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Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3585-3622, 2025, DOI:10.32604/cmc.2025.062867 - 19 May 2025
（This article belongs to the Special Issue: AI and Advanced High-Tech Research and Development)
Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

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1687

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Aamir Farooq¹, Zhengrong Xiang^1,*, Wen-Jer Chang^2,*, Muhammad Shamrooz Aslam³
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3623-3674, 2025, DOI:10.32604/cmc.2025.063665 - 19 May 2025
Abstract Formation control in multi-agent systems has become a critical area of interest due to its wide-ranging applications in robotics, autonomous transportation, and surveillance. While various studies have explored distributed cooperative control, this review focuses on the theoretical foundations and recent developments in formation control strategies. The paper categorizes and analyzes key formation types, including formation maintenance, group or cluster formation, bipartite formations, event-triggered formations, finite-time convergence, and constrained formations. A significant portion of the review addresses formation control under constrained dynamics, presenting both model-based and model-free approaches that consider practical limitations such as actuator bounds,… More >

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662

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Bisni Fahad Mon¹, Mohammad Hayajneh^1,2,*, Najah Abu Ali¹, Farman Ullah¹, Hikmat Ullah³, Shayma Alkobaisi⁴
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3675-3712, 2025, DOI:10.32604/cmc.2025.061411 - 19 May 2025
（This article belongs to the Special Issue: Exploring the Metaverse: Advancements in Future Networks Communication and Computing Technologies for Enhanced Quality of Experience)
Abstract In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an… More >

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699

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Arshiya Sajid Ansari^1,*, Ghadir Altuwaijri², Fahad Alodhyani¹, Moulay Ibrahim El-Khalil Ghembaza³, Shahabas Manakunnath Devasam Paramb³, Mohammad Sajid Mohammadi³
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3713-3752, 2025, DOI:10.32604/cmc.2025.064027 - 19 May 2025
（This article belongs to the Special Issue: Safe and Secure Artificial Intelligence)
Abstract IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing… More >

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250

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Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3753-3841, 2025, DOI:10.32604/cmc.2025.063643 - 19 May 2025
Abstract This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning… More >

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635

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Chuan Li, Xuanlin Wen^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3843-3875, 2025, DOI:10.32604/cmc.2025.063047 - 19 May 2025
Abstract Spark performs excellently in large-scale data-parallel computing and iterative processing. However, with the increase in data size and program complexity, the default scheduling strategy has difficulty meeting the demands of resource utilization and performance optimization. Scheduling strategy optimization, as a key direction for improving Spark’s execution efficiency, has attracted widespread attention. This paper first introduces the basic theories of Spark, compares several default scheduling strategies, and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency. Subsequently, existing scheduling optimization schemes are summarized based on three scheduling modes: load characteristics, cluster characteristics, and matching More >

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808

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407

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Peicheng Shi^1,*, Li Yang¹, Xinlong Dong¹, Heng Qi², Aixi Yang³
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3877-3917, 2025, DOI:10.32604/cmc.2025.063205 - 19 May 2025
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >

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831

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Takanobu Hirosawa^*, Taro Shimizu
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025
（This article belongs to the Special Issue: Generative AI for Recommendation Services)
Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

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783

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379

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Huu-Tuong Ho¹, Thi-Thuy-Hoai Nguyen², Duong Nguyen Minh Huy³, Luong Vuong Nguyen^1,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3945-3973, 2025, DOI:10.32604/cmc.2025.063099 - 19 May 2025
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze More >

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220

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Shrabani Sutradhar^1,2, Rajesh Bose³, Sudipta Majumder¹, Arfat Ahmad Khan^4,*, Sandip Roy³, Fasee Ullah⁵, Deepak Prashar^6,7
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3975-4029, 2025, DOI:10.32604/cmc.2025.061965 - 19 May 2025
Abstract Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe… More >

