Open Access

REVIEW

Qinghua Qin^*
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 >

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2429

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Diego Vergara^*, Antonio del Bosque, Pablo Fernández-Arias
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 >

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3217

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2353

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REVIEW

Junhui Hua¹, Junyuan Xiong², Bo Xu^1,*, Chong Wang¹, Qingyuan Wang¹
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 >

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2829

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2136

Open Access

REVIEW

Lingling Wu¹, Xu An Wang^1,2,*, Jiasen Liu¹, Yunxuan Su¹, Zheng Tu¹, Wenhao Liu¹, Haibo Lei¹, Dianhua Tang³, Yunfei Cao³, Jianping Zhang³
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 >

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4907

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3118

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REVIEW

MD Tausif Mallick¹, Saptarshi Banerjee², Nityananda Thakur³, Himadri Nath Saha^4,*, Amlan Chakrabarti¹
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 >

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15192

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3323

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REVIEW

Tarak Nandy^1,*, Sananda Bhattacharyya²
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 181-219, 2025, DOI:10.32604/cmc.2025.066306 - 29 August 2025
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 >

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2602

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2119

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REVIEW

Hitesh Mohapatra^*
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 >

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2629

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2074

Open Access

REVIEW

Yubin Qu^1,2, Song Huang^2,*, Long Li³, Peng Nie², Yongming Yao²
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 249-300, 2025, DOI:10.32604/cmc.2025.067750 - 29 August 2025
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 >

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3272

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2500

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REVIEW

Chaimae Hazman^1,2, Azidine Guezzaz², Said Benkirane², Mourade Azrour^3,*, Vinayakumar Ravi⁴, Abdulatif Alabdulatif ⁵
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 >

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3100

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2215

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REVIEW

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹
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 >

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2888

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2243

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REVIEW

Apoorav Sharma¹, Ayush Dogra^2,*, Bhawna Goyal³, Archana Saini², Vinay Kukreja²
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 >

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2226

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2039

Open Access

ARTICLE

Irfan Abbas, Robertas Damaševičius^*
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 >

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2990

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2451

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ARTICLE

Ray-I Chang¹, Chia-Hui Wang^2,*, Yen-Ting Chang¹, Lien-Chen Wei²
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 >

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2765

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2281

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ARTICLE

Mingoo Kwon, Kyeongmin Kim, Minseok Song^*
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 >

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2459

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2118

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ARTICLE

Jian Ding¹, Jinghan Gao¹, Enkuan Zhang¹, Ying Tang^2,*, Lijun Zhang^3,*, Xingchuan Xia¹
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 >

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2087

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ARTICLE

Ying Zhou¹, Jiayang Yuan¹, Zhengtao Shu¹, Mengli Ye¹, Liang Gao¹, Qiong Wang^2,*
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 >

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2167

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ARTICLE

Cristian Martínez-Fuentes¹, Pedro Pesante^2,*, Karin Saavedra³, Paul Oumaziz⁴
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 >

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2457

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2038

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ARTICLE

Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹
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 >

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2578

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2241

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ARTICLE

Xuefei Yang, Ying Zhou, Liang Gao, Hao Li^*
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 >

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2149

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ARTICLE

Micheal Arockiaraj^1,*, Aravindan Maaran², C. I. Arokiya Doss²
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 >

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1987

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ARTICLE

Siying Zhu^1,#, Weijian Gao^2,#, Min Yi^1,2,*, Zhuhua Zhang^1,2,*
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 >

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2525

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2025

Open Access

ARTICLE

Zilu Liu^1,#, Hongjin Zhu^2,#,*
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 >

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2378

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2151

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ARTICLE

Mohammed Naif Alatawi^*
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 >

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3044

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2316

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ARTICLE

Gang He¹, Zhuoyan Chen¹, Mufeng Wang², Xingcheng Yang³, Zhenyong Zhang^1,*
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 >

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2698

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2215

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ARTICLE

Huagang Jin, Yu Zhou^*
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 >

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1991

Open Access

ARTICLE

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*
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 >

