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Xin Wen^1,2,3, Siru Chen¹, Kechen Song^2,3,4,*, Han Yu^2,3,*, Xingjie Li^2,3, Ling Zhong¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4173-4201, 2025, DOI:10.32604/cmc.2025.070906 - 23 October 2025
Abstract With the growing demand for higher product quality in manufacturing, X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applications, owing to its unique capability to penetrate materials and reveal both internal and surface defects. This paper presents a systematic review of recent advances and current applications of X-ray-based defect detection in industrial components. It begins with an overview of the fundamental principles of X-ray imaging and typical inspection workflows, followed by a review of classical image processing methods for defect detection, segmentation,… More >

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Thomas Hanne^1,*, Mohammad Jahani Moghaddam²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4203-4236, 2025, DOI:10.32604/cmc.2025.068087 - 23 October 2025
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their More >

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705

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Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

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302

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Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097 - 23 October 2025
Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >

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902

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341

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Inam Ullah Khan¹, Fida Muhammad Khan^1,*, Zeeshan Ali Haider¹, Fahad Alturise^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4307-4344, 2025, DOI:10.32604/cmc.2025.070189 - 23 October 2025
Abstract The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges due to the scale, complexity, and heterogeneity of interconnected devices. The current traditional centralized security models are deemed irrelevant in dealing with these threats, especially in decentralized applications where the IoT devices may at times operate on minimal resources. The emergence of new technologies, including Artificial Intelligence (AI), blockchain, edge computing, and Zero-Trust-Architecture (ZTA), is offering potential solutions as it helps with additional threat detection, data integrity, and system resilience in real-time. AI offers sophisticated anomaly detection and prediction analytics, and… More >

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701

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206

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Shengjia Chang, Baojiang Cui^*, Shaocong Feng
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4345-4374, 2025, DOI:10.32604/cmc.2025.070195 - 23 October 2025
Abstract Binary Code Similarity Detection (BCSD) is vital for vulnerability discovery, malware detection, and software security, especially when source code is unavailable. Yet, it faces challenges from semantic loss, recompilation variations, and obfuscation. Recent advances in artificial intelligence—particularly natural language processing (NLP), graph representation learning (GRL), and large language models (LLMs)—have markedly improved accuracy, enabling better recognition of code variants and deeper semantic understanding. This paper presents a comprehensive review of 82 studies published between 1975 and 2025, systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence (AI) techniques. Particular… More >

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Hiba Sameer Saeed^#, Amenah Dahim Abbood^#,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4375-4405, 2025, DOI:10.32604/cmc.2025.067783 - 23 October 2025
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms… More >

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626

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148

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Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025
Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

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202

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Nameer Baht^1,*, Enrique Domínguez^1,2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025
（This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)
Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >

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682

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281

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Ibrahim Yahaya Garta, William Eric Manongga, Su-Wen Huang, Rung-Ching Chen^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4457-4471, 2025, DOI:10.32604/cmc.2025.067149 - 23 October 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Unlike the detection of marked on-street parking spaces, detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking. In urban cities with heavy traffic flow, these challenges can result in traffic disruptions, rear-end collisions, sideswipes, and congestion as drivers struggle to make decisions. We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search. Lightweight versions of YOLOv5, YOLOv7-tiny, and YOLOv8 with different architectures are trained. Among the models, YOLOv5s with SPPF… More >

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663

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Yuanyuan Xiong¹, Tong Wu¹, Lixin Sun¹, Mingyu Hu², Jie Yu^1,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4473-4489, 2025, DOI:10.32604/cmc.2025.067722 - 23 October 2025
（This article belongs to the Special Issue: Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations)
Abstract Ag/Al₂O₃ powders are highly effective catalytic materials utilized in the epoxidation of ethylene to produce ethylene oxide. One of the critical challenges in this catalytic process is the stability of nano-sized Ag particles, especially during high-temperature catalysis. However, this issue can be effectively addressed through in-situ reaction synthesis. To gain a deeper understanding of the underlying mechanisms, the phase transformation process and the thermodynamic mechanism of the oxidation reaction in the Ag/Al₂O₃ system have been investigated using first-principles thermodynamic calculations in conjunction with traditional thermodynamic data. These calculations, whose accuracy has been verified, provide valuable insights into… More >

