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Kunkun Tong^1,#, Guchu Zou^2,#, Xin Tan^1,*, Jingyu Gong¹, Zhenyi Qi², Zhizhong Zhang¹, Yuan Xie¹, Lizhuang Ma¹
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3421-3461, 2024, DOI:10.32604/cmc.2024.055327
Abstract Confusing object detection (COD), such as glass, mirrors, and camouflaged objects, represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds, leveraging deep learning methodologies. Despite garnering increasing attention in computer vision, the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures. As of now, there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks. To fill this gap, our study presents a pioneering review that covers… More >

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Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886
Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

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Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222
（This article belongs to the Special Issue: Deep Learning and IoT for Smart Healthcare)
Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

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Farhan Amin, Gyu Sang Choi^*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3507-3521, 2024, DOI:10.32604/cmc.2024.053183
Abstract Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community. The call detail records (CDR) of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing… More >

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Weiqiang Jin^1,#, Xingwu Tian^1,#, Bohang Shi¹, Biao Zhao^1,*, Haibin Duan², Hao Wu³
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125
Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

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Shih-Lin Lin^*, Bo-Chen Lin
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456
Abstract This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories,… More >

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Qianyao Zhu¹, Kaizhou Gao^1,*, Wuze Huang¹, Zhenfang Ma¹, Adam Slowik²
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3573-3589, 2024, DOI:10.32604/cmc.2024.055244
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract The flow shop scheduling problem is important for the manufacturing industry. Effective flow shop scheduling can bring great benefits to the industry. However, there are few types of research on Distributed Hybrid Flow Shop Problems (DHFSP) by learning assisted meta-heuristics. This work addresses a DHFSP with minimizing the maximum completion time (Makespan). First, a mathematical model is developed for the concerned DHFSP. Second, four Q-learning-assisted meta-heuristics, e.g., genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), and differential evolution (DE), are proposed. According to the nature of DHFSP, six local search operations… More >

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33

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REVIEW

Ali Asghar^1,#, Amna Shifa^2,#, Mamoona Naveed Asghar^2,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3591-3635, 2024, DOI:10.32604/cmc.2024.054654
Abstract Videos represent the most prevailing form of digital media for communication, information dissemination, and monitoring. However, their widespread use has increased the risks of unauthorised access and manipulation, posing significant challenges. In response, various protection approaches have been developed to secure, authenticate, and ensure the integrity of digital videos. This study provides a comprehensive survey of the challenges associated with maintaining the confidentiality, integrity, and availability of video content, and examining how it can be manipulated. It then investigates current developments in the field of video security by exploring two critical research questions. First, it… More >

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Dong-Wook Kim¹, Gun-Yoon Sin², Kwangsoo Kim³, Jaesik Kang³, Sun-Young Im³, Myung-Mook Han^1,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3637-3653, 2024, DOI:10.32604/cmc.2024.054108
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract In the rapidly evolving field of cybersecurity, the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical. Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats. To address this gap, we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems. Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field. The cornerstone of our approach is the use of a conditional tabular generative adversarial network (CTGAN),… More >

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Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Mohamed Aashiq^3,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

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Sagar Kavaiya¹, Hiren Mewada^2,*, Sagarkumar Patel³, Dharmendra Chauhan³, Faris A. Almalki⁴, Hana Mohammed Mujlid⁴
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3685-3711, 2024, DOI:10.32604/cmc.2024.053587
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The mobility and connective capabilities of unmanned aerial vehicles (UAVs) are becoming more and more important in defense, commercial, and research domains. However, their open communication makes UAVs susceptible to undesirable passive attacks such as eavesdropping or jamming. Recently, the inefficiency of traditional cryptography-based techniques has led to the addition of Physical Layer Security (PLS). This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments, proposing a solution to complement the conventional cryptography approach. Initially, we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems, namely hybrid… More >

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64

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Zhongmin Huangfu, Shuqing Li^*, Luoheng Yan
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3713-3731, 2024, DOI:10.32604/cmc.2024.054188
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract Addressing the challenges in detecting surface floating litter in artificial lakes, including complex environments, uneven illumination, and susceptibility to noise and weather, this paper proposes an efficient and lightweight Ghost-YOLO (You Only Look Once) v8 algorithm. The algorithm integrates advanced attention mechanisms and a small-target detection head to significantly enhance detection performance and efficiency. Firstly, an SE (Squeeze-and-Excitation) mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization. This mechanism models feature channel dependencies, enabling adaptive adjustment of channel importance, thereby improving recognition of floating litter targets.… More >

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Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634
Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

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Irum Ilays¹, Yaser Hafeez^1,*, Nabil Almashfi², Sadia Ali¹, Mamoona Humayun^3,*, Muhammad Aqib¹, Ghadah Alwakid⁴
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3761-3784, 2024, DOI:10.32604/cmc.2024.053830
（This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
Abstract Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to… More >

