Open Access

COMMENTARY

Yaping Qi^1,*, Weijie Yang^2,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1555-1559, 2025, DOI:10.32604/cmc.2025.064061 - 16 April 2025
Abstract AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of Solid-State Electrolyte (DDSE) demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development. These databases facilitate data standardization, high-throughput screening, and cross-disciplinary collaboration, addressing key challenges in materials informatics. As AI techniques advance, materials databases are expected to play an increasingly vital role in accelerating research and innovation. More >

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REVIEW

Mozhgan Gholami¹, Ali Ghaffari^1,2,3,*, Nahideh Derakhshanfard¹, Nadir iBRAHIMOĞLU⁴, Ali Asghar Pourhaji Kazem²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1561-1605, 2025, DOI:10.32604/cmc.2025.063304 - 16 April 2025
Abstract The Internet has been enhanced recently by blockchain and Internet of Things (IoT) networks. The Internet of Things is a network of various sensor-equipped devices. It gradually integrates the Internet, sensors, and cloud computing. Blockchain is based on encryption algorithms, which are shared database technologies on the Internet. Blockchain technology has grown significantly because of its features, such as flexibility, support for integration, anonymity, decentralization, and independent control. Computational nodes in the blockchain network are used to verify online transactions. However, this integration creates scalability, interoperability, and security challenges. Over the last decade, several advancements… More >

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ARTICLE

Federica Uccello^1,2, Marek Pawlicki^3,4, Salvatore D'Antonio¹, Rafał Kozik^3,4, Michał Choraś^3,4,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1607-1621, 2025, DOI:10.32604/cmc.2025.062801 - 16 April 2025
（This article belongs to the Special Issue: Artificial Intelligence Current Perspectives and Alternative Paths: From eXplainable AI to Generative AI and Data Visualization Technologies)
Abstract The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning–based intrusion detection systems can capture complex network behaviours, their “black-box” nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules More >

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859

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ARTICLE

Ilesanmi Afolabi Daniyan^1,*, Rumbidzai Muvunzi², Festus Fameso³, Julius Ndambuki³, Williams Kupolati³, Jacques Snyman³
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1623-1648, 2025, DOI:10.32604/cmc.2025.062092 - 16 April 2025
（This article belongs to the Special Issue: Design, Optimisation and Applications of Additive Manufacturing Technologies)
Abstract This study evaluates the Fuzzy Analytical Hierarchy Process (FAHP) as a multi-criteria decision (MCD) support tool for selecting appropriate additive manufacturing (AM) techniques that align with cleaner production and environmental sustainability. The FAHP model was validated using an example of the production of aircraft components (specifically fuselage) employing AM technologies such as Wire Arc Additive Manufacturing (WAAM), laser powder bed fusion (L-PBF), Binder Jetting (BJ), Selective Laser Sintering (SLS), and Laser Metal Deposition (LMD). The selection criteria prioritized eco-friendly manufacturing considerations, including the quality and properties of the final product (e.g., surface finish, high strength,… More >

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ARTICLE

Seyha Ros^1,#, Seungwoo Kang^1,#, Taikuong Iv¹, Inseok Song¹, Prohim Tam², Seokhoon Kim^1,3,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1649-1665, 2025, DOI:10.32604/cmc.2025.062716 - 16 April 2025
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Recently, Network Functions Virtualization (NFV) has become a critical resource for optimizing capability utilization in the 5G/B5G era. NFV decomposes the network resource paradigm, demonstrating the efficient utilization of Network Functions (NFs) to enable configurable service priorities and resource demands. Telecommunications Service Providers (TSPs) face challenges in network utilization, as the vast amounts of data generated by the Internet of Things (IoT) overwhelm existing infrastructures. IoT applications, which generate massive volumes of diverse data and require real-time communication, contribute to bottlenecks and congestion. In this context, Multi-access Edge Computing (MEC) is employed to support resource… More >

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ARTICLE

Xuejing Li^*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1667-1681, 2025, DOI:10.32604/cmc.2025.062161 - 16 April 2025
Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >

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ARTICLE

Ricardo Rodriguez-Torrealba, Eva Garcia-Lopez^*, Antonio Garcia-Cabot
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1683-1705, 2025, DOI:10.32604/cmc.2025.062004 - 16 April 2025
Abstract Generation of good-quality distractors is a key and time-consuming task associated with multiple-choice questions (MCQs), one of the assessment items that have dominated the educational field for years. Recent advances in language models and architectures present an opportunity for helping teachers to generate and update these elements to the required speed and scale of widespread increase in online education. This study focuses on a text-to-text approach for joints generation of distractors for MCQs, where the context, question and correct answer are used as input, while the set of distractors corresponds to the output, allowing the… More >

