Open Access
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
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.
SCI: 2023 Impact Factor 2.1; 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.
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)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
TECHNICAL REPORT
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3413-3419, 2024, DOI:10.32604/cmc.2024.060288 - 19 December 2024
Abstract The Dynamic Database of Solid-State Electrolyte (DDSE) is an advanced online platform offering a comprehensive suite of tools for solid-state battery research and development. Its key features include statistical analysis of both experimental and computational solid-state electrolyte (SSE) data, interactive visualization through dynamic charts, user data assessment, and literature analysis powered by a large language model. By facilitating the design and optimization of novel SSEs, DDSE serves as a critical resource for advancing solid-state battery technology. This Technical Report provides detailed tutorials and practical examples to guide users in effectively utilizing the platform. More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3421-3450, 2024, DOI:10.32604/cmc.2024.059359 - 19 December 2024
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data. The blockchain is a decentralized digital ledger that ensures data-sharing security, transparency, and traceability through cryptographic technology and consensus algorithms. Consequently, medical blockchain data-sharing methods have garnered significant attention and research efforts. Nevertheless, current methods have different storage and transmission measures for original data in the medical blockchain, resulting in large differences in performance and privacy. Therefore, we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location. More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3451-3498, 2024, DOI:10.32604/cmc.2024.055575 - 19 December 2024
Abstract The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0, blockchain, and immersive technologies. This paper presents a thorough analysis of the metaverse, showcasing its evolution from a conceptual phase rooted in science fiction to a dynamic and transformative digital environment impacting various sectors including gaming, education, healthcare, and entertainment. The paper introduces the metaverse, details its historical development, and introduces key technologies that enable its existence such as virtual and augmented reality, blockchain, and artificial intelligence. Further this work explores diverse application scenarios, future trends, and critical More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3499-3559, 2024, DOI:10.32604/cmc.2024.057877 - 19 December 2024
Abstract Enhancing the interconnection of devices and systems, the Internet of Things (IoT) is a paradigm-shifting technology. IoT security concerns are still a substantial concern despite its extraordinary advantages. This paper offers an extensive review of IoT security, emphasizing the technology’s architecture, important security elements, and common attacks. It highlights how important artificial intelligence (AI) is to bolstering IoT security, especially when it comes to addressing risks at different IoT architecture layers. We systematically examined current mitigation strategies and their effectiveness, highlighting contemporary challenges with practical solutions and case studies from a range of industries, such More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3561-3582, 2024, DOI:10.32604/cmc.2024.058637 - 19 December 2024
Abstract Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety. Crowd counting has attracted considerable attention in the field of computer vision, leading to the development of numerous advanced models and methodologies. These approaches vary in terms of supervision techniques, network architectures, and model complexity. Currently, most crowd counting methods rely on fully supervised learning, which has proven to be effective. However, this approach presents challenges in real-world scenarios, where labeled data and ground-truth… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3583-3616, 2024, DOI:10.32604/cmc.2024.055671 - 19 December 2024
Abstract As an advanced data science technology, the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine (TCM). This not only contributes to a deeper comprehension of traditional Chinese medical theories but also provides robust support for the intelligent decision systems and medical applications of TCM. Against this backdrop, this paper aims to systematically review the current status and development trends of TCM knowledge graphs, offering theoretical and technical foundations to facilitate the inheritance, innovation, and integrated development of TCM. Firstly, we introduce the relevant concepts and research status… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3617-3663, 2024, DOI:10.32604/cmc.2024.058461 - 19 December 2024