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107

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Salvatore Brischetto^*, Domenico Cesare, Tommaso Mondino
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4491-4518, 2025, DOI:10.32604/cmc.2025.068518 - 23 October 2025
（This article belongs to the Special Issue: Advanced Modeling of Smart and Composite Materials and Structures)
Abstract A three-dimensional (3D) analytical formulation is proposed to put together magnetic, electric and elastic fields to analyze the vibration modes of simply-supported layered piezo-electro-magnetic plates. The present 3D model allows analyses for layered smart plates in both open-circuit and closed-circuit configurations. The second-order differential equations written in the mixed curvilinear reference system govern the magneto-electro-elastic free vibration problem for multilayered plates. This set consists of the 3D equations of motion and the 3D divergence equations for the magnetic induction and electric displacement. Navier harmonic forms in the planar directions and the exponential matrix method in… More >

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651

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269

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Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4519-4550, 2025, DOI:10.32604/cmc.2025.071504 - 23 October 2025
Abstract Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary… More >

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599

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176

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Fengjuan Ding¹, Lu Suo², Tengjiao Hong^1,3,*, Fulong Dong¹, Dong Huang¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4551-4570, 2025, DOI:10.32604/cmc.2025.069565 - 23 October 2025
（This article belongs to the Special Issue: Applications of Neural Networks in Materials)
Abstract The complex phenomena that occur during the plastic deformation process of aluminum alloys, such as strain rate hardening, dynamic recovery, recrystallization, and damage evolution, can significantly affect the properties of these alloys and limit their applications. Therefore, studying the high-temperature flow stress characteristics of these materials and developing accurate constitutive models has significant scientific research value. In this study, quasi-static tensile tests were conducted on 5754 aluminum alloy using an electronic testing machine combined with a high-temperature environmental chamber to explore its plastic flow behavior under main deformation parameters (such as deformation temperatures, strain rates,… More >

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166

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Tao Chen¹, Xiaoxi Mi^2,*, Shibo Zhou^3,*, Shijun Tong¹, Yunxuan Zhou¹, Yulin Zhang¹, Yuan Yuan⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4571-4586, 2025, DOI:10.32604/cmc.2025.070649 - 23 October 2025
（This article belongs to the Special Issue: Machine Learning-Assisted Light Alloy Design)
Abstract Magnesium (Mg) alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases. To address this, machine learning was employed to predict the density and volume of Mg-containing binary phases, aiming to guide lightweight alloy design. Using 211 experimentally observed data points, five machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Bayesian Ridge (Bayes)—were trained and tested. Quantitative results showed that RF achieved exceptional performance in volume prediction, with a testing coefficient of determination (R²) exceeding 0.96 and More >

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Lin Huang^1,2, Ying Zhou², Jingjing Yang^1,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4587-4606, 2025, DOI:10.32604/cmc.2025.068370 - 23 October 2025
Abstract The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application. However, for rare and precious metal materials, existing data resources are often decentralized. This results in persistent issues such as data silos and fragmentation, which significantly hinder efficient data utilization and collaboration. In response to these challenges, this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials. The research begins by analyzing current trends in material data platforms, both domestically and internationally. These insights help inform the architectural… More >

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320

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116

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Qing-Xing Qu^1,*, Le Zhang^2,*, Fu Guo¹, Vincent G. Duffy³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4607-4627, 2025, DOI:10.32604/cmc.2025.069245 - 23 October 2025
Abstract This study aims to validate the Object-Oriented User Interface Customization (OOUIC) framework by employing Use Case Analysis (UCA) to facilitate the development of adaptive User Interfaces (UIs). The OOUIC framework advocates for User-Centered Design (UCD) methodologies, including UCA, to systematically identify intricate user requirements and construct adaptive UIs tailored to diverse user needs. To operationalize this approach, thirty users of Product Lifecycle Management (PLM) systems were interviewed across six distinct use cases. Interview transcripts were subjected to deductive content analysis to classify UI objects systematically. Subsequently, adaptive UIs were developed for each use case, and… More >

