special Issue: Collaborative Edge Intelligence and Its Emerging Applications)
Abstract Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0. Latency, privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems. We demonstrate that, to overcome these challenges, for instance, the EdgeGuard-IoT framework, a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid, is needed on the edge to integrate Secure Federated Learning (SFL) and Adaptive Anomaly Detection (AAD). With ultra-reliable low latency communication (URLLC) of 6G, artificial intelligence-based network orchestration, and massive machine type… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 729-750, 2025, DOI:10.32604/cmc.2025.066748 - 29 August 2025
Abstract In rapid urban development, outdoor parking lots have become essential components of urban transportation systems. However, the increasing number of parking lots is accompanied by a rising risk of vehicle fires, posing a serious challenge to public safety. As a result, there is a critical need for fire warning systems tailored to outdoor parking lots. Traditional smoke detection methods, however, struggle with the complex outdoor environment, where smoke characteristics often blend into the background, resulting in low detection efficiency and accuracy. To address these issues, this paper introduces a novel model named Dynamic Contextual Transformer… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 751-768, 2025, DOI:10.32604/cmc.2025.067867 - 29 August 2025
Abstract Recently, ship detection technology has been applied extensively in the marine security monitoring field. However, achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales, slightly occluded objects, uneven illumination, and sea clutter. To address these issues, we propose a novel ship detection approach, i.e., the Twin Feature Pyramid Network and Data Augmentation (TFPN-DA), which mainly consists of three modules. First, to eliminate the negative effects of slightly occluded objects and uneven illumination, we propose the Spatial Attention within the Twin Feature Pyramid Network (SA-TFPN) method, which is based More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025
Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 801-826, 2025, DOI:10.32604/cmc.2025.066540 - 29 August 2025
Abstract Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 827-853, 2025, DOI:10.32604/cmc.2025.064924 - 29 August 2025
Abstract Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks. This article offers an intriguing architecture for semantic, instance, and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks (Bi-FPN). When implemented in place of the EfficientNet-B5 backbone, EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications. By ensuring superior multi-scale feature fusion, Bi-FPN integration enhances the segmentation of complex objects across various urban environments. The design suggested is examined on rigorous datasets, encompassing Cityscapes, Common Objects in Context, KITTI Karlsruhe Institute of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 855-874, 2025, DOI:10.32604/cmc.2025.066343 - 29 August 2025
Abstract Classroom behavior recognition is a hot research topic, which plays a vital role in assessing and improving the quality of classroom teaching. However, existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures, and inconsistent objects. To address this challenge, we proposed an effective, lightweight object detector method called the RFNet model (YOLO-FR). The YOLO-FR is a lightweight and effective model. Specifically, for efficient multi-scale feature extraction, effective feature pyramid shared convolutional (FPSC) was designed to improve the feature extract performance by leveraging convolutional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 875-903, 2025, DOI:10.32604/cmc.2025.067078 - 29 August 2025
（This article belongs to the Special Issue: Advances in Secure Computing: Post-Quantum Security, Multimedia Encryption, and Intelligent Threat Defence)
Abstract The rapid evolution of quantum computing poses significant threats to traditional cryptographic schemes, particularly in Decentralized Finance (DeFi) systems that rely on legacy mechanisms like RSA and ECDSA for digital identity verification. This paper proposes a quantum-resilient, blockchain-based identity verification framework designed to address critical challenges in privacy preservation, scalability, and post-quantum security. The proposed model integrates Post-quantum Cryptography (PQC), specifically lattice-based cryptographic primitives, with Decentralized Identifiers (DIDs) and Zero-knowledge Proofs (ZKPs) to ensure verifiability, anonymity, and resistance to quantum attacks. A dual-layer architecture is introduced, comprising an identity layer for credential generation and validation,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 905-923, 2025, DOI:10.32604/cmc.2025.066307 - 29 August 2025
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract As the use of deepfake facial videos proliferate, the associated threats to social security and integrity cannot be overstated. Effective methods for detecting forged facial videos are thus urgently needed. While many deep learning-based facial forgery detection approaches show promise, they often fail to delve deeply into the complex relationships between image features and forgery indicators, limiting their effectiveness to specific forgery techniques. To address this challenge, we propose a dual-branch collaborative deepfake detection network. The network processes video frame images as input, where a specialized noise extraction module initially extracts the noise feature maps.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 925-953, 2025, DOI:10.32604/cmc.2025.065709 - 29 August 2025