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract The rapid population growth, insecure lifestyle, wastage of natural resources, indiscipline behavior of human beings, urgency in the medical field, security of patient information, agricultural-related problems, and automation requirements in industries are the reasons for invention of technologies. Smart cities aim to address these challenges through the integration of technology, data, and innovative practices. Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living, improve resource efficiency, and create sustainable environments. This review presents five of the most critical technologies for smart and/or safe cities, addressing pertinent topics such as More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3665-3678, 2024, DOI:10.32604/cmc.2024.059895 - 19 December 2024
Abstract This study explores the mechanical behavior of single-crystal copper with silver inclusions, focusing on the effects of dendritic and spherical geometries using molecular dynamics simulations. Uniaxial tensile tests reveal that dendritic inclusions lead to an earlier onset of plasticity due to the presence of high-strain regions at the complex inclusion/matrix interfaces, whereas spherical inclusions exhibit delayed plasticity associated with their symmetric geometry and homogeneous strain distribution. During the plastic regime, the dislocation density is primarily influenced by the volume fraction of silver inclusions rather than their shape, with spherical inclusions showing the highest densities due… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3679-3705, 2024, DOI:10.32604/cmc.2024.059378 - 19 December 2024
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats, with Internet of Things (IoT) security gaining particular attention due to its role in data communication across various industries. However, IoT devices, typically low-powered, are susceptible to cyber threats. Conversely, blockchain has emerged as a robust solution to secure these devices due to its decentralised nature. Nevertheless, the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks, network scalability limitations, and blockchain-specific security vulnerabilities. Blockchain, on the other hand, is a recently emerged information… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3707-3729, 2024, DOI:10.32604/cmc.2024.057684 - 19 December 2024
（This article belongs to the Special Issue: Computer Vision for Smart Cities)
Abstract Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3731-3760, 2024, DOI:10.32604/cmc.2024.058488 - 19 December 2024
Abstract Breast cancer remains a significant global health concern, with early detection being crucial for effective treatment and improved survival rates. This study introduces HERA-Net (Hybrid Extraction and Recognition Architecture), an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities. The HERA-Net model integrates powerful deep learning architectures, including VGG19, U-Net, GRU (Gated Recurrent Units), and ResNet-50, to capture multi-dimensional features that support robust image segmentation, feature extraction, and temporal analysis. For thermographic imaging, a comprehensive dataset of 3534 infrared (IR) images from the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3761-3780, 2024, DOI:10.32604/cmc.2024.057103 - 19 December 2024
Abstract For data mining tasks on large-scale data, feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance. Traditional wrapper feature selection methodologies typically require extensive model training and evaluation, which cannot deliver desired outcomes within a reasonable computing time. In this paper, an innovative wrapper approach termed Contribution Tracking Feature Selection (CTFS) is proposed for feature selection of large-scale data, which can locate informative features without population-level evolution. In other words, fewer evaluations are needed for CTFS compared to other evolutionary methods. We initially More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3781-3804, 2024, DOI:10.32604/cmc.2024.057131 - 19 December 2024
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract User location trajectory refers to the sequence of geographic location information that records the user’s movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user participates in the sensing task, the process of sensing data collection faces the problem of privacy leakage. To address the privacy leakage issue of trajectory data during uploading, publishing, and sharing when users use location services on mobile smart group sensing terminal devices, this paper proposes a privacy protection method based on generative adversarial networks and attention mechanisms (BiLS-A-GAN).… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3805-3824, 2024, DOI:10.32604/cmc.2024.056941 - 19 December 2024
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3825-3851, 2024, DOI:10.32604/cmc.2024.055497 - 19 December 2024
Abstract As the popularity and dependence on the Internet increase, DDoS (distributed denial of service) attacks seriously threaten network security. By accurately distinguishing between different types of DDoS attacks, targeted defense strategies can be formulated, significantly improving network protection efficiency. DDoS attacks usually manifest as an abnormal increase in network traffic, and their diverse types of attacks, along with a severe data imbalance, make it difficult for traditional classification methods to effectively identify a small number of attack types. To solve this problem, this paper proposes a DDoS recognition method CVWGG (Conditional Variational Autoencoder-Wasserstein Generative Adversarial… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3853-3880, 2024, DOI:10.32604/cmc.2024.057538 - 19 December 2024