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684

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ARTICLE

Yiming Tang^*, Yanqiu Chen
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1707-1726, 2025, DOI:10.32604/cmc.2025.062412 - 16 April 2025
Abstract Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision. Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples. Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes. These prototypes are matched with the features of the query set to get segmentation results. However, class prototypes are usually obtained by applying global average pooling on masked support images. Global pooling discards much structural information, which may reduce the accuracy of model predictions. To… More >

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640

Open Access

ARTICLE

Ali Batouche¹, Souham Meshoul^2,*, Hadil Shaiba³, Mohamed Batouche^2,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1727-1752, 2025, DOI:10.32604/cmc.2025.063227 - 16 April 2025
Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >

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726

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ARTICLE

Dawei Qiu¹, Peng Wu^1,*, Xiaoming Zhang^2,*, Renjie Xu³
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1753-1769, 2025, DOI:10.32604/cmc.2025.062599 - 16 April 2025
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >

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593

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ARTICLE

Lianqiang Wu, Deming Lei^*, Yutong Cai
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1771-1789, 2025, DOI:10.32604/cmc.2025.063944 - 16 April 2025
（This article belongs to the Special Issue: Algorithms for Planning and Scheduling Problems)
Abstract Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines (BPM). In this study, the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered, and an adaptive cooperated shuffled frog-leaping algorithm (ACSFLA) is proposed to minimize makespan and total tardiness simultaneously. ACSFLA determines the search times for each memeplex based on its quality, with more searches in high-quality memeplexes. An adaptive cooperated and diversified search mechanism is applied, dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality. During the… More >

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589

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ARTICLE

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Amir Mosavi^3,4,5
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1791-1820, 2025, DOI:10.32604/cmc.2025.063962 - 16 April 2025
Abstract Underwater Wireless Sensor Networks (UWSNs) are gaining popularity because of their potential uses in oceanography, seismic activity monitoring, environmental preservation, and underwater mapping. Yet, these networks are faced with challenges such as self-interference, long propagation delays, limited bandwidth, and changing network topologies. These challenges are coped with by designing advanced routing protocols. In this work, we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks (UWF-RPL), an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network nodes. Our method extends RPL with the aid of fuzzy logic More >

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ARTICLE

Borja Bordel Sánchez^1,*, Fernando Rodríguez-Sela¹, Ramón Alcarria², Tomás Robles¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1821-1853, 2025, DOI:10.32604/cmc.2025.059012 - 16 April 2025
Abstract The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers. Specific lightweight cryptosystems are needed to protect those links, as the hardware node tends to be resource-constrained. Then Pseudo Random Number Generators are employed to produce random keys, whose final behavior depends on the initial seed. To guarantee good mathematical behavior, most key generators need an unpredictable voltage signal as input. However, physical signals evolve slowly and have a significant autocorrelation, so they do not have enough entropy to support high-randomness seeds. Then, electronic… More >

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484

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ARTICLE

Qian Zhu¹, Qian Kang¹, Tao Xu², Dengxiu Yu^3,*, Zhen Wang¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1855-1879, 2025, DOI:10.32604/cmc.2025.061913 - 16 April 2025
Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >

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415

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ARTICLE

Muhammad Saad, Ki-Woong Park^*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1881-1900, 2025, DOI:10.32604/cmc.2025.061733 - 16 April 2025
Abstract The proliferation of Internet of Things (IoT) devices introduces substantial security challenges. Currently, privacy constitutes a significant concern for individuals. While maintaining privacy within these systems is an essential characteristic, it often necessitates certain compromises, such as complexity and scalability, thereby complicating management efforts. The principal challenge lies in ensuring confidentiality while simultaneously preserving individuals’ anonymity within the system. To address this, we present our proposed architecture for managing IoT devices using blockchain technology. Our proposed architecture works on and off blockchain and is integrated with dashcams and closed-circuit television (CCTV) security cameras. In this… More >