Abstract It is evident that complex optimization problems are becoming increasingly prominent, metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional, nonlinear problems. However, the traditional Sparrow Search Algorithm (SSA) suffers from limited global search capability, insufficient population diversity, and slow convergence, which often leads to premature stagnation in local optima. Despite the proposal of various enhanced versions, the effective balancing of exploration and exploitation remains an unsolved challenge. To address the previously mentioned problems, this study proposes a multi-strategy collaborative improved SSA, which systematically integrates four complementary strategies: (1) the Northern Goshawk Optimization (NGO) mechanism… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 955-982, 2025, DOI:10.32604/cmc.2025.065853 - 29 August 2025
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Wound classification is a critical task in healthcare, requiring accurate and efficient diagnostic tools to support clinicians. In this paper, we investigated the effectiveness of the YOLO11n model in classifying different types of wound images. This study presents the training and evaluation of a lightweight YOLO11n model for automated wound classification using the AZH dataset, which includes six wound classes: Background (BG), Normal Skin (N), Diabetic (D), Pressure (P), Surgical (S), and Venous (V). The model’s architecture, optimized through experiments with varying batch sizes and epochs, ensures efficient deployment in resource-constrained environments. The model’s architecture… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 983-999, 2025, DOI:10.32604/cmc.2025.065978 - 29 August 2025
Abstract With the ongoing digitalization and intelligence of power systems, there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting. Nevertheless, power data often contains sensitive information, making it a critical industry challenge to efficiently utilize this data while ensuring privacy. Traditional Federated Learning (FL) methods can mitigate data leakage by training models locally instead of transmitting raw data. Despite this, FL still has privacy concerns, especially gradient leakage, which might expose users’ sensitive information. Therefore, integrating Differential Privacy (DP) techniques is essential for stronger privacy protection.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1001-1018, 2025, DOI:10.32604/cmc.2025.068201 - 29 August 2025
（This article belongs to the Special Issue: Towards Privacy-preserving, Secure and Trustworthy AI-enabled Systems)
Abstract Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across various object classes, forming unseen backdoor-object configurations that evade standard detection pipelines. In this paper, we introduce DBOM (Disentangled Backdoor-Object Modeling), a proactive framework that leverages structured disentanglement to identify and neutralize both seen and unseen backdoor threats at the dataset level. Specifically, DBOM factorizes input image representations by modeling triggers and objects as independent primitives in the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1019-1035, 2025, DOI:10.32604/cmc.2025.066532 - 29 August 2025
Abstract With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1037-1052, 2025, DOI:10.32604/cmc.2025.067650 - 29 August 2025
（This article belongs to the Special Issue: Deep Learning: Emerging Trends, Applications and Research Challenges for Image Recognition)
Abstract Vehicle detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving technologies. The complexity and diversity of real-world road environments, coupled with traffic congestion, pose significant challenges to the accuracy and real-time performance of vehicle detection models. To address these challenges, this paper introduces a fast and accurate vehicle detection algorithm named BES-Net. Firstly, the BoTNet module is integrated into the backbone network to bolster the model’s long-distance dependency, address the complexities and diversity of road environments, and accelerate the detection speed of the BES-Net network. Secondly, to accommodate the varying sizes… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1053-1072, 2025, DOI:10.32604/cmc.2025.064386 - 29 August 2025
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract Hydraulic-electric systems are widely utilized in various applications. However, over time, these systems may encounter random faults such as loose cables, ambient environmental noise, or sensor aging, leading to inaccurate sensor readings. These faults may result in system instability or compromise safety. In this paper, we propose a fault compensation control system to mitigate the effects of sensor faults and ensure system safety. Specifically, we utilize the pressure sensor within the system to implement the control process and evaluate performance based on the piston position. First, we develop a mathematical model to identify optimal parameters… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1073-1094, 2025, DOI:10.32604/cmc.2025.066078 - 29 August 2025
Abstract Roads inevitably have defects during use, which not only seriously affect their service life but also pose a hidden danger to traffic safety. Existing algorithms for detecting road defects are unsatisfactory in terms of accuracy and generalization, so this paper proposes an algorithm based on YOLOv11. The method embeds wavelet transform convolution (WTConv) into the backbone’s C3k2 module to enhance low-frequency feature extraction while avoiding parameter bloat. Secondly, a novel multi-scale fusion diffusion network (MFDN) architecture is designed for the neck to strengthen cross-scale feature interactions, boosting detection precision. In terms of model optimization, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1095-1111, 2025, DOI:10.32604/cmc.2025.067129 - 29 August 2025
（This article belongs to the Special Issue: Omnipresent AI in the Cloud Era Reshaping Distributed Computation and Adaptive Systems for Modern Applications)