Abstract Imbalance is a distinctive feature of many datasets, and how to make the dataset balanced become a hot topic in the machine learning field. The Synthetic Minority Oversampling Technique (SMOTE) is the classical method to solve this problem. Although much research has been conducted on SMOTE, there is still the problem of synthetic sample singularity. To solve the issues of class imbalance and diversity of generated samples, this paper proposes a hybrid resampling method for binary imbalanced data sets, RE-SMOTE, which is designed based on the improvements of two oversampling methods parameter-free SMOTE (PF-SMOTE) and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3881-3901, 2024, DOI:10.32604/cmc.2024.056773 - 19 December 2024
Abstract In the age of smart technology, the widespread use of small LCD (Liquid Crystal Display) necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products. Manual inspection is both time-consuming and labor-intensive. Existing methods struggle with accurately detecting small targets, such as point defects, and handling defects with significant scale variations, such as line defects, especially in complex background conditions. To address these challenges, this paper presents the YOLO-DEI (Deep Enhancement Information) model, which integrates DCNv2 (Deformable convolution) into the backbone network to enhance feature extraction under geometric transformations. The model More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024
Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3927-3950, 2024, DOI:10.32604/cmc.2024.056961 - 19 December 2024
Abstract This paper investigates an unmanned aerial vehicle (UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything (V2X) systems. Due to the presence of an eavesdropper (Eve), the system’s communication links may be insecure. This paper proposes deploying an intelligent reflecting surface (IRS) on the UAV to enhance the communication performance of mobile vehicles, improve system flexibility, and alleviate eavesdropping on communication links. The links for uploading task data from vehicles to a base station (BS) are protected by IRS-assisted physical layer security (PLS). Upon receiving task data, the computing resources provided by the edge computing servers (MEC)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3951-3968, 2024, DOI:10.32604/cmc.2024.053245 - 19 December 2024
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract The process of generating descriptive captions for images has witnessed significant advancements in last years, owing to the progress in deep learning techniques. Despite significant advancements, the task of thoroughly grasping image content and producing coherent, contextually relevant captions continues to pose a substantial challenge. In this paper, we introduce a novel multimodal method for image captioning by integrating three powerful deep learning architectures: YOLOv8 (You Only Look Once) for robust object detection, EfficientNetB7 for efficient feature extraction, and Transformers for effective sequence modeling. Our proposed model combines the strengths of YOLOv8 in detecting objects,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3969-3987, 2024, DOI:10.32604/cmc.2024.056093 - 19 December 2024
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3989-4014, 2024, DOI:10.32604/cmc.2024.057473 - 19 December 2024
Abstract In complex agricultural environments, cucumber disease identification is confronted with challenges like symptom diversity, environmental interference, and poor detection accuracy. This paper presents the DM-YOLO model, which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases. Traditional detection models have a tough time identifying small-scale and overlapping symptoms, especially when critical features are obscured by lighting variations, occlusion, and background noise. The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way. First, the MultiCat module employs a multi-scale feature processing strategy with… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4015-4032, 2024, DOI:10.32604/cmc.2024.058480 - 19 December 2024
Abstract The controller is a main component in the Software-Defined Networking (SDN) framework, which plays a significant role in enabling programmability and orchestration for 5G and next-generation networks. In SDN, frequent communication occurs between network switches and the controller, which manages and directs traffic flows. If the controller is not strategically placed within the network, this communication can experience increased delays, negatively affecting network performance. Specifically, an improperly placed controller can lead to higher end-to-end (E2E) delay, as switches must traverse more hops or encounter greater propagation delays when communicating with the controller. This paper introduces… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4049-4080, 2024, DOI:10.32604/cmc.2024.057211 - 19 December 2024
