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Computers, Materials & Continua
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
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About the Journal
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
Indexing and Abstracting
SCI: 2023 Impact Factor 2.0; Scopus CiteScore (Impact per Publication 2023): 5.3; SNIP (Source Normalized Impact per Paper 2023): 0.73; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
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Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
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Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
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Open AccessREVIEW
Confusing Object Detection: A SurveyCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3421-3461, 2024, DOI:10.32604/cmc.2024.055327 - 12 September 2024
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 > -
Open Access
ARTICLE
Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAPCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024
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 > -
Open Access
ARTICLE
Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter OptimizationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024
（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 > -
Open Access
ARTICLE
Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social SystemCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3507-3521, 2024, DOI:10.32604/cmc.2024.053183 - 12 September 2024
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 > -
Open Access
ARTICLE
Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRLCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125 - 12 September 2024
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 > -
Open Access
ARTICLE
Enhancing Safety in Autonomous Vehicle Navigation: An Optimized Path Planning Approach Leveraging Model Predictive ControlCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3555-3572, 2024, DOI:10.32604/cmc.2024.055456 - 12 September 2024
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 > -
Open Access
ARTICLE
Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop ProblemsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3573-3589, 2024, DOI:10.32604/cmc.2024.055244 - 12 September 2024
（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 > -
Open Access
REVIEW
Survey on Video Security: Examining Threats, Challenges, and Future TrendsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3591-3635, 2024, DOI:10.32604/cmc.2024.054654 - 12 September 2024
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 > -
Open Access
ARTICLE
Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise SystemsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3637-3653, 2024, DOI:10.32604/cmc.2024.054108 - 12 September 2024
（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 > -
Open Access
ARTICLE
Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection SystemCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610 - 12 September 2024
（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 > -
Open Access
ARTICLE
Physical Layer Security of 6G Vehicular Networks with UAV Systems: First Order Secrecy Metrics, Optimization, and BoundsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3685-3711, 2024, DOI:10.32604/cmc.2024.053587 - 12 September 2024
（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 > -
Open Access
ARTICLE
Ghost-YOLO v8: An Attention-Guided Enhanced Small Target Detection Algorithm for Floating Litter on Water SurfacesCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3713-3731, 2024, DOI:10.32604/cmc.2024.054188 - 12 September 2024
（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 > -
Open Access
ARTICLE
Value Function Mechanism in WSNs-Based Mango Plantation Monitoring SystemCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634 - 12 September 2024
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 > -
Open Access
ARTICLE
Towards Improving the Quality of Requirement and Testing Process in Agile Software Development: An Empirical StudyCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3761-3784, 2024, DOI:10.32604/cmc.2024.053830 - 12 September 2024
（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 > -
Open Access
ARTICLE
Explainable AI-Based DDoS Attacks Classification Using Deep Transfer LearningCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599 - 12 September 2024
（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 > -
Open Access
ARTICLE
FPGA Accelerators for Computing Interatomic Potential-Based Molecular Dynamics Simulation for Gold Nanoparticles: Exploring Different Communication ProtocolsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3803-3818, 2024, DOI:10.32604/cmc.2024.052851 - 12 September 2024
（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 > -
Open Access
ARTICLE
Rail-PillarNet: A 3D Detection Network for Railway Foreign Object Based on LiDARCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525 - 12 September 2024
（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 > -
Open Access
ARTICLE
Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of ThingsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3835-3854, 2024, DOI:10.32604/cmc.2024.053950 - 12 September 2024
（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 > -
Open Access
ARTICLE
Metaheuristic-Driven Two-Stage Ensemble Deep Learning for Lung/Colon Cancer ClassificationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024
（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 > -
Open Access
ARTICLE
Automatic Generation of Attribute-Based Access Control Policies from Natural Language DocumentsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3881-3902, 2024, DOI:10.32604/cmc.2024.055167 - 12 September 2024
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 > -
Open Access
ARTICLE
