Experimental Study and Prediction Model of the Flexural Strength of concrete Containing Fly Ash and Ground Granulated Blast-Furnace Slag by Hua Zhang, Qing-Fu Li, Hua-De Zhou, Zong-Ming Song

“…A flexural strength prediction model of the concrete was developed based on a backpropagation neural network (BPNN) and a support vector machine (SVM) model. …”
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The Construction of an Action-Speech Feature-Based School Violence Recognition Algorithm and Occupational Therapy Education Model for Adolescents by Shuaiqing Zhang, Huan Li

“…For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. …”
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RETRACTED: Research on smart grid management and security guarantee of sports stadiums based on GCNN-GRU and self-attention mechanism by Song Li

“…This paper proposes a method based on the Graph Convolutional Neural Network (GCNN) with Gated Recurrent Units (GRU) and a self-attention mechanism. …”
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A hybrid model for smart grid theft detection based on deep learning by Yinling LIAO, Jincan LI, Bing WANG, Jun ZHANG, Yaoyuan LIANG

“…A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems.…”
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Development of a standardized data collection and intelligent fabric quality prediction system for the weaving department by Ning Dai, Lunjun Li, Kaixin Xu, Zhehao Lu, Xudong Hu, Yanhong Yuan

“…This study leverages the standardization and interoperability features of open platform communications unified architecture technology to facilitate data acquisition within the weaving department, establishing a reliable Internet of Things framework that supports subsequent fabric quality prediction, and optimizing the back propagation neural network through the K-means clustering algorithm and particle swarm optimization to predict the type and number of fabric defects. …”
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Short Video Copyright Storage Algorithm Based on Blockchain and Expression Recognition by Yang Yang, Dingguo Yu

“…In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). …”
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E-commerce big data processing based on an improved RBF model by Lu Qiuping

“…Addressing this critical issue, this study introduces an innovative approach employing a radial basis function neural network for predicting CC within the e-commerce sector. …”
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Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies by Xian Wu, Huan Liu

“…Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. …”
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Maize quality detection based on MConv-SwinT high-precision model. by Ning Zhang, Yuanqi Chen, Enxu Zhang, Ziyang Liu, Jie Yue

“…Experimental results demonstrate that the MC-Swin Transformer model proposed in this paper significantly outperforms traditional convolutional neural network models in key metrics such as accuracy, precision, recall, and F1 score, achieving a recognition accuracy rate of 99.89%. …”
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Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis by Jun He, Xiang Li, Yong Chen, Danfeng Chen, Jing Guo, Yan Zhou

“…First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. …”
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MODELING AND SIMULATION OF COAL-ROCK RECOGNITION SYSTEM OF SHEARER BASED ON CYBER-PHYSICAL SYSTEM (MT) by ZHAO LiJuan, SI HuanHuan, SHI Lei, JIN Xin, ZHANG MeiChen

“…The overall simulation of the system was carried out, the experimental data was extracted from the physical prototype system, and the information processing system model was constructed based on the wavelet packet feature extraction and the PSO-BP neural network algorithm. Through the joint simulation of Ptolemy II and Matlab, the reliability of the CPS-based coal-rock identification system is verified.The simulation results show that the average error value of the system co-simulation is larger than the result obtained by the single method simulation, the maximum error is 0.027 924, and the error is still within the allowable range. …”
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Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image by Shiliang Lu, Juwei Zhang

“…The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.…”
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Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region by L. Aneesh Euprazia, A. Rajeswari, K. K. Thyagharajan, N. R. Shanker

“…The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. …”
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Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data by Wenchen Shan, Xianqiang Wang, Yubo Jiao

“…Three regression-based numerical models using multiple linear regression (MLR), back-propagation neural network (BPNN), and support vector regression (SVR) techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. …”
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Construction of advanced persistent threat attack detection model based on provenance graph and attention mechanism by Yuancheng LI, Hao LUO, Xinyu WANG, Jiexuan YUAN

“…In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.…”
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Modulation recognition driven by signal enhancement by CHENG Fengyun, ZHOU Jin

“…The experimental results show that the proposed method can achieve better recognition accuracy performance in small sample training sets and fading channel environments compared to existing recognition methods based on long short-term memory (LSTM), convolutional neural network (CNN), attention mechanism, etc.…”
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Research and DSP Implementation of Speech Enhancement Technology Based on Dynamic Mixed Features and Adaptive Mask by Jie Yang, Yachun Tang

“…Then, an improved deep neural network model is designed to effectively improve the speech enhancement performance. …”
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LSTM-based estimation of lithium-ion battery SOH using data characteristics and spatio-temporal attention. by Gengchen Xu, Jingyun Xu, Yifan Zhu

“…Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. …”
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Multiparameter Logging Evaluation of Chang 73 Shale Oil in the Jiyuan Area, Ordos Basin by Bobiao Liu, Chengqian Tan, Yinchao Huai, Yuhan Tan, Han Zhang, Zhao Feng

“…The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. …”
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Adaptation optimizes sensory encoding for future stimuli. by Jiang Mao, Constantin A Rothkopf, Alan A Stocker

“…We further tested our hypothesis by analyzing the internal sensory representations of a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). …”
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