Abstract Spear Phishing Attacks (SPAs) pose a significant threat to the healthcare sector, resulting in data breaches, financial losses, and compromised patient confidentiality. Traditional defenses, such as firewalls and antivirus software, often fail to counter these sophisticated attacks, which target human vulnerabilities. To strengthen defenses, healthcare organizations are increasingly adopting Machine Learning (ML) techniques. ML-based SPA defenses use advanced algorithms to analyze various features, including email content, sender behavior, and attachments, to detect potential threats. This capability enables proactive security measures that address risks in real-time. The interpretability of ML models fosters trust and allows security… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4081-4107, 2024, DOI:10.32604/cmc.2024.057071 - 19 December 2024
Abstract Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products, leading to suboptimal user experiences. To address this, our study presents a Personalized Adaptive Multi-Product Recommendation System (PAMR) leveraging transfer learning and Bi-GRU (Bidirectional Gated Recurrent Units). Using a large dataset of user reviews from Amazon and Flipkart, we employ transfer learning with pre-trained models (AlexNet, GoogleNet, ResNet-50) to extract high-level attributes from product data, ensuring effective feature representation even with limited data. Bi-GRU captures both spatial and sequential dependencies in user-item interactions. The innovation of this study lies… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4109-4124, 2024, DOI:10.32604/cmc.2024.056476 - 19 December 2024
Abstract Phishing attacks seriously threaten information privacy and security within the Internet of Things (IoT) ecosystem. Numerous phishing attack detection solutions have been developed for IoT; however, many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application. This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection. Our model employs a two-fold optimization approach: first, it utilizes the analysis of the variance (ANOVA) F-test to select the optimal features for phishing detection, and second, it applies the Cuckoo Search algorithm to tune the hyperparameters (learning rate… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4125-4147, 2024, DOI:10.32604/cmc.2024.056666 - 19 December 2024
Abstract Depression is a prevalent mental health issue affecting individuals of all age groups globally. Similar to other mental health disorders, diagnosing depression presents significant challenges for medical practitioners and clinical experts, primarily due to societal stigma and a lack of awareness and acceptance. Although medical interventions such as therapies, medications, and brain stimulation therapy provide hope for treatment, there is still a gap in the efficient detection of depression. Traditional methods, like in-person therapies, are both time-consuming and labor-intensive, emphasizing the necessity for technological assistance, especially through Artificial Intelligence. Alternative to this, in most cases… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024
（This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)
Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4171-4194, 2024, DOI:10.32604/cmc.2024.056971 - 19 December 2024
Abstract Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively, predict potential criminal activities, and ensure public safety. Traditional methods of crime analysis often rely on manual, time-consuming processes that may overlook intricate patterns and correlations within the data. While some existing machine learning models have improved the efficiency and accuracy of crime prediction, they often face limitations such as overfitting, imbalanced datasets, and inadequate handling of spatiotemporal dynamics. This research proposes an advanced machine learning framework, CHART (Crime Hotspot Analysis and Real-time Tracking), designed to overcome these challenges. The proposed methodology… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4195-4216, 2024, DOI:10.32604/cmc.2024.057453 - 19 December 2024
Abstract Visual Question Answering (VQA) is an interdisciplinary artificial intelligence (AI) activity that integrates computer vision and natural language processing. Its purpose is to empower machines to respond to questions by utilizing visual information. A VQA system typically takes an image and a natural language query as input and produces a textual answer as output. One major obstacle in VQA is identifying a successful method to extract and merge textual and visual data. We examine “Fusion” Models that use information from both the text encoder and picture encoder to efficiently perform the visual question-answering challenge. For More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4217-4263, 2024, DOI:10.32604/cmc.2024.052548 - 19 December 2024
Abstract Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade. The diagnosis of skin cancer is of paramount importance, yet the process is intricate and challenging. The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature, the visibility of dense hair, and the presence of various substitute characteristics. The texture of the epidermis is notably different from that of normal skin, and these differences are often evident in cases of unhealthy skin. As a consequence, the development of an effective… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4265-4283, 2024, DOI:10.32604/cmc.2024.059271 - 19 December 2024