Abstract In the context of global change, understanding changes in water resources requires close monitoring of groundwater levels. A mismatch between water supply and demand could lead to severe consequences such as land subsidence. To ensure a sustainable water supply and to minimize the environmental effects of land subsidence, groundwater must be effectively monitored and managed. Despite significant global progress in groundwater management, the swift advancements in technology and artificial intelligence (AI) have spurred extensive studies aimed at enhancing the accuracy of groundwater predictions. This study proposes an AI-based method that combines deep learning with a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1113-1134, 2025, DOI:10.32604/cmc.2025.067240 - 29 August 2025
Abstract The evidential reasoning (ER) rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty. However, traditional ER implementations rely on two critical limitations: 1) unrealistic assumptions of complete evidence independence, and 2) a lack of mechanisms to differentiate causal relationships from spurious correlations. Existing similarity-based approaches often misinterpret interdependent evidence, leading to unreliable decision outcomes. To address these gaps, this study proposes a causality-enhanced ER rule (CER-e) framework with three key methodological innovations: 1) a multidimensional causal representation of evidence to capture dependency structures; 2) probabilistic quantification of causal… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1135-1160, 2025, DOI:10.32604/cmc.2025.066969 - 29 August 2025
Abstract The healthcare sector involves many steps to ensure efficient care for patients, such as appointment scheduling, consultation plans, online follow-up, and more. However, existing healthcare mechanisms are unable to facilitate a large number of patients, as these systems are centralized and hence vulnerable to various issues, including single points of failure, performance bottlenecks, and substantial monetary costs. Furthermore, these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access. To address these issues, this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials. Furthermore, also… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1161-1184, 2025, DOI:10.32604/cmc.2025.062330 - 29 August 2025
（This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)
Abstract To address the high-quality forged videos, traditional approaches typically have low recognition accuracy and tend to be easily misclassified. This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content (AIGC) video authenticity detection with a multimodal information fusion approach. First, a high-quality multimodal video dataset is collected and normalized, including resolution correction and frame rate unification. Next, feature extraction techniques are employed to draw out features from visual, audio, and text modalities. Subsequently, these features are fused into a multilayer perceptron and attention mechanisms-based More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1185-1220, 2025, DOI:10.32604/cmc.2025.066898 - 29 August 2025
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities (IIoTCC). It introduces two key innovations: a Quantum Secure Authentication (QSA) mechanism for adversarial defense and integrity validation, and a Self-Attention Long Short-Term Memory (SALSTM) model for high-accuracy spatiotemporal anomaly detection. Addressing core challenges in traditional Federated Learning (FL)—such as model poisoning, communication overhead, and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure, real-time operation across heterogeneous IIoT domains including utilities, public safety, and traffic systems. Evaluated on the More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1221-1234, 2025, DOI:10.32604/cmc.2025.066842 - 29 August 2025
（This article belongs to the Special Issue: Omnipresent AI in the Cloud Era Reshaping Distributed Computation and Adaptive Systems for Modern Applications)
Abstract Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions, which is a typical manifestation of this trend. With the increasing complexity of image in tasks and the growth of data scale, existing deep learning methods still have some limitations. For example, they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal. To solve this problem, the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network. The encoder of the proposed model More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1235-1265, 2025, DOI:10.32604/cmc.2025.066527 - 29 August 2025
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract The combined heat and power economic dispatch (CHPED) problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints. This paper presents a hybrid differential evolution (DE) algorithm combined with an improved equilibrium optimizer (DE-IEO) specifically for the CHPED problem. The DE-IEO incorporates three enhancement strategies: a chaotic mechanism for initializing the population, an improved equilibrium pool strategy, and a quasi-opposite based learning mechanism. These strategies enhance the individual utilization capabilities of the equilibrium optimizer, while differential evolution boosts local exploitation and escape capabilities. The IEO enhances… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1267-1285, 2025, DOI:10.32604/cmc.2025.066601 - 29 August 2025
Abstract These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1287-1309, 2025, DOI:10.32604/cmc.2025.065670 - 29 August 2025