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228

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Uddagiri Sirisha^1,*, Chanumolu Kiran Kumar², Revathi Durgam³, Poluru Eswaraiah⁴, G Muni Nagamani⁵
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4031-4059, 2025, DOI:10.32604/cmc.2025.063721 - 19 May 2025
（This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)
Abstract A complete examination of Large Language Models’ strengths, problems, and applications is needed due to their rising use across disciplines. Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance, strengths, and weaknesses. This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies. In this research, 50 studies on 25+ LLMs, including GPT-3, GPT-4, Claude 3.5, DeepKet, and hybrid multimodal frameworks like ContextDET and GeoRSCLIP, are thoroughly reviewed. We propose LLM application taxonomy by grouping techniques by task focus—healthcare,… More >

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221

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Milinda Priyankara Bandara Gamawelagedara¹, Mian Usman Sattar¹, Raza Hasan^2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4061-4084, 2025, DOI:10.32604/cmc.2025.062826 - 19 May 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >

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464

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Yury V. Knyazev, Semyon T. Baidak, Yury I. Kuz’min, Alexey V. Lukoyanov^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4085-4095, 2025, DOI:10.32604/cmc.2025.065091 - 19 May 2025
（This article belongs to the Special Issue: Study on the Photophysical Mechanism of Complex Systems based on First Principles Calculations)
Abstract The work presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds. Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling. These compounds are computed to be semiconductors. The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters. In ScNiBi and YNiBi, an intense peak at the energy of −2 eV is composed of the Ni 3d states in the conduction band, and the valence band mostly contains these states with some contributions from the… More >

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648

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193

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Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4097-4116, 2025, DOI:10.32604/cmc.2025.063468 - 19 May 2025
Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

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363

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Tianyu Li¹, Dezhi Han¹, Jiatao Li¹, Kuan-Ching Li^2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4117-4140, 2025, DOI:10.32604/cmc.2025.063630 - 19 May 2025
Abstract As is known, centralized federated learning faces risks of a single point of failure and privacy breaches, and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works. However, malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data. In addition, blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead. To address these issues, we propose an asynchronous, tiered federated learning storage scheme based on blockchain and IPFS. It manages the execution of federated learning tasks through smart contracts… More >

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376

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154

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Haotong Wang^*, Yves Lepage
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4141-4166, 2025, DOI:10.32604/cmc.2025.062806 - 19 May 2025
Abstract Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks, and generating descriptive text based on these graphs places significant emphasis on content consistency. However, knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes, making it challenging to ensure content coherence in generating text that spans multiple sentences. This lack of coherence can further compromise the overall consistency of the content within a paragraph. In this work, we present the generation of scientific abstracts by leveraging knowledge graphs, with a focus on enhancing both… More >

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416

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160

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Jixiang Wang¹, Jing Wei², Siqi Chen¹, Haiyang Yu^1,3,4, Yilong Ren^1,3,4,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4167-4192, 2025, DOI:10.32604/cmc.2025.062503 - 19 May 2025
Abstract The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation. While existing vehicle-to-infrastructure coordination frameworks partially address congestion mitigation, they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles. To bridge this gap, this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol, explicitly balancing system-wide efficiency (measured by network throughput) with priority vehicle rights protection (quantified via time-sensitive utility functions). The approach innovatively combines (1) a multi-vehicle… More >

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525

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218

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He Su, Jianwei Gao, Kang Kong^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025
Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

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440

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198

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Praveen Kumar Sekharamantry^1,2,*, Farid Melgani¹, Roberto Delfiore³, Stefano Lusardi³
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4215-4238, 2025, DOI:10.32604/cmc.2025.062686 - 19 May 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >

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549

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Gaoshang Lu^#, Fa Fu^#,*, Zixiang Tang
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4239-4256, 2025, DOI:10.32604/cmc.2025.062556 - 19 May 2025
Abstract In the data transaction process within a data asset trading platform, quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures. To address this issue, a distributed data source trust assessment management framework, a trust quantification model, and a dynamic adjustment mechanism are proposed. The model integrates the Analytic Hierarchy Process (AHP) and Dempster-Shafer (D-S) evidence theory to determine attribute weights and calculate direct trust values, while the PageRank algorithm is employed to derive indirect trust values. The direct and indirect trust values are then combined to compute More >