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract The rapid growth and pervasive presence of the Internet of Things (IoT) have led to an unparalleled increase in IoT devices, thereby intensifying worries over IoT security. Deep learning (DL)-based intrusion detection (ID) has emerged as a vital method for protecting IoT environments. To rectify the deficiencies of current detection methodologies, we proposed and developed an IoT cyberattacks detection system (IoT-CDS) based on DL models for detecting bot attacks in IoT networks. The DL models—long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional neural network-LSTM (CNN-LSTM) were suggested to detect and classify IoT attacks.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4285-4305, 2024, DOI:10.32604/cmc.2024.057029 - 19 December 2024
Abstract Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4307-4325, 2024, DOI:10.32604/cmc.2024.058238 - 19 December 2024
Abstract Multi-modal 3D object detection has achieved remarkable progress, but it is often limited in practical industrial production because of its high cost and low efficiency. The multi-view camera-based method provides a feasible solution due to its low cost. However, camera data lacks geometric depth, and only using camera data to obtain high accuracy is challenging. This paper proposes a multi-modal Bird-Eye-View (BEV) distillation framework (MMDistill) to make a trade-off between them. MMDistill is a carefully crafted two-stage distillation framework based on teacher and student models for learning cross-modal knowledge and generating multi-modal features. It can… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4327-4347, 2024, DOI:10.32604/cmc.2024.055628 - 19 December 2024
Abstract Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness. However, accurately classifying diverse and complex weather conditions remains a significant challenge. While advanced techniques such as Vision Transformers have been developed, they face key limitations, including high computational costs and limited generalization across varying weather conditions. These challenges present a critical research gap, particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’ intricate and dynamic nature in real-time. To address this gap, we propose a Multi-level Knowledge Distillation (MLKD) framework, which leverages… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4371-4396, 2024, DOI:10.32604/cmc.2024.058168 - 19 December 2024
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity, in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices. In this paper, we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models. The proposed method combines two main techniques: (1) constructing a malware behavior profile and (2) extracting features from the malware behavior profile using graph neural networks. Specifically, to effectively construct an Android malware behavior profile,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4397-4417, 2024, DOI:10.32604/cmc.2024.056367 - 19 December 2024
Abstract In the modern world, the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems. This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness, which combines heart rate monitoring and face recognition technologies. The research objectives include developing a non-contact method for detecting driver drowsiness, training and assessing the proposed system using pre-trained machine learning models, and implementing a real-time alert feature to trigger warnings when drowsiness is detected. Deep learning models based on convolutional neural networks (CNNs),… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4419-4433, 2024, DOI:10.32604/cmc.2024.055478 - 19 December 2024
（This article belongs to the Special Issue: Recognition Tasks with Transformers)
Abstract While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental… More >
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4435-4463, 2024, DOI:10.32604/cmc.2024.058478 - 19 December 2024
Abstract In this paper, we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext encryption. Specifically, we leverage the B92 Quantum Key Distribution (QKD) protocol to secure the distribution of encryption keys, which are further processed through Galois Field (GF(28)) operations for increased security. The encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map (H3LM), a chaotic system that generates complex and unpredictable sequences, thereby ensuring strong confusion and diffusion in the encryption process. This hybrid approach More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4465-4483, 2024, DOI:10.32604/cmc.2024.059616 - 19 December 2024
（This article belongs to the Special Issue: Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control)
Abstract In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4485-4502, 2024, DOI:10.32604/cmc.2024.059116 - 19 December 2024
Abstract With the advancements in artificial intelligence (AI) technology, attackers are increasingly using sophisticated techniques, including ChatGPT. Endpoint Detection & Response (EDR) is a system that detects and responds to strange activities or security threats occurring on computers or endpoint devices within an organization. Unlike traditional antivirus software, EDR is more about responding to a threat after it has already occurred than blocking it. This study aims to overcome challenges in security control, such as increased log size, emerging security threats, and technical demands faced by control staff. Previous studies have focused on AI detection models,… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4503-4523, 2024, DOI:10.32604/cmc.2024.057974 - 19 December 2024