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63

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Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599
（This article belongs to the Special Issue: Artificial Intelligence for Addressing Security and Communications Challenges of Internet-connected Critical Infrastructures)
Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

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Ankitkumar Patel¹, Srivathsan Vasudevan^1,*, Satya Bulusu^2,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3803-3818, 2024, DOI:10.32604/cmc.2024.052851
（This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract Molecular Dynamics (MD) simulation for computing Interatomic Potential (IAP) is a very important High-Performance Computing (HPC) application. MD simulation on particles of experimental relevance takes huge computation time, despite using an expensive high-end server. Heterogeneous computing, a combination of the Field Programmable Gate Array (FPGA) and a computer, is proposed as a solution to compute MD simulation efficiently. In such heterogeneous computation, communication between FPGA and Computer is necessary. One such MD simulation, explained in the paper, is the (Artificial Neural Network) ANN-based IAP computation of gold (Au₁₄₇ & Au₃₀₉) nanoparticles. MD simulation calculates the forces… More >

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Fan Li^1,2, Shuyao Zhang³, Jie Yang^1,2,*, Zhicheng Feng^1,2, Zhichao Chen^1,2
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525
（This article belongs to the Special Issue: Artificial Intelligence Driven Innovations in Integrating Communications, Image and Signal Processing Applications)
Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

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Zhaobin Li^*, Xiantao Liu^*, Nan Zhang, Zhanzhen Wei
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3835-3854, 2024, DOI:10.32604/cmc.2024.053950
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The Industrial Internet of Things (IIoT) consists of massive devices in different management domains, and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange. To address the above challenges, a viable solution that combines Certificateless Public Key Cryptography (CL-PKC) with blockchain technology can be utilized. However, as many existing schemes rely on a single Key Generation Center (KGC), they are prone to problems such as single points of failure and high computational overhead. In this case, this paper proposes a novel blockchain-based certificateless cross-domain authentication… More >

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Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

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Fangfang Shan^1,2,*, Zhenyu Wang^1,2, Mengyao Liu^1,2, Menghan Zhang^1,2
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3881-3902, 2024, DOI:10.32604/cmc.2024.055167
Abstract In response to the challenges of generating Attribute-Based Access Control (ABAC) policies, this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents. This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model. The manual retrieval and analysis involved in this transition are inefficient, prone to errors, and costly. Most organizations have high-level specifications defined for security policies that include a set of access control policies, which often exist in the form of natural language documents. Utilizing… More >

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Kwang-Cheol Rim¹, Young-Min Yoon², Sung-Uk Kim³, Jeong-In Kim^4,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3903-3916, 2024, DOI:10.32604/cmc.2024.054620
Abstract The identification and mitigation of anomaly data, characterized by deviations from normal patterns or singularities, stand as critical endeavors in modern technological landscapes, spanning domains such as Non-Fungible Tokens (NFTs), cyber-security, and the burgeoning metaverse. This paper presents a novel proposal aimed at refining anomaly detection methodologies, with a particular focus on continuous data streams. The essence of the proposed approach lies in analyzing the rate of change within such data streams, leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy. Through empirical evaluation, our method demonstrates a marked improvement over existing More >

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REVIEW

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Zaharaddeen Karami Lawal^3,4,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150
（This article belongs to the Special Issue: Fortifying the Foundations: Novel Approaches to Cyber-Physical Systems Intrusion Detection and Industrial 4.0 Security)
Abstract The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining… More >

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R. Mahaveerakannan¹, Thanarajan Tamilvizhi^2,*, Sonia Jenifer Rayen³, Osamah Ibrahim Khalaf⁴, Habib Hamam^5,6,7,8
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3945-3966, 2024, DOI:10.32604/cmc.2024.051611
Abstract The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content. In light of the data-centric aspect of contemporary communication, the information-centric network (ICN) paradigm offers hope for a solution by emphasizing content retrieval by name instead of location. If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things (IoT) devices, then effective caching solutions will be required to maximize network throughput and minimize the use of resources. Hence, an ICN-based Cooperative Caching (ICN-CoC) technique has been used to select… More >

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Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

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Zhanyang Xu^*, Zhe Wang, Jian Xu, Hongyan Shi, Hong Zhao
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3991-4015, 2024, DOI:10.32604/cmc.2024.051620
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract System logs, serving as a pivotal data source for performance monitoring and anomaly detection, play an indispensable role in assuring service stability and reliability. Despite this, the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs, utilizing solely a single Recurrent Neural Network (RNN) and its variant sequence models for detection. These approaches have not thoroughly exploited the semantic information embedded in logs, exhibit limited adaptability to novel logs, and a single model struggles to fully unearth the potential features within the log sequence. Addressing these challenges,… More >