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2249

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ARTICLE

Shuo Zhang, Hanwu He, Yueming Wu^*
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 >

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2141

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ARTICLE

Darthy Rabecka V^1,*, Britto Pari J¹, Man-Fai Leung^2,*
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 >

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2053

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ARTICLE

Chuanchuan Wang^1,2, Ahmad Sufril Azlan Mohamed^2,*, Xiao Yang ², Hao Zhang ², Xiang Li¹, Mohd Halim Bin Mohd Noor ²
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 >

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2111

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ARTICLE

Ahmed I. Alutaibi^*
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 >

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2313

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Yuanqing Ding^1,2, Hanming Zhai¹, Qiming Ma¹, Liang Zhang¹, Lei Shao², Fanliang Bu^1,*
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 >

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2569

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2023

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ARTICLE

Hui Lv^1,#, Yuer Yang^2,3,4,#, Yifeng Lin^2,3,*
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 >

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2645

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2177

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ARTICLE

Fathe Jeribi^1,2, Ayesha Siddiqa^3,*, Hareem Kibriya⁴, Ali Tahir¹, Nadim Rana¹
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 >

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2238

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ARTICLE

Zekun Liu¹, Junwei Ma^1,2,*, Xin Gong¹, Xiu Liu¹, Bingbing Liu¹, Long An¹
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 >

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2152

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Kyle Stein^1,*, Andrew A. Mahyari^1,2, Guillermo Francia III³, Eman El-Sheikh³
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 >

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2046

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ARTICLE

Kejun Zhang¹, Meijiao Li^1,*, Jiahao Cheng¹, Jun Wang¹, Ying Yang²
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 >

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2456

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2034

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Heng Wang¹, Jian-Hua Qin^2,*
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 >

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2638

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2122

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ARTICLE

Huy Q. Tran¹, Tan Nguyen Van^2,*, Cheolkeun Ha³
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 >

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2499

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2107

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Xubo Liu¹, Yunxiang Liu², Peng Luo^2,*
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 >

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2128

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Yu-Sheng Su^1,2,3,*, Yi-Wen Wang¹, Yun-Chin Wu³, Zheng-Yun Xiao¹, Ting-Jou Ding⁴
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 >

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2671

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2223

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ARTICLE

Shanshan Liu¹, Liang Chang^1,*, Guanyu Hu^1,2, Shiyu Li¹
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 >

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2503

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2149

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Maazen Alsabaan¹, Jasmin Praful Bharadiya², Vishwanath Eswarakrishnan³, Adnan Mustafa Cheema⁴, Zaid Bin Faheem⁵, Jehad Ali^6,*
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 >

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2650

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2047

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Yong Liu^1,2, Tianning Sun^3,*, Daofu Gong^1,4, Li Di⁵, Xu Zhao¹
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1161-1184, 2025, DOI:10.32604/cmc.2025.062330 - 29 August 2025
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 >

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2657

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2082

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ARTICLE

Abdulatif Alabdulatif^*
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 >

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2087

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Rongrong Gong^1,#, Tingxian Zhang^2,#, Yawen Wei², Dengyong Zhang², Yan Li^3,*
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 >

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2134

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Yuanfei Wei^1,2, Panpan Song³, Qifang Luo^3,4,*, Yongquan Zhou^1,2,3,4
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 >

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2126

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Soufiane Khedairia¹, Akram Bennour^2,*, Mouaaz Nahas³, Aida Chefrour¹, Rashiq Rafiq Marie⁴, Mohammed Al-Sarem⁵
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 >

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3063

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2306

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Li Li, Xiao Wang^*, Ran Ding, Linlin Luo, Qinmu Wu, Zhiqin He
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 >

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2121

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Abrar Hussain¹, Hafiz Aftab Anwar¹, Kifayat Ullah^1,2, Dragan Pamucar^3,*, Vladimir Simic^4,5,6
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 >

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2507

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2074

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Lei Lei¹, Chengfu Wu^2,*, Huaimin Chen²
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1339-1363, 2025, DOI:10.32604/cmc.2025.067117 - 29 August 2025
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 >

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