Abstract The applications of machine learning (ML) in the medical domain are often hindered by the limited availability of high-quality data. To address this challenge, we explore the synthetic generation of echocardiography images (echoCG) using state-of-the-art generative models. We conduct a comprehensive evaluation of three prominent methods: Cycle-consistent generative adversarial network (CycleGAN), Contrastive Unpaired Translation (CUT), and Stable Diffusion 1.5 with Low-Rank Adaptation (LoRA). Our research presents the data generation methodology, image samples, and evaluation strategy, followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of More >
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4525-4549, 2024, DOI:10.32604/cmc.2024.056291 - 19 December 2024
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4551-4573, 2024, DOI:10.32604/cmc.2024.057714 - 19 December 2024
Abstract Microblogging platforms like X (formerly Twitter) and Sina Weibo have become key channels for spreading information online. Accurately predicting information spread, such as users’ reposting activities, is essential for applications including content recommendation and analyzing public sentiment. Current advanced models rely on deep representation learning to extract features from various inputs, such as users’ social connections and repost history, to forecast reposting behavior. Nonetheless, these models frequently ignore intrinsic confounding factors, which may cause the models to capture spurious relationships, ultimately impacting prediction performance. To address this limitation, we propose a novel Debiased Reposting Prediction… More >
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4575-4601, 2024, DOI:10.32604/cmc.2024.055307 - 19 December 2024
Abstract In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4603-4624, 2024, DOI:10.32604/cmc.2024.057979 - 19 December 2024
Abstract Software cost estimation is a crucial aspect of software project management, significantly impacting productivity and planning. This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset, which comprises data from 93 unique software projects with 24 attributes. By applying multiple machine learning algorithms alongside three feature selection methods, this study aims to reduce data redundancy and enhance model accuracy. Our findings reveal that the principal component analysis (PCA)-based feature selection technique achieved the highest performance, underscoring the importance of optimal feature selection in improving software More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4625-4658, 2024, DOI:10.32604/cmc.2024.058607 - 19 December 2024
Abstract Named Data Networking (NDN) has emerged as a promising communication paradigm, emphasizing content-centric access rather than location-based access. This model offers several advantages for Internet of Healthcare Things (IoHT) environments, including efficient content distribution, built-in security, and natural support for mobility and scalability. However, existing NDN-based IoHT systems face inefficiencies in their forwarding strategy, where identical Interest packets are forwarded across multiple nodes, causing broadcast storms, increased collisions, higher energy consumption, and delays. These issues negatively impact healthcare system performance, particularly for individuals with disabilities and chronic diseases requiring continuous monitoring. To address these challenges,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4659-4690, 2024, DOI:10.32604/cmc.2024.058115 - 19 December 2024
Abstract Infrastructure as a Service (IaaS) in cloud computing enables flexible resource distribution over the Internet, but achieving optimal scheduling remains a challenge. Effective resource allocation in cloud-based environments, particularly within the IaaS model, poses persistent challenges. Existing methods often struggle with slow optimization, imbalanced workload distribution, and inefficient use of available assets. These limitations result in longer processing times, increased operational expenses, and inadequate resource deployment, particularly under fluctuating demands. To overcome these issues, a novel Clustered Input-Oriented Salp Swarm Algorithm (CIOSSA) is introduced. This approach combines two distinct strategies: Task Splitting Agglomerative Clustering (TSAC)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4691-4705, 2024, DOI:10.32604/cmc.2024.057648 - 19 December 2024
Abstract With the ever-increasing continuous adoption of Industrial Internet of Things (IoT) technologies, security concerns have grown exponentially, especially regarding securing critical infrastructures. This is primarily due to the potential for backdoors to provide unauthorized access, disrupt operations, and compromise sensitive data. Backdoors pose a significant threat to the integrity and security of Industrial IoT setups by exploiting vulnerabilities and bypassing standard authentication processes. Hence its detection becomes of paramount importance. This paper not only investigates the capabilities of Machine Learning (ML) models in identifying backdoor malware but also evaluates the impact of balancing the dataset More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4707-4726, 2024, DOI:10.32604/cmc.2024.058675 - 19 December 2024