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Shunshun Ji, Jiajun Sun, Chao Zhang^*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038
Abstract Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus… More >

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Wenquan Sun^1,2, Jia Liu^1,2,*, Lifeng Chen^1,2, Weina Dong^1,2, Fuqiang Di^1,2
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of… More >

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Turki Ali Alghamdi, Saud S. Alotaibi^*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4047-4064, 2024, DOI:10.32604/cmc.2024.052796
Abstract Internet of Things (IoTs) provides better solutions in various fields, namely healthcare, smart transportation, home, etc. Recognizing Denial of Service (DoS) outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems. Deep learning (DL) models outperform in detecting complex, non-linear relationships, allowing them to effectually severe slight deviations from normal IoT activities that may designate a DoS outbreak. The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection, permitting proactive reduction events to be executed, hence securing the IoT network’s safety and functionality. Subsequently, this… More >

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Wenquan Sun^1,2, Jia Liu^1,2,*, Weina Dong^1,2, Lifeng Chen^1,2, Fuqiang Di^1,2
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4065-4083, 2024, DOI:10.32604/cmc.2024.053115
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract As neural radiance fields continue to advance in 3D content representation, the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing. In response to this challenge, this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking. Leveraging 2D image watermarking technology for 3D scene protection, the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from… More >

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Syed Kamran Haider^1,2, Abbas Ahmed², Noman Mujeeb Khan², Ali Nauman^3,*, Sung Won Kim^3,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4085-4110, 2024, DOI:10.32604/cmc.2024.052997
（This article belongs to the Special Issue: AI-Assisted Energy Harvesting Techniques and its Applications in Wireless Sensor Networks)
Abstract In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm More >

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Zixuan Wu¹, Ye Wang^1,*, Lifeng Shen², Feng Hu¹, Hong Yu^1,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581
（This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >

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Run Yang^1,2,3, Yanping Chen^1,2,3,*, Jiaxin Yan^1,2,3, Yongbin Qin^1,2,3
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4129-4147, 2024, DOI:10.32604/cmc.2024.051675
Abstract The relation is a semantic expression relevant to two named entities in a sentence. Since a sentence usually contains several named entities, it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities. In related work, graph convolutional neural networks are widely adopted to learn semantic dependencies, where a dependency tree initializes the adjacency matrix. However, this approach has two main issues. First, parsing a sentence heavily relies on external toolkits, which can be error-prone. Second, the dependency tree only encodes the syntactical structure of a sentence,… More >

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Xu Sun, Yinhui Yu^*, Qing Cheng
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images.… More >

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Nguyen Tho Thong¹, Nguyen Van Quyet^1,2, Cu Nguyen Giap^3,*, Nguyen Long Giang¹, Luong Thi Hong Lan⁴
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4173-4196, 2024, DOI:10.32604/cmc.2024.054031
Abstract Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy… More >

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Sheng Xu¹, Shixiong Xiang², Feiyu Meng¹, Qiang Wu^1,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157
Abstract In Unsupervised Domain Adaptation (UDA) for person re-identification (re-ID), the primary challenge is reducing the distribution discrepancy between the source and target domains. This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain. Implicit construction is difficult due to the absence of intermediate state supervision, making smooth knowledge transfer from the source to the target domain a challenge. To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,… More >

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Changyu Liu¹, Hao Huang¹, Guogang Huang^2,*, Chunyin Wu¹, Yingqi Liang³
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625
Abstract Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal… More >

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Congcong Ma^1,2, Jiaqi Mi¹, Wanlin Gao^1,2, Sha Tao^1,2,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506
Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >

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Mohamed Rabik Mohamed Ismail¹, Muthuramalingam Thangaraj^1,*, Khaja Moiduddin^2,*, Zeyad Almutairi^2,3, Mustufa Haider Abidi²
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4263-4279, 2024, DOI:10.32604/cmc.2024.055271
Abstract The measurement and mapping of objects in the outer environment have traditionally been conducted using ground-based monitoring systems, as well as satellites. More recently, unmanned aerial vehicles have also been employed for this purpose. The accurate detection and mapping of a target such as buildings, trees, and terrains are of utmost importance in various applications of unmanned aerial vehicles (UAVs), including search and rescue operations, object transportation, object detection, inspection tasks, and mapping activities. However, the rapid measurement and mapping of the object are not currently achievable due to factors such as the object’s size,… More >

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Neng Wan^1,2, Guangping Zeng^1,*, Xianwei Zhou¹
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4281-4302, 2024, DOI:10.32604/cmc.2024.054794
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract With the development of vehicles towards intelligence and connectivity, vehicular data is diversifying and growing dramatically. A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle (ICV) applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points (NCPs). Considering the amount of task data and the idle resources of NCPs, a computing resource scheduling model for NCPs is established. Taking the heterogeneous task execution delay threshold as a constraint, the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs. The… More >