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751

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312

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Segyeong Bang^#, Soeun Kim^#, Gaeun Ahn, Hyemin Hong, Junhyoung Oh^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4629-4643, 2025, DOI:10.32604/cmc.2025.068221 - 23 October 2025
Abstract This study analyzes the risks of re-identification in Korean text data and proposes a secure, ethical approach to data anonymization. Following the ‘Lee Luda’ AI chatbot incident, concerns over data privacy have increased. The Personal Information Protection Commission of Korea conducted inspections of AI services, uncovering 850 cases of personal information in user input datasets, highlighting the need for pseudonymization standards. While current anonymization techniques remove personal data like names, phone numbers, and addresses, linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data. To address this,… More >

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Jiming Lan¹, Hongwei Fu^1,*, Yadong Wu^1,2, Yaxian Liu^1,3, Jianhua Dong^1,2, Wei Liu^1,2, Huaqiang Chen^1,2
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4645-4665, 2025, DOI:10.32604/cmc.2025.067289 - 23 October 2025
Abstract In the research and production of fluorinated materials, large volumes of unstructured textual data are generated, characterized by high heterogeneity and fragmentation. These issues hinder systematic knowledge integration and efficient utilization. Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications. Among its core components, Named Entity Recognition (NER) plays an essential role, as its accuracy directly impacts relation extraction and semantic modeling, which ultimately affects the knowledge graph construction for fluorinated materials. However, NER in this domain faces challenges such as fuzzy entity boundaries, inconsistent terminology,… More >

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Khadija Slimani^1,2,*, Samira Khoulji², Hamed Taherdoost^3,4, Mohamed Larbi Kerkeb⁵
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4667-4686, 2025, DOI:10.32604/cmc.2025.070173 - 23 October 2025
Abstract The integration of the dynamic adaptive routing (DAR) algorithm in unmanned aerial vehicle (UAV) networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G. DAR’s innovative framework incorporates real-time path adjustments, energy-aware routing, and predictive models, optimizing reliability, latency, and energy efficiency in UAV operations. This study demonstrated DAR’s superior performance in dynamic, large-scale environments, proving its adaptability and scalability for real-time applications. As 6G networks evolve, challenges such as bandwidth demands, global spectrum management, security vulnerabilities, and financial feasibility become prominent. DAR aligns with these demands by offering More >

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Pasi Fränti^1,*, Claude Cariou², Qinpei Zhao³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4687-4704, 2025, DOI:10.32604/cmc.2025.066534 - 23 October 2025
Abstract K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances. We show that, as a consequence, it also maximizes between-cluster variances. This means that the two measures do not provide complementary information and that using only one is enough. Based on this property, we propose a new objective function called cluster overlap, which is measured intuitively as the proportion of points shared between the clusters. We adopt the new function within k-means and present an algorithm called overlap k-means. It is an alternative way to design a k-means algorithm. A localized variant is also More >

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Bappa Muktar^*, Vincent Fono, Adama Nouboukpo
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025
（This article belongs to the Special Issue: Smart Roads, Smarter Cars, Safety and Security: Evolution of Vehicular Ad Hoc Networks)
Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… More >

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Juan Guerrero-Martín^*, Eladio Estella-Nonay, Margarita Bachiller-Mayoral, Mariano Rincón
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4729-4752, 2025, DOI:10.32604/cmc.2025.066083 - 23 October 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Neuropsychological tests, such as the Rey-Osterrieth complex figure (ROCF) test, help detect mild cognitive impairment (MCI) in adults by assessing cognitive abilities such as planning, organization, and memory. Furthermore, they are inexpensive and minimally invasive, making them excellent tools for early screening. In this paper, we propose the use of image analysis models to characterize the relationship between an individual’s ROCF drawing and their cognitive state. This task is usually framed as a classification problem and is solved using deep learning models, due to their success in the last decade. In order to achieve good… More >