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417

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Rajendren Subramaniam¹, Saaidal Razalli Azzuhri^2,*, Teh Ying Wah¹, Atif Mahmood³, Vimala Balakrishnan¹
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4257-4280, 2025, DOI:10.32604/cmc.2025.061560 - 19 May 2025
Abstract Counterfeiting is still a pervasive global issue, affecting multiple industries and hindering industrial innovation, while causing substantial financial losses, reputational damage, and risks to consumer safety. From luxury goods and pharmaceuticals to electronics and automotive parts, counterfeit products infiltrate supply chains, leading to a loss of revenue for legitimate businesses and undermining consumer trust. Traditional anti-counterfeiting measures, such as holograms, serial numbers, and barcodes, have proven to be insufficient as counterfeiters continuously develop more sophisticated replication techniques. As a result, there is a growing need for more advanced, secure, and reliable methods to prevent counterfeiting.… More >

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853

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Qichang Li^1,2,*, Bing Bu¹, Junyi Zhao¹
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4281-4313, 2025, DOI:10.32604/cmc.2025.061525 - 19 May 2025
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract With the integration of informatization and intelligence into the Communication-Based Train Control (CBTC) systems, the system is facing an increasing number of information security threats. As an important method of characterizing the system security status, the security situation assessment is used to analyze the system security situation. However, existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems, and cannot dynamically characterize the real-time security situation changes under cyber attacks. In this paper, a hierarchical security situation assessment approach is proposed to address… More >

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Yong Zheng^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4315-4328, 2025, DOI:10.32604/cmc.2025.063498 - 19 May 2025
Abstract A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts… More >

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Huiling Yu¹, Xibei Jia², Yongfeng Niu¹, Yizhuo Zhang^1,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4329-4352, 2025, DOI:10.32604/cmc.2025.061882 - 19 May 2025
Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >

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Zijun Gao^*, Zheyi Li, Chunqi Zhang, Ying Wang, Jingwen Su
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4353-4371, 2025, DOI:10.32604/cmc.2025.061743 - 19 May 2025
Abstract Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks; however, their application in the actual agricultural production process is still challenging owing to the problems of inter-species similarity, multi-scale, and background complexity of pests. To address these problems, this study proposes an FD-YOLO pest target detection model. The FD-YOLO model uses a Fully Connected Feature Pyramid Network (FC-FPN) instead of a PANet in the neck, which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer, enhance large-scale target features in the… More >

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Thien-Tan Cao, Huu-Thanh Duong, Viet-Tuan Le, Hau Nguyen Trung, Vinh Truong Hoang, Kiet Tran-Trung^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4373-4391, 2025, DOI:10.32604/cmc.2025.061583 - 19 May 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Kinship verification is a key biometric recognition task that determines biological relationships based on physical features. Traditional methods predominantly use facial recognition, leveraging established techniques and extensive datasets. However, recent research has highlighted ear recognition as a promising alternative, offering advantages in robustness against variations in facial expressions, aging, and occlusions. Despite its potential, a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively. To address this challenge, we introduce the EarKinshipVN dataset, a novel and extensive collection of ear images designed specifically for kinship… More >

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Peiying Zhang^1,2,*, Yihong Yu^1,2, Jing Liu³, Chong Lv^1,2, Lizhuang Tan^4,5, Yulin Zhang^6,7,8
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654 - 19 May 2025
（This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

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Aqsa Rahim^*, Fuqing Yuan, Javad Barabady
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4411-4428, 2025, DOI:10.32604/cmc.2025.061575 - 19 May 2025
Abstract In recent years, advancements in autonomous vehicle technology have accelerated, promising safer and more efficient transportation systems. However, achieving fully autonomous driving in challenging weather conditions, particularly in snowy environments, remains a challenge. Snow-covered roads introduce unpredictable surface conditions, occlusions, and reduced visibility, that require robust and adaptive path detection algorithms. This paper presents an enhanced road detection framework for snowy environments, leveraging Simple Framework for Contrastive Learning of Visual Representations (SimCLR) for Self-Supervised pretraining, hyperparameter optimization, and uncertainty-aware object detection to improve the performance of You Only Look Once version 8 (YOLOv8). The model… More >

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Muhammad Qasim^1,2, Syed M. Adnan Shah¹, Qamas Gul Khan Safi¹, Danish Mahmood², Adeel Iqbal^3,*, Ali Nauman³, Sung Won Kim^3,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025
（This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)
Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… More >

