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  • Open Access

    ARTICLE

    Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

    Feng Zhao, Guangdi Liu*, Xiaoqiang Chen, Ying Wang

    Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

    Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

  • Open Access

    ARTICLE

    Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm

    Haoan Gu1, Kai Zhu1, Alfred Strauss2, Yehui Shi3,4, Dragoslav Sumarac5, Maosen Cao1,*

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 363-380, 2024, DOI:10.32604/sdhm.2024.042388

    Abstract Concrete materials and structures are extensively used in transformation infrastructure and they usually bear cracks during their long-term operation. Detecting cracks using deep-learning algorithms like YOLOv3 (You Only Look Once version 3) is a new trend to pursue intelligent detection of concrete surface cracks. YOLOv3 is a typical deep-learning algorithm used for object detection. Owing to its generality, YOLOv3 lacks specific efficiency and accuracy in identifying concrete surface cracks. An improved algorithm based on YOLOv3, specialized in the rapid and accurate identification of concrete surface cracks is worthy of investigation. This study proposes a tailored… More >

  • 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 New Malicious Code Classification Method for the Security of Financial Software

    Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849

    Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

  • Open Access

    ARTICLE

    Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

    Fadl Dahan*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

    Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… 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

    Spatial and Contextual Path Network for Image Inpainting

    Dengyong Zhang1,2, Yuting Zhao1,2, Feng Li1,2, Arun Kumar Sangaiah3,4,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 115-133, 2024, DOI:10.32604/iasc.2024.040847

    Abstract Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area. Image feature extraction is the core of image restoration. Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting. However, in the process of feature extraction, it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time. In order to obtain more spatial information and a larger receptive field at the same time, we put forward… 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 >

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