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  • Open Access

    ARTICLE

    Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

    Ahmad Yahiya Ahmad Bani Ahmad1, Jafar Alzubi2, Sophers James3, Vincent Omollo Nyangaresi4,5,*, Chanthirasekaran Kutralakani6, Anguraju Krishnan7

    CMC-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

    A Graph with Adaptive Adjacency Matrix for Relation Extraction

    Run Yang1,2,3, Yanping Chen1,2,3,*, Jiaxin Yan1,2,3, Yongbin Qin1,2,3

    CMC-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

    A Pooling Method Developed for Use in Convolutional Neural Networks

    İsmail Akgül*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 751-770, 2024, DOI:10.32604/cmes.2024.052549 - 20 August 2024

    Abstract In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural… More >

  • Open Access

    ARTICLE

    Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

    Muaadh A. Alsoufi1,*, Maheyzah Md Siraj1, Fuad A. Ghaleb2, Muna Al-Razgan3, Mahfoudh Saeed Al-Asaly3, Taha Alfakih3, Faisal Saeed2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112 - 20 August 2024

    Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

  • Open Access

    ARTICLE

    Enhancing Communication Accessibility: UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

    Khushal Das1, Fazeel Abid2, Jawad Rasheed3,4,*, Kamlish5, Tunc Asuroglu6,*, Shtwai Alsubai7, Safeeullah Soomro8

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 689-711, 2024, DOI:10.32604/cmes.2024.051335 - 20 August 2024

    Abstract Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language… More >

  • Open Access

    ARTICLE

    Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

    Jianfeng Lu1,2, Caijin Li1, Xiangye Huang1, Chen Cui3, Mahmoud Emam1,2,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.053680 - 15 August 2024

    Abstract The widespread availability of digital multimedia data has led to a new challenge in digital forensics. Traditional source camera identification algorithms usually rely on various traces in the capturing process. However, these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms. Convolutional Neural Networks (CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices. However, their performances is not ideal in case of distinguishing between individual devices of the same model, because cameras of the same model typically use the same… More >

  • Open Access

    ARTICLE

    Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

    Kexin Wang*, Yingdong Gou, Dingrui Xue*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893 - 15 August 2024

    Abstract The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >

  • Open Access

    REVIEW

    AI-Driven Learning Management Systems: Modern Developments, Challenges and Future Trends during the Age of ChatGPT

    Sameer Qazi1,*, Muhammad Bilal Kadri2, Muhammad Naveed1,*, Bilal A. Khawaja3, Sohaib Zia Khan4, Muhammad Mansoor Alam5,6,7, Mazliham Mohd Su’ud6

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893 - 15 August 2024

    Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >

  • Open Access

    CORRECTION

    Correction: Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1075-1081, 2024, DOI:10.32604/csse.2024.054179 - 17 July 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

    Asad Raza1,*, Shahzad Memon1, Muhammad Ali Nizamani1, Mahmood Hussain Shah2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779 - 11 July 2024

    Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

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