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ARTICLE

Ting-Yu Lin¹, Kuo-Chen Hung^2,*, Josef Jablonsky³, Kuo-Ping Lin¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1901-1927, 2025, DOI:10.32604/cmc.2025.063543 - 16 April 2025
Abstract VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) has been developed and applied for over twenty-five years, gaining recognition as a prominent multi-criteria decision-making (MCDM) method. Over this period, numerous studies have explored its applications, conducted comparative analyses, integrated it with other methods, and proposed various modifications to enhance its performance. This paper aims to delve into the fundamental principles and objectives of VIKOR, which aim to maximize group utility and minimize individual regret simultaneously. However, this study identifies a significant limitation in the VIKOR methodology: its process amplifies the weight of individual regret, and the calculated… More >

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ARTICLE

Honglin Wang¹, Yaolong Zhang^2,*, Cheng Zhu³
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025
Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

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ARTICLE

Xiaotong Han^1,2, Yunqi Jiang^2,3, Haitao Wang^1,2, Yuan Tian^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1951-1971, 2025, DOI:10.32604/cmc.2025.060134 - 16 April 2025
Abstract Knowledge graphs (KGs), which organize real-world knowledge in triples, often suffer from issues of incompleteness. To address this, multi-hop knowledge graph reasoning (KGR) methods have been proposed for interpretable knowledge graph completion. The primary approaches to KGR can be broadly classified into two categories: reinforcement learning (RL)-based methods and sequence-to-sequence (seq2seq)-based methods. While each method has its own distinct advantages, they also come with inherent limitations. To leverage the strengths of each method while addressing their weaknesses, we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the… More >

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ARTICLE

Khulud Salem Alshudukhi¹, Mamoona Humayun^2,*, Ghadah Naif Alwakid¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1973-1986, 2025, DOI:10.32604/cmc.2025.063077 - 16 April 2025
Abstract The Internet of Things (IoT) and edge computing have substantially contributed to the development and growth of smart cities. It handled time-constrained services and mobile devices to capture the observing environment for surveillance applications. These systems are composed of wireless cameras, digital devices, and tiny sensors to facilitate the operations of crucial healthcare services. Recently, many interactive applications have been proposed, including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT services. Nonetheless, most solutions lack optimizing relaying methods and impose excessive overheads for maintaining devices’ connectivity. Alternatively, data More >

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ARTICLE

Hongxiao Wang¹, Hongshen Liu¹, Dingsen Zhang^1,*, Ziye Zhang¹, Yonghui Yue¹, Jie Chen²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1987-2001, 2025, DOI:10.32604/cmc.2025.060384 - 16 April 2025
Abstract Despite its immense potential, the application of digital twin technology in real industrial scenarios still faces numerous challenges. This study focuses on industrial assembly lines in sectors such as microelectronics, pharmaceuticals, and food packaging, where precision and speed are paramount, applying digital twin technology to the robotic assembly process. The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments. Based on this system, a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed, More >

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ARTICLE

Buwei Dang, Huanming Chen^*, Heng Zhang, Jixian Wang, Jian Zhou
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2003-2023, 2025, DOI:10.32604/cmc.2025.062653 - 16 April 2025
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract This paper introduces a lane-changing strategy aimed at trajectory planning and tracking control for intelligent vehicles navigating complex driving environments. A fifth-degree polynomial is employed to generate a set of potential lane-changing trajectories in the Frenet coordinate system. These trajectories are evaluated using non-cooperative game theory, considering the interaction between the target vehicle and its surroundings. Models considering safety payoffs, speed payoffs, comfort payoffs, and aggressiveness are formulated to obtain a Nash equilibrium solution. This way, collision avoidance is ensured, and an optimal lane change trajectory is planned. Three game scenarios are discussed, and the More >

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ARTICLE

Xiaoying Qiu^1,*, Xiaoyu Ma¹, Guangxu Zhao¹, Jinwei Yu², Wenbao Jiang¹, Zhaozhong Guo¹, Maozhi Xu³
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869 - 16 April 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >

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ARTICLE

Sony Peng¹, Sophort Siet¹, Ilkhomjon Sadriddinov¹, Dae-Young Kim^2,*, Kyuwon Park^3,*, Doo-Soon Park²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2041-2057, 2025, DOI:10.32604/cmc.2025.061166 - 16 April 2025
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