Abstract Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues, and their appearance may lead to a series of complex symptoms. However, current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology, size, and complex background, resulting in low detection accuracy, high rate of misdiagnosis and underdiagnosis, and challenges in meeting clinical needs. Therefore, this paper proposes the CMS-YOLO network model for multi-category brain tumor detection, which is based on the You Only Look Once version 10 (YOLOv10s) algorithm. This model… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1311-1337, 2025, DOI:10.32604/cmc.2025.066569 - 29 August 2025
（This article belongs to the Special Issue: Fuzzy Logic: Next-Generation Algorithms and Applications)
Abstract For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1339-1363, 2025, DOI:10.32604/cmc.2025.067117 - 29 August 2025
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies, 2nd Edition)
Abstract This paper proposes a Multi-Agent Attention Proximal Policy Optimization (MA2PPO) algorithm aiming at the problems such as credit assignment, low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles (UAVs). Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks, leading to low capture efficiency and a significant decline in performance when the scale expands. To tackle these issues, based on the proximal policy optimization (PPO) algorithm, MA2PPO adopts the centralized training with decentralized execution (CTDE) framework and introduces a dynamic decoupling mechanism,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1365-1382, 2025, DOI:10.32604/cmc.2025.067764 - 29 August 2025
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave (FMCW) automotive radar performance under high noise and interference. The four-stage pipeline is applied consecutively: (i) an improved independent component analysis (ICA) blindly separates the two-channel echoes, isolating target and interference components; (ii) a recursive least-squares (RLS) filter compensates amplitude- and phase-mismatches, restoring signal fidelity; (iii) variational mode decomposition (VMD) followed by the Hilbert-Huang Transform (HHT) extracts noise-free intrinsic mode functions (IMFs) and sharpens their time-frequency signatures; and (iv) HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information. Finally, More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1383-1425, 2025, DOI:10.32604/cmc.2025.067784 - 29 August 2025
（This article belongs to the Special Issue: Bridging the Gap: AutoML and Explainable AI for Industrial and Healthcare Innovations)
Abstract Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1427-1456, 2025, DOI:10.32604/cmc.2025.066476 - 29 August 2025
Abstract With the increasing importance of multimodal data in emotional expression on social media, mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches. However, the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis. To address these challenges, this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception (TGICP). Specifically, we utilize a Inter-sample Commonality Perception (ICP) module to extract common features from similar samples within the same modality, and use these common features to enhance the original features of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1457-1493, 2025, DOI:10.32604/cmc.2025.062954 - 29 August 2025
Abstract Recent advances in artificial intelligence and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Therefore, developing reliable and robust deepfake video detection mechanisms is paramount. This research introduces a novel real-time deepfake video detection framework by analyzing gaze and blink patterns, addressing the spatial-temporal challenges unique to gaze and blink anomalies using the TimeSformer and hybrid Transformer-CNN models. The TimeSformer architecture leverages spatial-temporal attention mechanisms to capture fine-grained blinking intervals and gaze direction anomalies. Compared to state-of-the-art traditional convolutional models like MesoNet and EfficientNet, which primarily focus on global facial… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1495-1514, 2025, DOI:10.32604/cmc.2025.066506 - 29 August 2025
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient. The adoption of artificial intelligence (AI) for surface evaluation has emerged as a promising solution. Since the visual appeal of wooden products directly impacts their market value and overall business success, effective quality control is crucial. However, conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times. To address these shortcomings, the authors propose a real-time deep learning-based system for evaluating surface appearance quality. The method integrates… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1515-1539, 2025, DOI:10.32604/cmc.2025.067518 - 29 August 2025
Abstract In contemporary society, rapid and accurate optical cable fault detection is of paramount importance for ensuring the stability and reliability of optical networks. The emergence of novel faults in optical networks has introduced new challenges, significantly compromising their normal operation. Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time-Domain Reflectometers (OTDR) to enable real-time fault detection and diagnosis in optical fibers. In this paper, we introduce a multi-scale Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1559-1583, 2025, DOI:10.32604/cmc.2025.065916 - 29 August 2025
Abstract Topic modeling is a fundamental technique of content analysis in natural language processing, widely applied in domains such as social sciences and finance. In the era of digital communication, social scientists increasingly rely on large-scale social media data to explore public discourse, collective behavior, and emerging social concerns. However, traditional models like Latent Dirichlet Allocation (LDA) and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets, especially in complex non-English languages like Chinese. This paper presents Generative Language Model Topic (GLMTopic) a novel hybrid topic modeling framework leveraging the capabilities… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1585-1602, 2025, DOI:10.32604/cmc.2025.066147 - 29 August 2025