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Yulin Yao, Kangfeng Zheng, Bin Wu^*, Chunhua Wu, Jiaqi Gao, Jvjie Wang, Minjiao Yang
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4753-4776, 2025, DOI:10.32604/cmc.2025.065059 - 23 October 2025
Abstract Cognitive biases are commonly used by attackers to manipulate users’ psychology in phishing emails. This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions: (1) Which cognitive biases are frequently exploited in phishing emails? (2) How are cognitive biases exploited in phishing emails? (3) How effective are cognitive bias features in detecting phishing emails? (4) How can the exploitation of cognitive biases in phishing emails be modelled? To address these questions, this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases More >

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Asif Nawaz^1,*, Rana Saud Shoukat², Mohammad Shehab¹, Khalil El Hindi³, Zohair Ahmed⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4777-4793, 2025, DOI:10.32604/cmc.2025.064088 - 23 October 2025
Abstract The kingdom Animalia encompasses multicellular, eukaryotic organisms known as animals. Currently, there are approximately 1.5 million identified species of living animals, including over 195 distinct breeds of dogs. Each breed possesses unique characteristics that can be challenging to distinguish. Each breed has its own characteristics that are difficult to identify. Various computer-based methods, including machine learning, deep learning, transfer learning, and robotics, are employed to identify dog breeds, focusing mainly on image or voice data. Voice-based techniques often face challenges such as noise, distortion, and changes in frequency or pitch, which can impair the model’s… More >

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Nujud Aloshban^*, Abeer A.K. Alharbi
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4795-4826, 2025, DOI:10.32604/cmc.2025.067523 - 23 October 2025
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent, real-time traffic forecasting solutions that preserve data privacy. This paper introduces NeuroCivitas, a federated deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial pattern recognition and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. Designed to meet the adaptive intelligence requirements of cognitive cities, NeuroCivitas leverages Federated Averaging (FedAvg) to ensure privacy-preserving training while significantly reducing communication overhead—by 98.7% compared to centralized models. The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records… More >

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Kunal Thakur¹, Ashu Taneja^1,*, Ali Alqahtani², Nayef Alqahtani³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025
Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

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Qin Qin¹, Xuan Jiang^1,*, Jinhua Jiang¹, Dongfang Zhao¹, Zimei Tu¹, Zhiwei Shen²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4849-4867, 2025, DOI:10.32604/cmc.2025.067919 - 23 October 2025
Abstract The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion, surface defects, and low contrast, which hinder the extraction of reliable visual information. The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments. This makes conventional OCR techniques and standard deep learning models unreliable. To address these limitations, this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs. A Biorthogonal 4.4 (bior4.4) wavelet transform is adaptively tuned using DE to enhance character… More >

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Syeda Zoupash Zahra¹, Najia Saher², Malik Muhammad Saad Missen³, Rab Nawaz Bashir^4,5, Salma Idris⁵, Tahani Jaser Alahmadi^6,*, Muhammad Inshal Khan⁵
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4869-4883, 2025, DOI:10.32604/cmc.2025.067639 - 23 October 2025
Abstract The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage. This study presents a comprehensive multi-hazard early warning framework by integrating the multi-source data fusion technique. A multi-source data extraction method was introduced by extracting pressure level and average precipitation data based on the hazard event from the Cooperative Open Online Landslide Repository (COOLR) dataset across multiple temporal intervals (12 h to 1 h prior to events). Feature engineering was performed using Choquet fuzzy integral-based importance scoring, which enables the model to account for interactions and uncertainty across multiple… More >

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624

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Tian Tian^1,2, Boyue Wang^1,2, Xiaxia He^1,2,*, Wentong Wang³, Meng Wang¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4885-4898, 2025, DOI:10.32604/cmc.2025.067795 - 23 October 2025
Abstract Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner, which usually encodes the node attribute feature and the corresponding graph structure into a latent feature space. However, traditional attribute-graph clustering methods often neglect the effect of neighbor information on clustering, leading to suboptimal clustering results as they fail to fully leverage the rich contextual information provided by neighboring nodes, which is crucial for capturing the intrinsic relationships between nodes and improving clustering performance. In this paper, we propose a novel Neighbor Dual-Consistency Constrained Attribute-Graph Clustering that leverages information from… More >

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Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

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190

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Remah Younisse^1,#, Mouhammd AlKasassbeh^1,#, Amjad Aldweesh^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4921-4941, 2025, DOI:10.32604/cmc.2025.067818 - 23 October 2025
Abstract Smart grid systems are advancing electrical services, making them more compatible with Internet of Things (IoT) technologies. The deployment of smart grids is facing many difficulties, requiring immediate solutions to enhance their practicality. Data privacy and security are widely discussed, and many solutions are proposed in this area. Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks. The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems, which is commonly solved via fog computing. This… More >

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Nada Alasbali^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4943-4958, 2025, DOI:10.32604/cmc.2025.068330 - 23 October 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 Intelligent Traffic Management (ITM) has progressively developed into a critical component of modern transportation networks, significantly enhancing traffic flow and reducing congestion in urban environments. This research proposes an enhanced framework that leverages Deep Q-Learning (DQL), Game Theory (GT), and Stochastic Optimization (SO) to tackle the complex dynamics in transportation networks. The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation, ensuring resilient decision-making. GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance. Results demonstrate that the proposed More >

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Siraj Un Muneer¹, Ihsan Ullah¹, Zeshan Iqbal^2,*, Rajermani Thinakaran³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4959-4975, 2025, DOI:10.32604/cmc.2025.068566 - 23 October 2025
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The complexity of cloud environments challenges secure resource management, especially for intrusion detection systems (IDS). Existing strategies struggle to balance efficiency, cost fairness, and threat resilience. This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm (GA) with a “double auction” method. This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework. It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders. The GA functions as an intelligent search mechanism that identifies optimal combinations of bids More >

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Hanyong Lee^#, Ye-Chan Park^#, Jaesung Lee^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4977-4993, 2025, DOI:10.32604/cmc.2025.068156 - 23 October 2025
Abstract Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a… More >

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Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4995-5013, 2025, DOI:10.32604/cmc.2025.068429 - 23 October 2025
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract Ensuring the secure transmission of secret messages, particularly through video—one of the most widely used media formats—is a critical challenge in the field of information security. Relying on a single-layered security approach is often insufficient for safeguarding sensitive data. This study proposes a triple-lightweight cryptographic and steganographic model that integrates the Hill Cipher Technique (HCT), Rotation Left Digits (RLD), and Discrete Wavelet Transform (DWT) to embed secret messages within video frames securely. The approach begins with encrypting the secret text using a private key matrix (PK₁) of size 2 × 2 up to 6 × 6… More >

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247

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Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025
Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >

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189

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Fang Yuan, Xueyao Gao^*, Chunxiang Zhang
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5037-5055, 2025, DOI:10.32604/cmc.2025.067760 - 23 October 2025
Abstract 3D model classification has emerged as a significant research focus in computer vision. However, traditional convolutional neural networks (CNNs) often struggle to capture global dependencies across both height and width dimensions simultaneously, leading to limited feature representation capabilities when handling complex visual tasks. To address this challenge, we propose a novel 3D model classification network named ViT-GE (Vision Transformer with Global and Efficient Attention), which integrates Global Grouped Coordinate Attention (GGCA) and Efficient Channel Attention (ECA) mechanisms. Specifically, the Vision Transformer (ViT) is employed to extract comprehensive global features from multi-view inputs using its self-attention More >

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176

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Lincong Zhao¹, Liandong Chen¹, Peipei Shen¹, Zizhou Liu¹, Chengzhu Li¹, Fanqin Zhou^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5057-5071, 2025, DOI:10.32604/cmc.2025.067521 - 23 October 2025
Abstract The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate. Federated learning offers a promising solution to expedite the training of security assessment models. However, ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge. To address these issues, this study proposes a shard aggregation network structure and a malicious node detection mechanism, along with improvements to the federated learning training process. First, we extract the data features of the participants by using spectral clustering methods combined… More >

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179

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Arham Adnan¹, Muhammad Tuaha Rizwan¹, Hafiz Muhammad Attaullah^1,2,*, Shakila Basheer³, Mohammad Tabrez Quasim⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5073-5091, 2025, DOI:10.32604/cmc.2025.068797 - 23 October 2025
Abstract Artificial intelligence (AI), particularly deep learning algorithms utilizing convolutional neural networks, plays an increasingly pivotal role in enhancing medical image examination. It demonstrates the potential for improving diagnostic accuracy within dental care. Orthopantomograms (OPGs) are essential in dentistry; however, their manual interpretation is often inconsistent and tedious. To the best of our knowledge, this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images. The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later… More >

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162

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Marwa Ahmim¹, Nour Ouafi¹, Insaf Ullah^2,*, Ahmed Ahmim³, Djalel Chefrour³, Reham Almukhlifi⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5093-5116, 2025, DOI:10.32604/cmc.2025.067641 - 23 October 2025
Abstract The Internet of Healthcare Things (IoHT) marks a significant breakthrough in modern medicine by enabling a new era of healthcare services. IoHT supports real-time, continuous, and personalized monitoring of patients’ health conditions. However, the security of sensitive data exchanged within IoHT remains a major concern, as the widespread connectivity and wireless nature of these systems expose them to various vulnerabilities. Potential threats include unauthorized access, device compromise, data breaches, and data alteration, all of which may compromise the confidentiality and integrity of patient information. In this paper, we provide an in-depth security analysis of LAP-IoHT,… More >

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162

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ARTICLE

Wei Lin, Yi Sun^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5117-5134, 2025, DOI:10.32604/cmc.2025.067968 - 23 October 2025
Abstract To enable efficient sharing of unbounded streaming data, this paper introduces blockchain technology into traditional cloud data, proposing a hybrid on-chain/off-chain storage model. We design a real-time verifiable data structure that is more suitable for streaming data to achieve efficient real-time verifiability for streaming data. Based on the notch gate hash function and vector commitment, an adaptive notch gate hash tree structure is constructed, and an efficient real-time verifiable data structure for on-chain and off-chain stream data is proposed. The structure binds dynamic root nodes sequentially to ordered leaf nodes in its child nodes. Only… More >

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159

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Candan Tumer¹, Erdal Guvenoglu², Volkan Tunali^3,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5135-5158, 2025, DOI:10.32604/cmc.2025.067840 - 23 October 2025
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Automated cartoon character recognition is crucial for applications in content indexing, filtering, and copyright protection, yet it faces a significant challenge in animated media due to high intra-class visual variability, where characters frequently alter their appearance. To address this problem, we introduce the novel Kral Sakir dataset, a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions. This paper conducts a comprehensive benchmark study, evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks (CNNs), including DenseNet, ResNet, and VGG, against a custom baseline model trained More >

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167

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ARTICLE

Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025
Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

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231

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ARTICLE

Muhammad Sajid Farooq¹, Muhammad Saleem², M.A. Khan^3,4, Muhammad Farrukh Khan⁵, Shahan Yamin Siddiqui⁶, Muhammad Shoukat Aslam⁷, Khan M. Adnan^8,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5183-5206, 2025, DOI:10.32604/cmc.2025.069641 - 23 October 2025
（This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)
Abstract The rapid evolution of smart cities through IoT, cloud computing, and connected infrastructures has significantly enhanced sectors such as transportation, healthcare, energy, and public safety, but also increased exposure to sophisticated cyber threats. The diversity of devices, high data volumes, and real-time operational demands complicate security, requiring not just robust intrusion detection but also effective feature selection for relevance and scalability. Traditional Machine Learning (ML) based Intrusion Detection System (IDS) improves detection but often lacks interpretability, limiting stakeholder trust and timely responses. Moreover, centralized feature selection in conventional IDS compromises data privacy and fails to… More >