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ARTICLE

Lingqiu Zeng¹, Peibing Sa¹, Qingwen Han², Lei Ye^2,*, Letian Yang¹, Cheng Zhang¹, Jiqiang Cheng²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2059-2086, 2025, DOI:10.32604/cmc.2025.060392 - 16 April 2025
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract Congestion control is an inherent challenge of V2X (Vehicle to Everything) technologies. Due to the use of a broadcasting mechanism, channel congestion becomes severe with the increase in vehicle density. The researchers suggested reducing the frequency of packet dissemination to relieve congestion, which caused a rise in road driving risk. Obviously, high-risk vehicles should be able to send messages timely to alarm surrounding vehicles. Therefore, packet dissemination frequency should be set according to the corresponding vehicle’s risk level, which is hard to evaluate. In this paper, a two-stage fuzzy inference model is constructed to evaluate More >

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560

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ARTICLE

Jianxin Feng^1,2,*, Xiaoyao Liu^1,2
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2087-2107, 2025, DOI:10.32604/cmc.2025.060252 - 16 April 2025
Abstract As a form of discrete representation learning, Vector Quantized Variational Autoencoders (VQ-VAE) have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity. However, existing VQ-VAEs often perform quantization in the spatial domain, ignoring global structural information and potentially suffering from codebook collapse and information coupling issues. This paper proposes a frequency quantized variational autoencoder (FQ-VAE) to address these issues. The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform (2D-FFT) and performs adaptive quantization on these frequency components… More >

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632

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ARTICLE

Jasem Almotiri^*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025
（This article belongs to the Special Issue: Cutting-Edge Machine Learning and AI Innovations in Medical Imaging Diagnosis)
Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >

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ARTICLE

Xuewen Mu^*, Bingcong Zhao
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025
Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

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539

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ARTICLE

De Rosal Ignatius Moses Setiadi^1,*, T. Sutojo¹, Supriadi Rustad¹, Muhamad Akrom¹, Sudipta Kr Ghosal², Minh T. Nguyen³, Arnold Adimabua Ojugo⁴
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2161-2188, 2025, DOI:10.32604/cmc.2025.063729 - 16 April 2025
Abstract Data security has become a growing priority due to the increasing frequency of cyber-attacks, necessitating the development of more advanced encryption algorithms. This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps (SQQLSR), a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range. The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X, Y, and Z axes to enhance randomness. Extensive numerical experiments validate the effectiveness of SQQLSR. The proposed method achieves a maximum Lyapunov exponent (LE) of ≈55.265, surpassing traditional chaotic maps in unpredictability. The bifurcation analysis… More >

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ARTICLE

Min Yao^1,*, Guofeng Liu², Yaozu Zhang³, Guangjie Hu¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2189-2205, 2025, DOI:10.32604/cmc.2025.060609 - 16 April 2025
Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >

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ARTICLE

Dawei Xu^1,2, Fan Huang¹, Jiaxin Zhang¹, Yunfang Liang¹, Baokun Zheng^3,*, Jian Zhao¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2207-2220, 2025, DOI:10.32604/cmc.2025.061455 - 16 April 2025
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract With the rapid proliferation of Internet of Things (IoT) devices, ensuring their communication security has become increasingly important. Blockchain and smart contract technologies, with their decentralized nature, provide strong security guarantees for IoT. However, at the same time, smart contracts themselves face numerous security challenges, among which reentrancy vulnerabilities are particularly prominent. Existing detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer functions. To address these limitations, this paper proposes a novel detection method that combines pattern matching… More >

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Omar Alqahtani, Mohamed Ghouse^*, Asfia Sabahath, Omer Bin Hussain, Arshiya Begum
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2221-2244, 2025, DOI:10.32604/cmc.2025.061977 - 16 April 2025
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer (ViT) architectures and a dynamic multi-loss function. The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features, while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance. Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets, yielding notable improvements. Specifically, on the ISIC-2018 dataset, our method achieves an F1-Score improvement of +4.84% compared to the standard ViT, with a precision increase of +5.46% More >

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709

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ARTICLE

Lieping Zhang^1,2, Hao Ma¹, Jiancheng Huang³, Cui Zhang^4,*, Xiaolin Gao²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2245-2265, 2025, DOI:10.32604/cmc.2025.061519 - 16 April 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.… More >

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Xiangbin Wang¹, Qingjun Yuan^1,*, Weina Niu², Qianwei Meng¹, Yongjuan Wang¹, Chunxiang Gu¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2267-2285, 2025, DOI:10.32604/cmc.2025.061767 - 16 April 2025
Abstract With the rise of encrypted traffic, traditional network analysis methods have become less effective, leading to a shift towards deep learning-based approaches. Among these, multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic, improving classification accuracy. However, existing research predominantly relies on late fusion techniques, which hinder the full utilization of deep features within the data. To address this limitation, we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction. Specifically, our approach performs real-time fusion of modalities More >