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Xiangkui Jiang^*, Zihao Wang, Chao Dong
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640
Abstract

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next

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Zhaojie Sun¹, Linlin Xing^1,*, Longbo Zhang¹, Hongzhen Cai², Maozu Guo³
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588
Abstract Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we… More >

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Cuiwei Peng¹, Jiahui Chen^2,*, Shicheng Wan³, Guotao Xu⁴
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4341-4360, 2024, DOI:10.32604/cmc.2024.054109
Abstract In today’s highly competitive retail industry, offline stores face increasing pressure on profitability. They hope to improve their ability in shelf management with the help of big data technology. For this, on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior. RFM (recency, frequency, and monetary) pattern mining is a powerful tool to evaluate the value of customer behavior. However, the existing RFM pattern mining algorithms do not consider the quarterly nature of goods, resulting in unreasonable shelf availability and difficulty in profit-making. To solve this problem, we… More >

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Abdijalil Abdullahi^1,2, Selvakumar Manickam^2,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4361-4385, 2024, DOI:10.32604/cmc.2024.054215
Abstract Internet Exchange Point (IXP) is a system that increases network bandwidth performance. Internet exchange points facilitate interconnection among network providers, including Internet Service Providers (ISPs) and Content Delivery Providers (CDNs). To improve service management, Internet exchange point providers have adopted the Software Defined Network (SDN) paradigm. This implementation is known as a Software-Defined Exchange Point (SDX). It improves network providers’ operations and management. However, performance issues still exist, particularly with multi-hop topologies. These issues include switch memory costs, packet processing latency, and link failure recovery delays. The paper proposes Enhanced Link Failure Rerouting (ELFR), an… More >

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Zhiyi Ding, Lei Sun^*, Xiuqing Mao, Leyu Dai, Ruiyang Ding
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4387-4412, 2024, DOI:10.32604/cmc.2024.052196
Abstract Object detection finds wide application in various sectors, including autonomous driving, industry, and healthcare. Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples. This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems. Most existing adversarial attack strategies focus primarily on image classification problems, failing to fully exploit the unique characteristics of object detection models, thus resulting in widespread deficiencies in their transferability. Furthermore, previous research has predominantly concentrated on… More >

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Xiaoyu Liu, Yong Hu^*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938
Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

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Yaoyao Wang¹, Mansheng Xiao^1,*, Yuqing Hu², Jin Yan¹, Zeyu Zhu¹
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4433-4449, 2024, DOI:10.32604/cmc.2024.053232
Abstract Due to the limitations of a priori knowledge and convolution operation, the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration, in order to more accurately restore the original appearance of the cultural relics mural images, an image restoration based on the denoising diffusion probability model (Denoising Diffusion Probability Model (DDPM)) and the Transformer method. The process involves two steps: in the first step, the damaged mural image is firstly utilized as the condition to generate the noise image, using the time, condition and noise image patch as the inputs… More >

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Chao Wang^1,3, Kaijie Zhang^1,2,*, Xiaoyong Yu¹, Dejun Li², Wei Xie², Xinqiao Wang²
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4451-4473, 2024, DOI:10.32604/cmc.2024.051764
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract In the model of the vehicle recognition algorithm implemented by the convolutional neural network, the model needs to compute and store a lot of parameters. Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance. Therefore, obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research. In this paper, a lightweight densely connected, and deeply separable convolutional network (DCDSNet) algorithm is proposed to achieve this goal. Visual Geometry Group (VGG) More >

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Xiwang Guo¹, Liangbo Zhou¹, Zhiwei Zhang^1,*, Liang Qi^2,*, Jiacun Wang³, Shujin Qin⁴, Jinrui Cao⁵
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4475-4496, 2024, DOI:10.32604/cmc.2024.048123
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers. A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time. Based on a product’s AND/OR graph, matrices for task-skill, worker-skill, precedence relationships, and disassembly correlations are developed. A multi-objective discrete chemical reaction optimization algorithm is designed. To enhance solution diversity, improvements are made to four reactions: decomposition, synthesis, intermolecular ineffective collision, and wall invalid collision reaction, completing the evolution of molecular individuals. The established model and improved algorithm are applied to ball More >

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Tianqing Yuan^1,2, Na Li^1,2, Hao Sun³, Sen Tan^4,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4497-4512, 2024, DOI:10.32604/cmc.2024.056061
Abstract To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with… More >

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Muhammad Dawood¹, Shanshan Tu¹, Chuangbai Xiao¹, Muhammad Haris², Hisham Alasmary³, Muhammad Waqas^4,5,*, Sadaqat Ur Rehman⁶
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4513-4542, 2024, DOI:10.32604/cmc.2024.050049
Abstract The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow More >

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