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract Evaluating artificial intelligence (AI) systems is crucial for their successful deployment and safe operation in real-world applications. The assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test set. The original approach lacks covering continuous ranges, for example, regression problems, and it produces only the probability of success. In this work, to address existing limitations and enhance practical applicability, we propose an assessor feedback mechanism designed to identify and learn from AI system errors, enabling the system to perform the More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4727-4742, 2024, DOI:10.32604/cmc.2024.058512 - 19 December 2024
Abstract Content delivery networks (CDNs) lead to fast content distribution through content caching at specific CDN servers near end users. However, existing CDNs based on infrastructure cannot be employed in special cases, such as military operations. Thus, a temporary CDN without an existing infrastructure is required. To achieve this goal, we introduce a new CDN for drone-aided ad hoc networks, whereby multiple drones form ad hoc networks and quickly store specific content according to new caching algorithms. Unlike the typical CDN server, the content-caching algorithm in the proposed architecture considers the limited storage capacity of the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4743-4756, 2024, DOI:10.32604/cmc.2024.058266 - 19 December 2024
（This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
Abstract Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4757-4786, 2024, DOI:10.32604/cmc.2024.055634 - 19 December 2024
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by researchers and control engineers, with learning-based methods like reinforcement learning, deep learning, and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems. This paper investigates a Reinforcement Learning (RL) approach for both high and low-level quadrotor control systems, focusing on attitude stabilization and position… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4787-4832, 2024, DOI:10.32604/cmc.2024.055079 - 19 December 2024
Abstract This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4833-4851, 2024, DOI:10.32604/cmc.2024.055561 - 19 December 2024
Abstract Wireless Sensor Network (WSNs) consists of a group of nodes that analyze the information from surrounding regions. The sensor nodes are responsible for accumulating and exchanging information. Generally, node localization is the process of identifying the target node’s location. In this research work, a Received Signal Strength Indicator (RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models. Initially, the RSSI value is identified using the Deep Neural Network (DNN). The RSSI is conceded as the range-based method and it does not require special hardware for the node… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4853-4874, 2024, DOI:10.32604/cmc.2024.057138 - 19 December 2024
Abstract Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial services. After the first reported incident in 1995, its impact keeps on increasing. Also, during COVID-19, due to the increase in digitization, there is an exponential increase in the number of victims of phishing attacks. Many deep learning and machine learning techniques are available to detect phishing attacks. However, most of the techniques did not use efficient optimization techniques. In this context, our proposed model used random forest-based techniques to select the best features, and then the Brown-Bear optimization… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4875-4893, 2024, DOI:10.32604/cmc.2024.060090 - 19 December 2024
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract The Coordinate Descent Method for K-means (CDKM) is an improved algorithm of K-means. It identifies better locally optimal solutions than the original K-means algorithm. That is, it achieves solutions that yield smaller objective function values than the K-means algorithm. However, CDKM is sensitive to initialization, which makes the K-means objective function values not small enough. Since selecting suitable initial centers is not always possible, this paper proposes a novel algorithm by modifying the process of CDKM. The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4895-4914, 2024, DOI:10.32604/cmc.2024.057585 - 19 December 2024
Abstract In Intelligent Manufacturing, Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors, ensuring more informed decision-making and adaptive system management. It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation, cost reduction, maximizing the process of production and so on. Various methods are employed to enhance productivity, yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration. This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid… More >
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