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Longgang Zhao¹, Seok-Won Lee^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4447-4476, 2025, DOI:10.32604/cmc.2025.063691 - 19 May 2025
Abstract This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. More >

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Qiming Li¹, Mengcheng Wu¹, Daozheng Chen^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4477-4494, 2025, DOI:10.32604/cmc.2025.062969 - 19 May 2025
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract Image style transfer is a research hotspot in the field of computer vision. For this job, many approaches have been put forth. These techniques do, however, still have some drawbacks, such as high computing complexity and content distortion caused by inadequate stylization. To address these problems, PhotoGAN, a new Generative Adversarial Network (GAN) model is proposed in this paper. A deeper feature extraction network has been designed to capture global information and local details better. Introducing multi-scale attention modules helps the generator focus on important feature areas at different scales, further enhancing the effectiveness of More >

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506

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207

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Hui Xu, Jiahui Chen^*, Zhonghao Hu
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

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Abdullah M. Alnajim^1,*, Faisal Mohammed Alotaibi^2,#, Sheroz Khan^3,#
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4515-4535, 2025, DOI:10.32604/cmc.2025.063139 - 19 May 2025
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically… More >

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Changlong Wang¹, Jiawei Jiang¹, Chong Han^1,2,*, Hengyi Ren³, Lijuan Sun^1,2, Jian Guo^1,2
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4537-4550, 2025, DOI:10.32604/cmc.2025.063295 - 19 May 2025
Abstract Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body. However, static individuals, lacking prominent motion features, do not generate Doppler information. Moreover, radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls, limiting recognition performance. To address these challenges, this study proposes a novel through-wall human activity recognition method based on MIMO radar. Utilizing a MIMO radar operating at 1–2 GHz, we capture activity data of individuals through walls and process it into range-angle maps to represent activity features. To… More >

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222

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Jing He¹, Haonan Zhu², Chenhao Zhao¹, Minrui Zhao^3,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4551-4573, 2025, DOI:10.32604/cmc.2025.062437 - 19 May 2025
Abstract Self-supervised monocular depth estimation has emerged as a major research focus in recent years, primarily due to the elimination of ground-truth depth dependence. However, the prevailing architectures in this domain suffer from inherent limitations: existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions. These assumptions are often violated in real-world scenarios due to dynamic objects, non-Lambertian reflectance, and unstructured background elements, leading to pervasive artifacts such as depth discontinuities (“holes”), structural collapse, and ambiguous reconstruction. To address these challenges, we propose a novel framework that integrates scene dynamic pose estimation into… More >

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401

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Taher Alzahrani^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

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282

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Qin Wang¹, Xiaofeng Wang^2,*, Jianghua Li², Ruidong Han², Zinian Liu¹, Mingtao Guo³
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4607-4621, 2025, DOI:10.32604/cmc.2025.063141 - 19 May 2025
Abstract The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged… More >

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253

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Sulaiman Al Amro^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4623-4658, 2025, DOI:10.32604/cmc.2025.063734 - 19 May 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 rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy.… More >

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363

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Huyong Yan¹, Huidong Zhou^2,*, Jian Zheng¹, Zhaozhe Zhou¹
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4659-4677, 2025, DOI:10.32604/cmc.2025.062807 - 19 May 2025
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract As smart manufacturing and Industry 4.0 continue to evolve, fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization. To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions, this paper introduces the CNN-1D-KAN model, which combines a 1D Convolutional Neural Network (1D-CNN) with a Kolmogorov–Arnold Network (KAN). The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer, leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations. Experimental… More >

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240

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Dang Hung Tran, Van Tinh Nguyen^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

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Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

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Jingcheng Yang¹, Lili Fan², Hongmei Liu^1,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4725-4740, 2025, DOI:10.32604/cmc.2025.062963 - 19 May 2025
Abstract With the advancement of deep learning in the automotive domain, more and more researchers are focusing on autonomous driving. Among these tasks, free space detection is particularly crucial. Currently, many model-based approaches have achieved autonomous driving on well-structured urban roads, but these efforts primarily focus on urban road environments. In contrast, there are fewer deep learning methods specifically designed for off-road traversable area detection, and their effectiveness is not yet satisfactory. This is because detecting traversable areas in complex outdoor environments poses significant challenges, and current methods often rely on single-image inputs, which do not… More >