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836

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Ahmed A. Ewees^1,2,*, Mohammed A. A. Al-Qaness³, Ali Alshahrani¹, Mohamed Abd Elaziz⁴
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2287-2303, 2025, DOI:10.32604/cmc.2025.061320 - 16 April 2025
Abstract Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output. This enhances the efficiency and reliability of renewable energy systems. Forecasting approaches inform energy management strategies, reduce reliance on fossil fuels, and support the broader transition to sustainable energy solutions. The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis. This research advances an optimized Multilayer Perceptron (MLP) model using recently proposed metaheuristic optimization algorithms, namely the Fire Hawk Optimizer (FHO)… More >

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567

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Erlin Tian¹, Zengchao Zhu^2,*, Fangmei Liu², Zuhe Li²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2305-2322, 2025, DOI:10.32604/cmc.2025.061145 - 16 April 2025
Abstract Within the realm of multimodal neural machine translation (MNMT), addressing the challenge of seamlessly integrating textual data with corresponding image data to enhance translation accuracy has become a pressing issue. We saw that discrepancies between textual content and associated images can lead to visual noise, potentially diverting the model’s focus away from the textual data and so affecting the translation’s comprehensive effectiveness. To solve this visual noise problem, we propose an innovative KDNR-MNMT model. The model combines the knowledge distillation technique with an anti-noise interaction mechanism, which makes full use of the synthesized graphic knowledge… More >

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1118

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605

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Yung-Hsiang Hung, Chuen-Kai Fan, Wen-Pai Wang^*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2323-2349, 2025, DOI:10.32604/cmc.2025.063270 - 16 April 2025
Abstract Bees play a crucial role in the global food chain, pollinating over 75% of food and producing valuable products such as bee pollen, propolis, and royal jelly. However, the Asian hornet poses a serious threat to bee populations by preying on them and disrupting agricultural ecosystems. To address this issue, this study developed a modified YOLOv7tiny (You Only Look Once) model for efficient hornet detection. The model incorporated space-to-depth (SPD) and squeeze-and-excitation (SE) attention mechanisms and involved detailed annotation of the hornet’s head and full body, significantly enhancing the detection of small objects. The Taguchi… More >

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1167

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528

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Shenjian Xiao¹, Xiaoli Qin¹, Yanzhao Tian^1,*, Zhongkai Dang²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2351-2373, 2025, DOI:10.32604/cmc.2025.062324 - 16 April 2025
Abstract The advent of the digital age has consistently provided impetus for facilitating global trade, as evidenced by the numerous customs clearance documents and participants involved in the international trade process, including enterprises, agents, and government departments. However, the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes. In addressing this need, this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption (MA-ABE) in combination with blockchain technology. This scheme supports proxy decryption, attribute revocation, and policy update,… More >

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643

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Liang Zhou¹, Qiyao Hu^1,2,3,*, Xianlin Peng^4,5, Qianlong Liu⁶
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2375-2401, 2025, DOI:10.32604/cmc.2025.061894 - 16 April 2025
（This article belongs to the Special Issue: Collaborative Edge Intelligence and Its Emerging Applications)
Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >

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671

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Martina Rinaldi^1,2, Stefano Valvano^1,*, Francesco Tornabene², Rossana Dimitri²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2403-2428, 2025, DOI:10.32604/cmc.2025.060672 - 16 April 2025
（This article belongs to the Special Issue: Advanced Modeling of Smart and Composite Materials and Structures)
Abstract This study conducts a thorough examination of honeycomb sandwich panels with a lattice core, adopting advanced computational techniques for their modeling. The research extends its analysis to investigate the natural frequency behavior of sandwich panels, encompassing the comprehensive assessment of the entire panel structure. At its core, the research applies the Representative Volume Element (RVE) theory to establish the equivalent material properties, thereby enhancing the predictive capabilities of lattice structure simulations. The methodology applies these properties in the core of infinite panels, which are modeled using double periodic boundary conditions to explore their natural frequencies.… More >

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623

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Qinghai Ou¹, Min Yang¹, Jingcai Kong¹, Yang Yang^2,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2429-2447, 2025, DOI:10.32604/cmc.2025.062067 - 16 April 2025
Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >

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778

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514

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Vijaya Krishna Akula^1,*, Tan Kuan Tak², Pravin Ramdas Kshirsagar³, Shrikant Vijayrao Sonekar⁴, Gopichand Ginnela⁵
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2449-2479, 2025, DOI:10.32604/cmc.2025.061486 - 16 April 2025
Abstract The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are More >

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1002

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511

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Panru Liang¹, Guojiang Xin^1,*, Xiaolei Yi², Hao Liang³, Changsong Ding¹
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2481-2504, 2025, DOI:10.32604/cmc.2025.060961 - 16 April 2025
Abstract Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases, facilitating treatment evaluations, and designing surgical plans. Due to the pancreas’s tiny size, significant variability in shape and location, and low contrast with surrounding tissues, achieving high segmentation accuracy remains challenging. To improve segmentation precision, we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas from CT images. Firstly, an EfficientNetV2 encoder is employed to extract complex and multi-level features, enhancing the model’s ability to capture the pancreas’s intricate morphology. Then, a residual multi-branch dilated attention… More >

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1162

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489

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Mengjiao Wei^1,*, Wenyu Liu²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2505-2523, 2025, DOI:10.32604/cmc.2025.060887 - 16 April 2025
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the… More >

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533

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Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025
Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

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652

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Yue Liu¹, Qinglang Guo², Chunyao Yang¹, Yong Liao^1,*
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2555-2572, 2025, DOI:10.32604/cmc.2025.060318 - 16 April 2025
Abstract Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data More >

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495

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Feng Zhu^*, Kailin Wu, Jie Ding
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2573-2598, 2025, DOI:10.32604/cmc.2025.062007 - 16 April 2025
Abstract With the widespread application of Internet of Things (IoT) technology, the processing of massive real-time streaming data poses significant challenges to the computational and data-processing capabilities of systems. Although distributed streaming data processing frameworks such as Apache Flink and Apache Spark Streaming provide solutions, meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem. To address this, the study proposes a formal modeling approach based on Performance Evaluation Process Algebra (PEPA), which abstracts the core components and interactions of cloud-based distributed streaming data processing systems. Additionally, a generic service… More >

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561

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Shabana Ziyad Puthu Vedu^1,*, Wafaa A. Ghonaim², Naglaa M. Mostafa³, Pradeep Kumar Singh⁴
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2599-2617, 2025, DOI:10.32604/cmc.2025.062695 - 16 April 2025
Abstract Hearing and Speech impairment can be congenital or acquired. Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges. However, the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study. Assistive learning devices enable students to access institutional resources and facilities fully. The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates. This tool converts the audio signals into sign language videos for the… More >

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364

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Qingqing Song^1,*, Shaoliang Xia¹, Zhen Wu²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2619-2642, 2025, DOI:10.32604/cmc.2025.062526 - 16 April 2025
Abstract Load time series analysis is critical for resource management and optimization decisions, especially automated analysis techniques. Existing research has insufficiently interpreted the overall characteristics of samples, leading to significant differences in load level detection conclusions for samples with different characteristics (trend, seasonality, cyclicality). Achieving automated, feature-adaptive, and quantifiable analysis methods remains a challenge. This paper proposes a Threshold Recognition-based Load Level Detection Algorithm (TRLLD), which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics. By utilizing distribution density uniformity, the algorithm classifies data points and ultimately… More >

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1004

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533

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Junzhe Zhang¹, Lei Tang^1,*, Xin Zhou²
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2643-2658, 2025, DOI:10.32604/cmc.2025.061690 - 16 April 2025
Abstract Fisheye cameras offer a significantly larger field of view compared to conventional cameras, making them valuable tools in the field of computer vision. However, their unique optical characteristics often lead to image distortions, which pose challenges for object detection tasks. To address this issue, we propose Yolo-CaSKA (Yolo with Contrastive Learning and Selective Kernel Attention), a novel training method that enhances object detection on fisheye camera images. The standard image and the corresponding distorted fisheye image pairs are used as positive samples, and the rest of the image pairs are used as negative samples, which More >

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Jianxiang Cao¹, Jinyang Wu¹, Wenqian Shang^1,*, Chunhua Wang¹, Kang Song¹, Tong Yi^2,*, Jiajun Cai¹, Haibin Zhu³
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2659-2675, 2025, DOI:10.32604/cmc.2025.060025 - 16 April 2025
Abstract With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the More >

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