Anomaly Detection Using Data Rate of Change on Medical DataCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3903-3916, 2024, DOI:10.32604/cmc.2024.054620 - 12 September 2024
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 > -
Open Access
REVIEW
A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 EraCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150 - 12 September 2024
（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 > -
Open Access
ARTICLE
Information Centric Networking Based Cooperative Caching Framework for 5G Communication SystemsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3945-3966, 2024, DOI:10.32604/cmc.2024.051611 - 12 September 2024
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 > -
Open Access
ARTICLE
Heart-Net: A Multi-Modal Deep Learning Approach for Diagnosing Cardiovascular DiseasesCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024
（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 > -
Open Access
ARTICLE
Enhancing Log Anomaly Detection with Semantic Embedding and Integrated Neural Network InnovationsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3991-4015, 2024, DOI:10.32604/cmc.2024.051620 - 12 September 2024
（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 > -
Open Access
ARTICLE
Phenotypic Image Recognition of Asparagus Stem Blight Based on Improved YOLOv8CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038 - 12 September 2024
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 > -
Open Access
ARTICLE
MarkINeRV: A Robust Watermarking Scheme for Neural Representation for Videos Based on Invertible Neural NetworksCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745 - 12 September 2024
（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 > -
Open Access
ARTICLE
Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning ApproachCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4047-4064, 2024, DOI:10.32604/cmc.2024.052796 - 12 September 2024
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 > -
Open Access
ARTICLE
RWNeRF: Robust Watermarking Scheme for Neural Radiance Fields Based on Invertible Neural NetworksCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4065-4083, 2024, DOI:10.32604/cmc.2024.053115 - 12 September 2024
（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 > -
Open Access
ARTICLE
AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks PerformanceCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4085-4110, 2024, DOI:10.32604/cmc.2024.052997 - 12 September 2024
（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 > -
Open Access
ARTICLE
Leveraging Uncertainty for Depth-Aware Hierarchical Text ClassificationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581 - 12 September 2024
（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 > -
Open Access
ARTICLE
A Graph with Adaptive Adjacency Matrix for Relation ExtractionCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4129-4147, 2024, DOI:10.32604/cmc.2024.051675 - 12 September 2024
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 > -
Open Access
ARTICLE
Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial ImagesCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725 - 12 September 2024
（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 > -
Open Access
ARTICLE
A Complex Fuzzy LSTM Network for Temporal-Related Forecasting ProblemsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4173-4196, 2024, DOI:10.32604/cmc.2024.054031 - 12 September 2024
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 > -
Open Access
ARTICLE
Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost FrameworkCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157 - 12 September 2024
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 > -
Open Access
ARTICLE
Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory ScenariosCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625 - 12 September 2024
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 > -
Open Access
ARTICLE
Cost-Sensitive Dual-Stream Residual Networks for Imbalanced ClassificationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506 - 12 September 2024
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 > -
Open Access
ARTICLE
Unknown Environment Measurement Mapping by Unmanned Aerial Vehicle Using Kalman Filter-Based Low-Cost Estimated Parallel 8-Beam LIDARCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4263-4279, 2024, DOI:10.32604/cmc.2024.055271 - 12 September 2024
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 > -
Open Access
ARTICLE
Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected VehiclesCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4281-4302, 2024, DOI:10.32604/cmc.2024.054794 - 12 September 2024
（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 > -
Open Access
ARTICLE
A Path Planning Algorithm Based on Improved RRT Sampling RegionCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640 - 12 September 2024
AbstractFor 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
… More > -
Open Access
ARTICLE
Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of LabelingCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588 - 12 September 2024
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 > -
Open Access
ARTICLE
A Quarterly High RFM Mining Algorithm for Big Data ManagementCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4341-4360, 2024, DOI:10.32604/cmc.2024.054109 - 12 September 2024
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 > -
Open Access
ARTICLE
Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point NetworksCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4361-4385, 2024, DOI:10.32604/cmc.2024.054215 - 12 September 2024
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 > -
Open Access
ARTICLE
Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss OptimizationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4387-4412, 2024, DOI:10.32604/cmc.2024.052196 - 12 September 2024
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 > -
Open Access
ARTICLE