Abstract Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples. This imbalance introduces bias into feature extraction within facial expression recognition (FER) models, which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy. A novel FER model is introduced to address these issues. It integrates rebalancing mechanisms to regulate attention consistency and focus, offering enhanced efficacy. Our approach proposes the following improvements: (i) rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart; (ii) coefficient More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1603-1630, 2025, DOI:10.32604/cmc.2025.068707 - 29 August 2025
（This article belongs to the Special Issue: Recent Advancements in Machine Learning and Data Analysis for Disease Detection)
Abstract Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1631-1651, 2025, DOI:10.32604/cmc.2025.067252 - 29 August 2025
Abstract With the continuous advancement of steganographic techniques, the task of image steganalysis has become increasingly challenging, posing significant obstacles to the fields of information security and digital forensics. Although existing deep learning methods have achieved certain progress in steganography detection, they still encounter several difficulties in real-world applications. Specifically, current methods often struggle to accurately focus on steganography sensitive regions, leading to limited detection accuracy. Moreover, feature information is frequently lost during transmission, which further reduces the model’s generalization ability. These issues not only compromise the reliability of steganography detection but also hinder its applicability… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1653-1674, 2025, DOI:10.32604/cmc.2025.067524 - 29 August 2025
Abstract The exponential growth of audio data shared over the internet and communication channels has raised significant concerns about the security and privacy of transmitted information. Due to high processing requirements, traditional encryption algorithms demand considerable computational effort for real-time audio encryption. To address these challenges, this paper presents a permutation for secure audio encryption using a combination of Tent and 1D logistic maps. The audio data is first shuffled using Tent map for the random permutation. The high random secret key with a length equal to the size of the audio data is then generated… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1675-1692, 2025, DOI:10.32604/cmc.2025.067675 - 29 August 2025
Abstract Accurately identifying crop pests and diseases ensures agricultural productivity and safety. Although current YOLO-based detection models offer real-time capabilities, their conventional convolutional layers involve high computational redundancy and a fixed receptive field, making it challenging to capture local details and global semantics in complex scenarios simultaneously. This leads to significant issues like missed detections of small targets and heightened sensitivity to background interference. To address these challenges, this paper proposes a lightweight adaptive detection network—StarSpark-AdaptiveNet (SSANet), which optimizes features through a dual-module collaborative mechanism. Specifically, the StarNet module utilizes Depthwise separable convolutions (DW-Conv) and dynamic… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1693-1714, 2025, DOI:10.32604/cmc.2025.064260 - 29 August 2025
（This article belongs to the Special Issue: Safe and Secure Artificial Intelligence)
Abstract Ransomware, particularly crypto-ransomware, remains a significant cybersecurity challenge, encrypting victim data and demanding a ransom, often leaving the data irretrievable even if payment is made. This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins. The approach employs a two-tiered approach: a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms. A critical feature is the integration of the most effective Application Programming Interface call monitoring, which analyzes system-level interactions such as file More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1715-1732, 2025, DOI:10.32604/cmc.2025.065560 - 29 August 2025
Abstract With the rapid development and widespread adoption of Internet of Things (IoT) technology, the innovative concept of the Internet of Vehicles (IoV) has emerged, ushering in a new era of intelligent transportation. Since vehicles are mobile entities, they move across different domains and need to communicate with the Roadside Unit (RSU) in various regions. However, open environments are highly susceptible to becoming targets for attackers, posing significant risks of malicious attacks. Therefore, it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs, particularly in scenarios where More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1733-1750, 2025, DOI:10.32604/cmc.2025.067284 - 29 August 2025
Abstract Graph Neural Networks (GNNs) have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models, such as Large Language Models (LLMs), to enhance structural reasoning, knowledge retrieval, and memory management. The expansion of their application scope imposes higher requirements on the robustness of GNNs. However, as GNNs are applied to more dynamic and heterogeneous environments, they become increasingly vulnerable to real-world perturbations. In particular, graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features, which are significantly more challenging than isolated attacks. These disruptions, caused… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1751-1777, 2025, DOI:10.32604/cmc.2025.066892 - 29 August 2025