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530

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204

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Phi Thuong Le¹, Huy Quoc Le², Viet Cuong Trinh^1,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4741-4756, 2025, DOI:10.32604/cmc.2025.064486 - 19 May 2025
Abstract Attribute-based encryption () is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes. is widely applied in cloud storage, file sharing, e-Health, and digital rights management. schemes rely on hard cryptographic assumptions such as pairings and others (pairing-free) to ensure their security against external and internal attacks. Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent. One common internal attack is the attribute collusion attack, in which users with different attribute keys collaborate to decrypt data they could not… More >

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182

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Qing Xie^*, Ruiyun Yu
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4757-4774, 2025, DOI:10.32604/cmc.2025.063345 - 19 May 2025
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract This study presents a groundbreaking method named Expo-GAN (Exposition-Generative Adversarial Network) for style transfer in exhibition hall design, using a refined version of the Cycle Generative Adversarial Network (CycleGAN). The primary goal is to enhance the transformation of image styles while maintaining visual consistency, an area where current CycleGAN models often fall short. These traditional models typically face difficulties in accurately capturing expansive features as well as the intricate stylistic details necessary for high-quality image transformation. To address these limitations, the research introduces several key modifications to the CycleGAN architecture. Enhancements to the generator involve… More >

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271

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Jiling Wan, Lifeng Cao^*, Jinlong Bai, Jinhui Li, Xuehui Du
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4775-4794, 2025, DOI:10.32604/cmc.2025.061964 - 19 May 2025
Abstract Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among More >

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Zeyang Zhou^1,*, Zachary James Ryan¹, Utkarsh Sharma², Tran Tien Anh³, Shashi Mehrotra⁴, Angelo Greco⁵, Jason West⁶, Mukesh Prasad^1,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4795-4813, 2025, DOI:10.32604/cmc.2025.060291 - 19 May 2025
Abstract Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced… More >

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561

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Tao Zhou^1,2, Yaxing Wang^1,2,*, Huiling Lu³, Wenwen Chai^1,2, Yunfeng Pan^1,2, Zhe Zhang^1,2
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4815-4834, 2025, DOI:10.32604/cmc.2025.062400 - 19 May 2025
Abstract The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched. However, due to the complex structure, low contrast, and complex background of teeth in panoramic X-ray images, the task of instance segmentation is technically tricky. In this study, the contrast between impacted Teeth and periodontal tissues such as gingiva, periodontal membrane, and alveolar bone is low, resulting in fuzzy boundaries of impacted teeth. A model based on Teeth YOLACT is proposed to provide a more efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films.… More >

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186

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Ziyang Wang, Yuanzhen Feng^*, Zhengxin Wang, Cong Zheng
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4835-4853, 2025, DOI:10.32604/cmc.2025.063179 - 19 May 2025
Abstract Continuous control protocols are extensively utilized in traditional MASs, in which information needs to be transmitted among agents consecutively, therefore resulting in excessive consumption of limited resources. To decrease the control cost, based on ISC, several LFC problems are investigated for second-order MASs without and with time delay, respectively. Firstly, an intermittent sampled controller is designed, and a sufficient and necessary condition is derived, under which state errors between the leader and all the followers approach zero asymptotically. Considering that time delay is inevitable, a new protocol is proposed to deal with the time-delay situation.… More >

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155

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Ahmet Emre Ergün, Aytuğ Onan^*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4855-4877, 2025, DOI:10.32604/cmc.2025.063826 - 19 May 2025
Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >

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1160

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626

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Shaoming Qiu, Jingjie He, Yan Wang^*, Bicong E
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4879-4898, 2025, DOI:10.32604/cmc.2025.061532 - 19 May 2025
Abstract Software defect prediction (SDP) aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products. Software defect prediction can be effectively performed using traditional features, but there are some redundant or irrelevant features in them (the presence or absence of this feature has little effect on the prediction results). These problems can be solved using feature selection. However, existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset. In… More >

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