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  • Open Access

    ARTICLE

    An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

    Md. Abdur Rahim1, Fahmid Al Farid2, Abu Saleh Musa Miah3, Arpa Kar Puza1, Md. Nur Alam4, Md. Najmul Hossain5, Sarina Mansor2, Hezerul Abdul Karim2,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714

    Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

  • Open Access

    ARTICLE

    Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

    Jiaxiang Luo1,2, Weien Zhou2,3, Bingxiao Du1,*, Daokui Li1, Wen Yao2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1919-1947, 2024, DOI:10.32604/cmes.2024.048118

    Abstract In recent years, there has been significant research on the application of deep learning (DL) in topology optimization (TO) to accelerate structural design. However, these methods have primarily focused on solving binary TO problems, and effective solutions for multi-material topology optimization (MMTO) which requires a lot of computing resources are still lacking. Therefore, this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design. The framework employs convolutional neural network (CNN) to construct a surrogate model for solving MMTO, and the obtained surrogate model can rapidly generate multi-material structure topologies… More >

  • Open Access

    ARTICLE

    A Framework for Driver Drowsiness Monitoring Using a Convolutional Neural Network and the Internet of Things

    Muhamad Irsan1,2,*, Rosilah Hassan2, Anwar Hassan Ibrahim3, Mohamad Khatim Hasan2, Meng Chun Lam2, Wan Mohd Hirwani Wan Hussain4

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 157-174, 2024, DOI:10.32604/iasc.2024.042193

    Abstract One of the major causes of road accidents is sleepy drivers. Such accidents typically result in fatalities and financial losses and disadvantage other road users. Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system. Most studies have examined how the mouth and eyelids move. However, this limits the system’s ability to identify drowsiness traits. Therefore, this study designed an Accident Detection Framework (RPK) that could be used to reduce road accidents due to sleepiness and detect the location of accidents. The drowsiness detection model used three facial… More >

  • Open Access

    ARTICLE

    Damage Diagnosis of Bleacher Based on an Enhanced Convolutional Neural Network with Training Interference

    Chaozhi Cai*, Xiaoyu Guo, Yingfang Xue, Jianhua Ren

    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 321-339, 2024, DOI:10.32604/sdhm.2024.045831

    Abstract Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operation poses a significant threat to the safety of both life and property. Consequently, it becomes imperative to conduct damage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types of potential damage, and the presence of similar vibration data in adjacent locations make it challenging to achieve satisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmental noise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and anti-noise capabilities… More > Graphic Abstract

    Damage Diagnosis of Bleacher Based on an Enhanced Convolutional Neural Network with Training Interference

  • Open Access

    ARTICLE

    MoBShield: A Novel XML Approach for Securing Mobile Banking

    Saeed Seraj1, Ali Safaa Sadiq1,*, Omprakash Kaiwartya1, Mohammad Aljaidi2, Alexandros Konios1, Mohammed Ali3, Mohammed Abazeed3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914

    Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based… More >

  • Open Access

    ARTICLE

    FusionNN: A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

    Li Wang1,2,3,*, Mingshan Xia1,2,*, Hao Hu1, Jianfang Li1,2, Fengyao Hou1,2, Gang Chen1,2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2991-3006, 2024, DOI:10.32604/cmc.2024.048637

    Abstract With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identification models were built relying on security experts’ empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as… More >

  • Open Access

    ARTICLE

    Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

    Dmitry Gura1,2, Bo Dong3,*, Duaa Mehiar4, Nidal Al Said5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238

    Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

  • Open Access

    ARTICLE

    Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

    Chuyuan Wei*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3299-3314, 2024, DOI:10.32604/cmc.2024.047811

    Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply… More >

  • Open Access

    ARTICLE

    Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition

    J. Sheril Angel1, A. Diana Andrushia1,*, T. Mary Neebha1, Oussama Accouche2, Louai Saker2, N. Anand3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2427-2448, 2024, DOI:10.32604/cmc.2024.047326

    Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial… More >

  • Open Access

    ARTICLE

    Combo Packet: An Encryption Traffic Classification Method Based on Contextual Information

    Yuancong Chai, Yuefei Zhu*, Wei Lin, Ding Li

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

    Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.… More >

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