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The increasing sophistication of cyberattacks, coupled with the limitations of rule-based detection systems, underscores the urgent need for proactive and intelligent cybersecurity solutions. Traditional intrusion detection systems often struggle with detecting early-stage threats, particularly in dynamic environments such as IoT, SDNs, and cloud infrastructures. These systems are hindered by high false positive rates, poor adaptability to evolving threats, and reliance on large labeled datasets. To address these challenges, this paper introduces CyberGuard-X, an AI-driven framework designed to identify attack precursors—subtle indicators of malicious intent—before full-scale intrusions occur. CyberGuard-X integrates anomaly detection, time-series analysis, and multi-stage… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1779-1806, 2025, DOI:10.32604/cmc.2025.066543 - 29 August 2025
（This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)
Abstract Accurate and reliable photovoltaic (PV) modeling is crucial for the performance evaluation, control, and optimization of PV systems. However, existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency. To address these challenges, we propose an adaptive multi-learning cooperation search algorithm (AMLCSA) for efficient identification of unknown parameters in PV models. AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises. It enhances the original cooperation search algorithm in two key aspects: (i) an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights, allowing better individuals to More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1807-1858, 2025, DOI:10.32604/cmc.2025.067127 - 29 August 2025
Abstract In today’s rapidly evolving digital landscape, web application security has become paramount as organizations face increasingly sophisticated cyber threats. This work presents a comprehensive methodology for implementing robust security measures in modern web applications and the proof of the Methodology applied to Vue.js, Spring Boot, and MySQL architecture. The proposed approach addresses critical security challenges through a multi-layered framework that encompasses essential security dimensions including multi-factor authentication, fine-grained authorization controls, sophisticated session management, data confidentiality and integrity protection, secure logging mechanisms, comprehensive error handling, high availability strategies, advanced input validation, and security headers implementation. Significant… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1859-1882, 2025, DOI:10.32604/cmc.2025.066283 - 29 August 2025
（This article belongs to the Special Issue: Advances in Wireless Sensor Networks: Security, Efficiency, and Intelligence)
Abstract Energy efficiency is critical in Wireless Sensor Networks (WSNs) due to the limited power supply. While clustering algorithms are commonly used to extend network lifetime, most of them focus on single-layer optimization. To this end, an Energy-efficient Cross-layer Clustering approach based on the Gini (ECCG) index theory was proposed in this paper. Specifically, a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election (GICHE) is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters. In addition, to improve inter-cluster energy efficiency, a Queue synchronous More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1883-1897, 2025, DOI:10.32604/cmc.2025.065529 - 29 August 2025
Abstract In recent years, diffusion models have achieved remarkable progress in image generation. However, extending them to text-to-video (T2V) generation remains challenging, particularly in maintaining semantic consistency and visual quality across frames. Existing approaches often overlook the synergy between high-level semantics and low-level texture information, resulting in blurry or temporally inconsistent outputs. To address these issues, we propose Dual Consistency Training (DCT), a novel framework designed to jointly optimize semantic and texture consistency in video generation. Specifically, we introduce a multi-scale spatial adapter to enhance spatial feature extraction, and leverage the complementary strengths of CLIP and More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1899-1916, 2025, DOI:10.32604/cmc.2025.064728 - 29 August 2025
Abstract Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1917-1936, 2025, DOI:10.32604/cmc.2025.067382 - 29 August 2025
Abstract Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement, an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed. This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model, and combines a post-processing correction mechanism to improve the drill pipe counting accuracy. In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1937-1957, 2025, DOI:10.32604/cmc.2025.068486 - 29 August 2025
（This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)
Abstract With the rapid development of Cloud-Edge-End (CEE) computing, the demand for secure and lightweight communication protocols is increasingly critical, particularly for latency-sensitive applications such as smart manufacturing, healthcare, and real-time monitoring. While traditional cryptographic schemes offer robust protection, they often impose excessive computational and energy overhead, rendering them unsuitable for use in resource-constrained edge and end devices. To address these challenges, in this paper, we propose a novel lightweight encryption framework, namely Dynamic Session Key Allocation with Time-Indexed Ascon (DSKA-TIA). Built upon the NIST-endorsed Ascon algorithm, the DSKA-TIA introduces a time-indexed session key generation mechanism… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1959-1980, 2025, DOI:10.32604/cmc.2025.066266 - 29 August 2025
Abstract As an essential tool for quantitative analysis of lower limb coordination, optical motion capture systems with marker-based encoding still suffer from inefficiency, high costs, spatial constraints, and the requirement for multiple markers. While 3D pose estimation algorithms combined with ordinary cameras offer an alternative, their accuracy often deteriorates under significant body occlusion. To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’ lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance. Compared to traditional optical motion capture systems, this work… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1981-1998, 2025, DOI:10.32604/cmc.2025.064268 - 29 August 2025
Abstract Remote sensing plays a pivotal role in environmental monitoring, disaster relief, and urban planning, where accurate scene classification of aerial images is essential. However, conventional convolutional neural networks (CNNs) struggle with long-range dependencies and preserving high-resolution features, limiting their effectiveness in complex aerial image analysis. To address these challenges, we propose a Hybrid HRNet-Swin Transformer model that synergizes the strengths of HRNet-W48 for high-resolution segmentation and the Swin Transformer for global feature extraction. This hybrid architecture ensures robust multi-scale feature fusion, capturing fine-grained details and broader contextual relationships in aerial imagery. Our methodology begins with… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1999-2020, 2025, DOI:10.32604/cmc.2025.066423 - 29 August 2025
Abstract Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2021-2050, 2025, DOI:10.32604/cmc.2025.064243 - 29 August 2025
（This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)
Abstract The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets, particularly in industrial contexts where efficient data handling and process innovation are critical. Feature selection, an essential step in data-driven process innovation, aims to identify the most relevant features to improve model interpretability, reduce complexity, and enhance predictive accuracy. To address the limitations of existing feature selection methods, this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization (APO) algorithm. Specifically, we incorporate a specialized conversion mechanism to effectively adapt APO from continuous… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2051-2070, 2025, DOI:10.32604/cmc.2025.065377 - 29 August 2025
Abstract Sarcasm detection is a complex and challenging task, particularly in the context of Chinese social media, where it exhibits strong contextual dependencies and cultural specificity. To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions, this paper proposes an event-aware model for Chinese sarcasm detection, leveraging a multi-head attention (MHA) mechanism and contrastive learning (CL) strategies. The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers (BERT) encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025
Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2095-2115, 2025, DOI:10.32604/cmc.2025.065679 - 29 August 2025
Abstract Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion. Existing identification methods based on Graph Neural Networks (GNNs) often lead to yield inaccurate features of influential users due to neighborhood aggregation, and require a large substantial amount of labeled data for training, making them difficult and challenging to apply in practice. To address this issue, we propose a semi-supervised contrastive learning method for identifying influential users. First, the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2117-2139, 2025, DOI:10.32604/cmc.2025.065232 - 29 August 2025
Abstract The generation of high-quality, realistic face generation has emerged as a key field of research in computer vision. This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network (SRGAN) with a Pyramid Attention Module (PAM) to enhance the quality of deep face generation. The SRGAN framework is designed to improve the resolution of generated images, addressing common challenges such as blurriness and a lack of intricate details. The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction, enabling the network to capture finer details and complex facial features… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2141-2155, 2025, DOI:10.32604/cmc.2025.066370 - 29 August 2025
Abstract Deep neural networks are known to be vulnerable to adversarial attacks. Unfortunately, the underlying mechanisms remain insufficiently understood, leading to empirical defenses that often fail against new attacks. In this paper, we explain adversarial attacks from the perspective of robust features, and propose a novel Generative Adversarial Network (GAN)-based Robust Feature Disentanglement framework (GRFD) for adversarial defense. The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator. For the generator, we introduce a novel Latent Variable Constrained Variational Auto-Encoder (LVCVAE), which enhances the typical beta-VAE with a constrained rectification module… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2157-2177, 2025, DOI:10.32604/cmc.2025.068360 - 29 August 2025
（This article belongs to the Special Issue: Sensing the Future: Breakthroughs in Wireless Sensor Networks)
Abstract Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies, limiting their use for lightweight, secure image encryption in resource-constrained Wireless Sensor Networks (WSNs). We propose the SPCM, a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions, leveraging a piecewise function to achieve a broad chaotic range () and a high Lyapunov exponent (5.04). Validated through nine benchmarks, including standard randomness tests, Diehard tests, and Shannon entropy (3.883), SPCM demonstrates superior randomness and high sensitivity to initial conditions. Applied to image encryption, SPCM achieves 0.152582 s (39% faster than some techniques) and 433.42 More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2179-2193, 2025, DOI:10.32604/cmc.2025.063190 - 29 August 2025
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the… More >
Login
Register
Submit a Paper
Propose a Special lssue