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ARTICLE

Abeer S. Al-Humaimeedy^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5207-5236, 2025, DOI:10.32604/cmc.2025.068273 - 23 October 2025
Abstract This paper presents 3RVAV (Three-Round Voting with Advanced Validation), a novel Byzantine Fault Tolerant consensus protocol combining Proof-of-Stake with a multi-phase voting mechanism. The protocol introduces three layers of randomized committee voting with distinct participant roles (Validators, Delegators, and Users), achieving -threshold approval per round through a verifiable random function (VRF)-based selection process. Our security analysis demonstrates 3RVAV provides resistance to Sybil attacks with participants and stake , while maintaining communication complexity. Experimental simulations show 3247 TPS throughput with 4-s finality, representing a 5.8× improvement over Algorand’s committee-based approach. The proposed protocol achieves approximately 4.2-s More >

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250

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ARTICLE

Abrar M. Alajlan^1,*, Marwah M. Almasri²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5237-5262, 2025, DOI:10.32604/cmc.2025.066736 - 23 October 2025
Abstract Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats. Attackers can non-invasively manipulate sensors and spoof controllers, which in turn increases the autonomy of the system. Even though the focus on protecting against sensor attacks increases, there is still uncertainty about the optimal timing for attack detection. Existing systems often struggle to manage the trade-off between latency and false alarm rate, leading to inefficiencies in real-time anomaly detection. This paper presents a framework designed to monitor, predict, and control dynamic systems with a particular emphasis on detecting and adapting to… More >

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229

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Siliang Chen¹, Dongri Shan^2,*, Yansheng Niu³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5263-5286, 2025, DOI:10.32604/cmc.2025.067892 - 23 October 2025
（This article belongs to the Special Issue: Collaborative Edge Intelligence and Its Emerging Applications)
Abstract With the increasing deployment of Unmanned Aerial Vehicle-Hangar (UAV-H) clusters in dynamic environments such as disaster response and precision agriculture, existing networking schemes often struggle with adaptability to complex scenarios, while traditional Vertical Handoff (VHO) algorithms fail to fully address the unique challenges of UAV-H systems, including high-speed mobility and limited computational resources. To bridge this gap, this paper proposes a heterogeneous network architecture integrating 5th Generation Mobile Communication Technology (5G) cellular networks and self-organizing mesh networks for UAV-H clusters, accompanied by a novel VHO algorithm. The proposed algorithm leverages Multi-Attribute Decision-Making (MADM) theory combined… More >

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121

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Yahya Alhaj Maz¹, Mohammed Anbar¹, Selvakumar Manickam^1,*, Mosleh M. Abualhaj², Sultan Ahmed Almalki³, Basim Ahmad Alabsi⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5287-5307, 2025, DOI:10.32604/cmc.2025.068257 - 23 October 2025
（This article belongs to the Special Issue: Towards Privacy-preserving, Secure and Trustworthy AI-enabled Systems)
Abstract The Internet of Things (IoT) is an innovation that combines imagined space with the actual world on a single platform. Because of the recent rapid rise of IoT devices, there has been a lack of standards, leading to a massive increase in unprotected devices connecting to networks. Consequently, cyberattacks on IoT are becoming more common, particularly keylogging attacks, which are often caused by security vulnerabilities on IoT networks. This research focuses on the role of transfer learning and ensemble classifiers in enhancing the detection of keylogging attacks within small, imbalanced IoT datasets. The authors propose… More >

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163

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Xiaofang Jin, Yiran Li^*, Yuying Yang
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5309-5326, 2025, DOI:10.32604/cmc.2025.067812 - 23 October 2025
Abstract Given the importance of sentiment analysis in diverse environments, various methods are used for image sentiment analysis, including contextual sentiment analysis that utilizes character and scene relationships. However, most existing works employ character faces in conjunction with context, yet lack the capacity to analyze the emotions of characters in unconstrained environments, such as when their faces are obscured or blurred. Accordingly, this article presents the Adaptive Multi-Channel Sentiment Analysis Network (AMSA), a contextual image sentiment analysis framework, which consists of three channels: body, face, and context. AMSA employs Multi-task Cascaded Convolutional Networks (MTCNN) to detect More >

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