Multi-Label Image Classification Based on Object Detection and Dynamic Graph Convolutional NetworksCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938 - 12 September 2024
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 > -
Open Access
ARTICLE
Research on Restoration of Murals Based on Diffusion Model and TransformerCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4433-4449, 2024, DOI:10.32604/cmc.2024.053232 - 12 September 2024
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 > -
Open Access
ARTICLE
Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNetCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4451-4473, 2024, DOI:10.32604/cmc.2024.051764 - 12 September 2024
（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 > -
Open Access
ARTICLE
Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization AlgorithmCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4475-4496, 2024, DOI:10.32604/cmc.2024.048123 - 12 September 2024
（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 > -
Open Access
ARTICLE
A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BPCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4497-4512, 2024, DOI:10.32604/cmc.2024.056061 - 12 September 2024
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 > -
Open Access
REVIEW
The Impact of Domain Name Server (DNS) over Hypertext Transfer Protocol Secure (HTTPS) on Cyber Security: Limitations, Challenges, and Detection TechniquesCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4513-4542, 2024, DOI:10.32604/cmc.2024.050049 - 12 September 2024
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 > -
Open Access
ARTICLE
HWD-YOLO: A New Vision-Based Helmet Wearing Detection MethodCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024
Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More > -
Open Access
ARTICLE
Diabetic Retinopathy Detection: A Hybrid Intelligent ApproachCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106 - 12 September 2024
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More > -
Open Access
ARTICLE
AMAD: Adaptive Mapping Approach for Datacenter Networks, an Energy-Friend Resource Allocation Framework via Repeated Leader Follower GameCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4577-4601, 2024, DOI:10.32604/cmc.2024.054102 - 12 September 2024
Abstract Cloud Datacenter Network (CDN) providers usually have the option to scale their network structures to allow for far more resource capacities, though such scaling options may come with exponential costs that contradict their utility objectives. Yet, besides the cost of the physical assets and network resources, such scaling may also impose more loads on the electricity power grids to feed the added nodes with the required energy to run and cool, which comes with extra costs too. Thus, those CDN providers who utilize their resources better can certainly afford their services at lower price-units when… More > -
Open Access
ARTICLE
IMTNet: Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature PyramidCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.053740 - 12 September 2024
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses… More > -
Open Access
ARTICLE
FIBTNet: Building Change Detection for Remote Sensing Images Using Feature Interactive Bi-Temporal NetworkCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4621-4641, 2024, DOI:10.32604/cmc.2024.053206 - 12 September 2024
Abstract In this paper, a feature interactive bi-temporal change detection network (FIBTNet) is designed to solve the problem of pseudo change in remote sensing image building change detection. The network improves the accuracy of change detection through bi-temporal feature interaction. FIBTNet designs a bi-temporal feature exchange architecture (EXA) and a bi-temporal difference extraction architecture (DFA). EXA improves the feature exchange ability of the model encoding process through multiple space, channel or hybrid feature exchange methods, while DFA uses the change residual (CR) module to improve the ability of the model decoding process to extract different features More > -
Open Access
ARTICLE
An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm OptimizationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4643-4662, 2024, DOI:10.32604/cmc.2024.055195 - 12 September 2024
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use… More > -
Open Access
ARTICLE
Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD EnvironmentCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4663-4686, 2024, DOI:10.32604/cmc.2024.055287 - 12 September 2024
Abstract Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms… More > -
Open Access
ARTICLE
Pyramid Separable Channel Attention Network for Single Image Super-ResolutionCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4687-4701, 2024, DOI:10.32604/cmc.2024.055803 - 12 September 2024
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Single Image Super-Resolution (SISR) technology aims to reconstruct a clear, high-resolution image with more information from an input low-resolution image that is blurry and contains less information. This technology has significant research value and is widely used in fields such as medical imaging, satellite image processing, and security surveillance. Despite significant progress in existing research, challenges remain in reconstructing clear and complex texture details, with issues such as edge blurring and artifacts still present. The visual perception effect still needs further enhancement. Therefore, this study proposes a Pyramid Separable Channel Attention Network (PSCAN) for the… More > -
Open Access
ARTICLE
A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog ComputingCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4703-4728, 2024, DOI:10.32604/cmc.2024.054203 - 12 September 2024
Abstract The Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this… More > -
Open Access
ARTICLE
An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy MaskingCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024
Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More > -
Open Access
ARTICLE
Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast ThermogramsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4749-4765, 2024, DOI:10.32604/cmc.2024.052793 - 12 September 2024
（This article belongs to the Special Issue: Privacy-Aware AI-based Models for Cancer Diagnosis)
Abstract Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide. Various Computer-Aided Diagnosis (CAD) tools, based on breast thermograms, have been developed for early detection of this disease. However, accurately segmenting the Region of Interest (ROI) from thermograms remains challenging. This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottom boundary using a second-degree polynomial. The proposed method demonstrated high efficacy, achieving an impressive Jaccard coefficient of 86% and a Dice… More > -
Open Access
ARTICLE
GATiT: An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge ReasoningCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4767-4790, 2024, DOI:10.32604/cmc.2024.053506 - 12 September 2024
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the… More > -
Open Access
ARTICLE
Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish OptimizationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4791-4812, 2024, DOI:10.32604/cmc.2024.052771 - 12 September 2024
Abstract In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is… More > -
Open Access
ARTICLE
Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation NetworkCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685 - 12 September 2024
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More > -
Open Access
ARTICLE
A Low Complexity ML-Based Methods for Malware ClassificationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849 - 12 September 2024
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More > -
Open Access
ARTICLE
Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5sCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4859-4874, 2024, DOI:10.32604/cmc.2024.055403 - 12 September 2024
Abstract Electrolysis tanks are used to smelt metals based on electrochemical principles, and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures, thus affecting normal production. Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks, an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5 (YOLOv5) is proposed. Firstly, we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) by changing the U-shaped network (U-Net)… More > -
Open Access
ARTICLE
Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement LearningCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4875-4893, 2024, DOI:10.32604/cmc.2024.055622 - 12 September 2024
Abstract Aiming at the rapid growth of network services, which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain (SFC) under 5G networks, this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment (MADDPG-SD). Initially, an optimization model is devised to enhance the request acceptance rate, minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case. Subsequently, we model the dynamic problem as a Markov decision process (MDP), facilitating adaptation to the… More > -
Open Access
ARTICLE
Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer AlgorithmCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4895-4916, 2024, DOI:10.32604/cmc.2024.050815 - 12 September 2024
Abstract Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each More > -
Open Access
ARTICLE
Knowledge-Driven Possibilistic Clustering with Automatic Cluster EliminationCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4917-4945, 2024, DOI:10.32604/cmc.2024.054775 - 12 September 2024
Abstract Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their objective function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers),… More > -
Open Access
ARTICLE
Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy SetsCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4947-4972, 2024, DOI:10.32604/cmc.2024.055260 - 12 September 2024
Abstract This socialized environment among educated and developed people causes them to focus more on their appearance and health, which turns them towards medical-related treatments, leading us to discuss anti-aging treatment methods for each age group, particularly for urban people who are interested in this. Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects. Five anti-aging therapies such as microdermabrasion or dermabrasion, laser resurfacing anti-aging skin treatments, chemical peels, dermal fillers for aged skin, and botox injections are considered in this… More > -
Open Access
ARTICLE
YOLO-RLC: An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024
Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More > -
Open Access
ARTICLE
Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient BoostingCMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4997-5027, 2024, DOI:10.32604/cmc.2024.054459 - 12 September 2024
Abstract Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones, computers, or tablets. It can occur through various channels, such as social media, text messages, online forums, or gaming platforms. Cyberbullying involves using technology to intentionally harm, harass, or intimidate others and may take different forms, including exclusion, doxing, impersonation, harassment, and cyberstalking. Unfortunately, due to the rapid growth of malicious internet users, this social phenomenon is becoming more frequent, and there is a huge need to address this issue. Therefore, the main goal of the research… More > -
Open Access
ARTICLE
Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AICMC-Computers, Materials & Continua, Vol.80, No.3, pp. 5029-5045, 2024, DOI:10.32604/cmc.2024.052531 - 12 September 2024
（This article belongs to the Special Issue: Privacy-Aware AI-based Models for Cancer Diagnosis)
Abstract Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNet-B0 having a Spatial Attention Layer with More >
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