Abdulgbar A. R. Farea¹, Gehad Abdullah Amran^2,*, Ebraheem Farea³, Amerah Alabrah^4,*, Ahmed A. Abdulraheem⁵, Muhammad Mursil⁶, Mohammed A. A. Al-qaness⁷

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3605-3622, 2023, DOI:10.32604/cmc.2023.040121

Abstract E-commerce, online ticketing, online banking, and other web-based applications that handle sensitive data, such as passwords, payment information, and financial information, are widely used. Various web developers may have varying levels of understanding when it comes to securing an online application. Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List Cross Site Scripting (XSS). An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws. Many published articles focused on these attacks’ binary classification. This… More >

Bahera H. Nayef^1,*, Siti Norul Huda Sheikh Abdullah², Rossilawati Sulaiman², Ashwaq Mukred Saeed³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3549-3567, 2023, DOI:10.32604/cmc.2023.039528

Abstract Text extraction from images using the traditional techniques of image collecting, and pattern recognition using machine learning consume time due to the amount of extracted features from the images. Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results. This study proposes using Dual Maxpooling and concatenating convolution Neural Networks (CNN) layers with the activation functions Relu and the Optimized Leaky Relu (OLRelu). The proposed method works by dividing the word image into slices that contain characters. Then pass them to deep… More >

Yinghua Song^1,2, Hairong Lyu^1,2, Wei Zhang^1,2,*

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249

Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy, by setting… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

Zhongguo Tang¹, Haijin Zhuo¹, Beian Li¹, Xiaotao Ma², Siyu Zhao², Kai Tong^2,*

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 407-431, 2023, DOI:10.32604/sdhm.2023.026033

Abstract Corrosion can be very harmful to the service life and several properties of reinforced concrete structures. The metal magnetic memory (MMM) method, as a newly developed spontaneous magnetic flux leakage (SMFL) non-destructive testing (NDT) technique, is considered a potentially viable method for detecting corrosion damage in reinforced concrete members. To this end, in this paper, the indoor electrochemical method was employed to accelerate the corrosion of outsourced concrete specimens with different steel bar diameters, and the normal components BB_z and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal magnetic memory (MMM) method.… More >

Hyun-Wook Son¹, Ali A. Al-Hamid^1,2, Yong-Seok Na¹, Dong-Yeong Lee¹, Hyung-Won Kim^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760

Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been implemented for You Only Look… More >

Qingyue Zhao¹, Qiaoyu Gu², Zhijun Gao^3,*, Shipian Shao¹, Xinyuan Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500

Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed… More >

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Amel Ali Alhussan^1,*, Marwa M. Eid³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, Wu Xian¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >