Gradient Disappearance Research Articles

A cGAN-based tumor segmentation method for breast ultrasound images

Objective. This paper proposes a conditional GAN (cGAN)-based method to perform data enhancement of ultrasound images and segmentation of tumors in breast ultrasound images, which improves the reality of the enhenced breast ultrasound image and obtains a more accurate segmentation result. Approach. We use the idea of generative adversarial training to accomplish the following two tasks: (1) in this paper, we use generative adversarial networks to generate a batch of samples with labels from the perspective of label-generated images to expand the dataset from a data enhancement perspective. (2) In this paper, we use adversarial training instead of postprocessing steps such as conditional random fields to enhance higher-level spatial consistency. In addition, this work proposes a new network, EfficientUNet, based on U-Net, which combines ResNet18, an attention mechanism and a deep supervision technique. This segmentation model uses the residual network as an encoder to retain the lost information in the original encoder and can avoid the gradient disappearance problem to improve the feature extraction ability of the model, and it also uses deep supervision techniques to speed up the convergence of the model. The channel-by-channel weighting module of SENet is then used to enable the model to capture the tumor boundary more accurately. Main results. The paper concludes with experiments to verify the validity of these efforts by comparing them with mainstream methods on Dataset B. The Dice score and IoU score reaches 0.8856 and 0.8111, respectively. Significance. This study successfully combines cGAN and optimized EfficientUNet for the segmentation of breast tumor ultrasound images. The conditional generative adversarial network has a good performance in data enhancement, and the optimized EfficientUNet makes the segmentation more accurate.

New encoders based on depthwise separable convolution for SteganoGAN to improve steganography efficiency

In recent years, deep learning has developed vigorously, including many applications in the field of image steganography, of which steganogan is a representative. However, its network structure is a generative countermeasure network based on convolutional neural network (CNN). Because the generative adversial network (GAN) is difficult to train, there are problems such as mode collapse and gradient disappearance. Training the generative countermeasure network often requires many rounds of training, consuming a lot of computing resources. In this paper, the idea of deep separable convolution of mobile net is used for reference, and the encryption and generator in the generation countermeasure network for encryption is changed into a depthwise separable convolution encoder, which is compared with the original model, improves the speed of training and reasoning, and facilitates steganography on mobile devices and some low-performance devices.

Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction

Although time series prediction is widely used to estimate the future state of complex systems in various industries, accurate, interpretable and generalizable methods are still limited when used to make long-term nonstationary predictions. To this end, this article proposes deep attention fuzzy cognitive maps (DAFCM), which is composed of spatiotemporal fuzzy cognitive maps (STFCM), long short-term memory (LSTM) neural network, temporal fuzzy cognitive maps (TFCM) and residual structures. First, an improved attention mechanism is used to build spatiotemporal fuzzy cognitive maps that capture the spatial correlation in pairs of nodes and the temporal correlation of respective nodes. Second, the node state updated through the STFCM is input to the LSTM to capture the long-term trend of these series, and the TFCM with improved time attention is applied for the nonstationary problem in the time series. Finally, we add the state values of previous nodes into the DAFCM and build residual structures through linear transformation to prevent gradient explosion and gradient disappearance in long-term backpropagation. By combining the interpretability of fuzzy cognitive maps (FCM) and the high prediction accuracy of deep learning, the DAFCM can be used to accomplish tasks such as multivariate long-term nonstationary time series forecasting in multiple domains, and its efficiency is validated with 6 public datasets across 9 baselines.

Generative adversarial networks driven by multi-domain information for improving the quality of generated samples in fault diagnosis

The performance of intelligent fault diagnosis models is often hindered by the lack of available samples, a common issue in both the few-shot learning and imbalanced learning problems. While data generation has been shown to be an effective strategy for addressing this issue, existing methods tend to focus solely on the similarity of the generated samples, overlooking their diversity. In this regard, a self-reasoning training strategy that enables the participation of highly reliable generated samples in the training process is proposed. By augmenting the training samples, the strategy provides the generation model with diverse input information, effectively enhancing the diversity of the samples generated by the model. To assess the reliability of the generated samples, Pearson correlation coefficient between the generated and real samples in the amplitude–frequency domain is utilized. To this end, an amplitude–frequency domain generation model is constructed. In order to avoid issues such as gradient disappearance and convergence degradation during the generation process in the amplitude–frequency domain, a phase-frequency domain generation model as well as a time domain discriminator model are developed. While the phase-frequency domain generation model enhances the diversity of the generated samples in that domain, the time domain discriminator model ensures sample similarity by correlating the amplitude–frequency domain generation model with the phase-frequency domain generation model. Through experiments, it is demonstrated that the proposed method can effectively address the overfitting problem of generative adversarial networks with few samples, improving the diversity of the generated samples. Moreover, the improvement in fault diagnosis performance achieved by the samples generated by the proposed method further underscores its potential and superiority in practical applications.

Deep Wyner-Ziv Decoding Using Auxiliary Hierarchical Features

Wyner-Ziv (WZ) video coding shifts the burden of complex calculations from the encoder to the decoder, making it suitable for video coding scenarios with limited resources. Motivated by deep learning has shown superior performance over traditional methods, this paper proposes deep WZ video coding with the help of auxiliary hierarchical features in the decoder. It uses an autoencoder to encode WZ frames. In the decoder side, an inter-frame correlation model called SI-Net is employed to enhance WZ frame quality with Key frames. The auxiliary hierarchical features are extracted from Key frames through multi-level downsampling and employed in autoencoder to optimize feature extraction, avoiding gradient disappearance caused by deep networks. Since the auxiliary hierarchical features of Key frames describe spatial information and expand the network’s perception of video frame features, a high-quality WZ frame reconstructed can be obtained. Compared with previous work, our method shows obvious superiority on four video datasets with different degrees of motion.

Performance analysis of sentiment classification based neural network

Deep learning has more significant advantages for word embedding technology than sentiment analysis. This paper studies the application of deep learning on the word embedding problem in context, mainly discusses the RNN model with Word2Vec and without Word2Vec, then compares and analyzes their performance in the experiment, mainly evaluating the accuracy and test loss of seven models. This paper compares and illustrates the model which gets the different results in experiments, complementing the model and re-running the model, and analyzing the reasons for the difference in the performance of each model. The seven models are a single-layer neural network, multiple-layer (two and three) feed-forward neural networks, Convolutional Neural Network (CNN)- A feedforward neural network, which consists of single or multiple convolutional layers, pooling layers, and a fully connected layer on top, so this model is good at image processing. Long Short Term Memory (LSTM)- A temporal recurrent neural network, the advantage of the model is it could solve the gradient disappearance and explosion problem when it handles the long-sequence problem. Bi-directional Long Short Term Memory (Bi-LSTM)-Composed of forwarding LSTM and backward LSTM, it is very common for sequence labelling tasks that are related to the top and bottom, which are often used to model context information in NLP. Bi-directional Encoder Representation from Transformers (BERT)- A bidirectional language model. Finally, this paper analyses and evaluates these models with a specific illustration and research.

Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image.

Digital cores can characterize the true internal structure of rocks at the pore scale. This method has become one of the most effective ways to quantitatively analyze the pore structure and other properties of digital cores in rock physics and petroleum science. Deep learning can precisely extract features from training images for a rapid reconstruction of digital cores. Usually, the reconstruction of three-dimensional (3D) digital cores is performed by optimization using generative adversarial networks. The training data required for the 3D reconstruction are 3D training images. In practice, two-dimensional (2D) imaging devices are widely used because they can achieve faster imaging, higher resolution, and easier identification of different rock phases, so replacing 3D images with 2D ones avoids the difficulty of acquiring 3D images. In this paper, we propose a method, named EWGAN-GP, for the reconstruction of 3D structures from a 2D image. Our proposed method includes an encoder, a generator, and three discriminators. The main purpose of the encoder is to extract statistical features of a 2D image. The generator extends the extracted features into 3D data structures. Meanwhile, the three discriminators have been designed to gauge the similarity of morphological characteristics between cross sectionsof the reconstructed 3D structure and the real image. The porosity loss function is used to control the distribution of each phase in general. In the entire optimization process, a strategy using Wasserstein distance with gradient penalty makes the convergence of the training process faster and the reconstruction result more stable; it also avoids the problems of gradient disappearance and mode collapse. Finally, the reconstructed 3D structure and the target 3D structure are visualized to ascertain their similar morphologies. The morphological parameter indicators of the reconstructed 3D structure were consistent with those of the target 3D structure. The microstructure parameters of the 3D structure were also compared and analyzed. The proposed method can achieve accurate and stable 3D reconstruction compared with classical stochastic methods of image reconstruction.

Fruit Tree Disease Recognition Based on Residual Neural Network and Attention Mechanism

Fruit growing has played a huge role in solving food supply issues in many coutries. However, the yield and quality of fruits can be affected by various diseases, and thus timely and accurate identification of disease conditions is particularly important. Currently, using image recognition and object detection technology to diagnose fruit tree diseases has become a research hotspot in forestry informatization. Convolutional neural networks eliminate the preprocessing of manual feature selection and have high recognition performance. However, it is not easy to train due to the risk of gradient disappearance. In order to achieve better recognition effect, this research addresses the problem of applying small-scale data samples through data enhancement and transfer learning, and it optimizes the model by combining the two main attention mechanisms of SE and CBAM with ResNet50. Through experiments, it is found that the CBAM ResNet50 model has the best effect, improving the application performance of the studied model in actual scenarios.

A multi-scale video surveillance based information aggregation model for crime prediction

When studying the relationship and potential association between various data, researchers face multiple challenges: first, researchers need to collect data from multiple knowledge systems and channels, which challenges the breadth of expertise of researchers; second, it is challenging to perceive the changes of application scenarios and environment through these data such as image unstructured data that may contain useful information; third, short term behavior prediction with more frequent changes is challenging, especially when the sensitivity of the prediction model is insufficient and there is a lack of support from other feature data. This article designs a multi-scale information aggregation prediction model based on video surveillance for predicting and analyzing short-term time series with less data, such as criminal behavior. Firstly, a multi-scale human traffic perception and quantification model was established based on FairMOT to obtain human traffic time series data, which is an important association to strengthen the main features of subsequent short-term time series. Then, an improved prediction model, the Anti Saturation Gate Control Model (ASGCM), is designed to introduce entry-level techniques and add anti-saturation conversion modules, making ASGCM more sensitive, helping to reduce dependence on long-term features, and suppressing gradient disappearance and exploration problems. Finally, the application of urban crime prediction was used as an example for validation, and two sets of data were tested using a single sequence and a fused sequence, respectively. The results showed that the ASGCM method proposed in this paper can improve prediction accuracy while reducing computational complexity.

Gated recurrent unit-enhanced deep convolutional neural network for real-time industrial process fault diagnosis

When deep learning-based models are employed for the fault diagnosis of chemical processes, problems of poor calculation accuracy and efficiency often occur in the scenarios of high-dimensional, nonlinear, and time-varying data, which affects the overall robustness of the fault diagnosis model. This study proposes a novel Gated Recurrent Unit (GRU) - enhanced deep convolutional neural network (EDCNN) model for the improved fault detection and diagnosis of chemical processes. In the GRU-EDCNN model, a maximum smooth function (MSF) is developed and presented to replace the classical activation function for better matching the input data format, which can significantly improve the calculation accuracy and the overall efficiency of DCNN models. Moreover, the convolutional layers in DCNN are optimized by a decentralized convolutional structure, thereby reducing the problem of parameter redundancy. The issue of gradient disappearance is tackled by the embedment of GRU, whose ability to control time series features and gateway information can ensure that no overfitting occurs in the GRU-EDCNN training processes. In two case studies, the GRU-EDCNN model was applied to the benchmark Tennessee Eastman (TE) process and an acid gas absorption process, and the corresponding data of fault diagnosis rate (FDR), false positive rate (FPR), and fault diagnosis time (FDT) of the GRU-EDCNN model were analyzed and compared with those of the other deep learning-based models, verifying the effectiveness of the GRU-EDCNN model for fault diagnosis in both simulated and real-time industrial processes.

Deep learning-driven end-to-end segmentation of Martian river valleys

Martian river valleys are inextricably linked to Martian researches, including the development of the Martian climate, geological development and shallow water ice distribution. The segmentation of Martian river valleys provides materials for scientific research. Due to the absence of water, however, the features of Martian rivers are not obvious, resulting in inefficient segmentation. In order to realize high-accuracy segmentation, we propose an end-to-end segmentation method of Martian river valleys based on deep learning. We put forward the MDR (multi-scale double residual) convolution module and the TA (triple attention) module to improve Unet, and thus, MDR-Unet-TA. In this network, we replace two 3 × 3 convolution layers in Unet with an MDR convolution module, which uses multi-scale convolution to extract features of multiple sizes, and uses the residual connection to avoid gradient disappearance. In addition, we introduce a TA module into the skip connection, which reduces the feature map difference between the encoder and the decoder, and obtains detailed information during decoding. Experimental results demonstrate that compared with current semantic segmentation networks, MDR-Unet-TA obtains higher accuracy, F1 and IOU scores of 98.78%, 95.77% and 95.12% on the simple test set and 97.50%, 95.12% and 93.50% on the complex test set, respectively. Compared with MultiRes Unet, the improvement by MDR-Unet-Tri1 in accuracy, F1, and IOU is 0.92%, 2.40% and 2.70% respectively on the simple test set, and 1.98%, 1.84% and 3.58% respectively on the complex test set. MDR-Unet-TA improves the segmentation performance significantly compared with the original network, and realizes the end-to-end segmentation of Martian river valleys.

Bearing Fault Diagnosis Using ACWGAN-GP Enhanced by Principal Component Analysis

Rolling bearings are one of the most widely used parts in all kinds of rotating machinery (including wind power equipment) and also one of the most easily damaged parts, which makes fault diagnosis of rolling bearings a promising research field. To this end, recent studies mainly focus on fault diagnosis cooperating with deep learning. However, in practical engineering, it is very challenging to collect massive fault data, resulting in low accuracy of bearing fault classification. To solve the problem, an auxiliary classifier optimized by a principal component analysis method is proposed to generate an adversarial network model in which Wasserstein distance and gradient penalty are used to improve the stability of the network training process in case of over-fitting and gradient disappearance during model training. Specifically, we implement the model system using two main components. First, the one-dimensional time domain signal is transformed into a two-dimensional grayscale image and the principal component analysis is employed to reduce the dimension of the original data; this is instead of random noise as the input of the generator thereby preserving the characteristics of the original data to a certain extent. Second, in a generative adversarial network, the label information of the fault data is inserted into the generator to achieve supervised learning, thereby improving the data generation performance and reducing the training time cost. The experimental results show that our model could produce high-quality samples that are similar to real samples and that it could significantly improve the classification accuracy of fault diagnosis in the case of insufficient fault samples.

Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM

A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor the status and operation of various devices in the power system in real-time and optimise the dispatch of the power system through intelligent algorithms to achieve efficient operation of the power system. However, due to its complexity and uncertainty, how to effectively perform real-time prediction is an important challenge. This paper proposes a smart grid real-time prediction model based on the attention mechanism of convolutional neural network (CNN) combined with bi-directional long and short-term memory BiLSTM.The model has stronger spatiotemporal feature extraction capability, more accurate prediction capability and better adaptability than ARMA and decision trees. The traditional prediction models ARMA and decision tree can often only use simple statistical methods for prediction, which cannot meet the requirements of high accuracy and efficiency of real-time load prediction, so the CNN-BiLSTM model based on Bayesian optimisation has the following advantages and is more suitable for smart grid real-time load prediction compared with ARMA and decision tree. CNN is a hierarchical neural network structure containing several layers such as a convolutional layer, pooling layer and fully connected layer. The convolutional layer is mainly used for extracting features from data such as images, the pooling layer is used for the dimensionality reduction of features, and the fully connected layer is used for classification and recognition. The core of CNN is the convolutional operation, a locally weighted summation operation on the input data that can effectively extract features from the data. In the convolution operation, different features can be extracted by setting different convolution kernels to achieve feature extraction and classification of data. BiLSTM can capture semantic dependencies in both directions. The BiLSTM structure consists of two LSTM layers that process the input sequence in the forward and backward directions to combine the information in both directions to obtain more comprehensive contextual information. BiLSTM can access both the front and back inputs at each time step to obtain more accurate prediction results. It effectively prevents gradient explosion and gradient disappearance while better capturing longer-distance dependencies. The CNN-BiLSTM extracts features of the data and then optimises them by Bayes. By collecting real-time data from the power system, including power, load, weather and other factors, our model uses the features of CNN-BiLSTM to deeply learn real-time load data from smart grids and extract key features to achieve future load prediction. Meanwhile, the Bayesian optimisation algorithm based on the model can optimise the model’s hyperparameters, thus improving the model’s prediction performance. The model can achieve accurate prediction of a real-time power system load, provide an important reference for the dispatch and operation of the power system, and help optimise the operation efficiency and energy utilisation efficiency of the power system.

RETRACTED ARTICLE: Model optimization of English intelligent translation based on outlier detection and machine learning

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.

An intrusion detection method based on granular autoencoders

Intrusion detection systems have become one of the important tools for network security due to the frequent attacks brought about by the explosive growth of network traffic. Autoencoder is an unsupervised learning model with a neural network structure. It has a powerful feature learning capability and is effective in intrusion detection. However, its network construction suffers from overfitting and gradient disappearance problems. Traditional granular computing methods have advantages in solving such problems, but the process is relatively complex, the granularity dimension is high, and the computational cost is large, which is not suitable for application in intrusion detection systems. To address these problems, we propose a novel autoencoder: Granular AutoEncoders (GAE). The granulation reference set is constructed by random sampling. The granulation of training samples is based on single-feature similarity in a reference set to form granules. The granulation of multiple features results in granular vectors. Some operations of granules are defined. Furthermore, we propose some granular measures, including granular norms and granular loss functions. The GAE is further applied to the field of intrusion detection by designing an anomaly detection algorithm based on the GAE. The algorithm determines whether the network flows are anomalous by comparing the difference between an input granular vector and its output granular vector that is reconstructed by the GAE. Finally, some experiments are conducted using an intrusion detection dataset, comparing multiple metrics in terms of precision, recall, and F1-Score. The experimental results validate the correctness and effectiveness of the intrusion detection method based on GAE. And contrast experiments show that the proposed method has stronger ability for detecting anomalies than the correlation algorithms.

AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion

To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like architecture and incorporates key refinements to enhance network architecture and loss functions. Regarding the network architecture, the proposed two-branch adjacent feature fusion module, called AFSF, expands the number of channels to fuse the feature channels of several adjacent convolutional layers in the first half of the AFSFusion, enhancing its ability to extract, transmit, and modulate feature information. We replace the original rectified linear unit (ReLU) with leaky ReLU to alleviate the problem of gradient disappearance and add a channel shuffling operation at the end of AFSF to facilitate information interaction capability between features. Concerning loss functions, we propose an adaptive weight adjustment (AWA) strategy to assign weight values to the corresponding pixels of the infrared (IR) and visible images in the fused images, according to the VGG16 gradient feature response of the IR and visible images. This strategy efficiently handles different scene contents. After normalization, the weight values are used as weighting coefficients for the two sets of images. The weighting coefficients are applied to three loss items simultaneously: mean square error (MSE), structural similarity (SSIM), and total variation (TV), resulting in clearer objects and richer texture detail in the fused images. We conducted a series of experiments on several benchmark databases, and the results demonstrate the effectiveness of the proposed network architecture and the superiority of the proposed network compared to other state-of-the-art fusion methods. It ranks first in several objective metrics, showing the best performance and exhibiting sharper and richer edges of specific targets, which is more in line with human visual perception. The remarkable enhancement in performance is ascribed to the proposed AFSF module and AWA strategy, enabling balanced feature extraction, fusion, and modulation of image features throughout the process.

PreVFs-RG: A Deep Hybrid Model for Identifying Virulence Factors Based on Residual Block and Gated Recurrent Unit.

Many infectious diseases are caused by bacterial pathogens. The pathogenic mechanisms of bacterial pathogens are complex and it is usually caused by virulence factors (VFs) in many cases. Whether VFs exist is the main difference between the genomes of pathogenic and non-pathogenic bacteria. Therefore, identification of VFs is of great significance for exploring the pathogenesis of infectious diseases. In this paper, we develop a new model for predicting VFs based on multiple features and deep learning. Firstly, we used kmer, Dipeptide deviation from expected mean (DDE) and Amino acid composition (AAC) to extract features. And the deep model is constructed by integrating residual block and gated recurrent unit (GRU). Residual neural network is a kind of jump connection network to avoid gradient explosion or gradient disappearance, which is used to train deeper networks. Two residual blocks, which are separated by Relu activation function and Batch Normalization(BN) layer are used. Finally, the combined features processed by resnet are input into the recurrent structure GRU, and important information is filtered out through reset gate and update gate. The results of the proposed model are better than those of the existing methods by 10-fold cross-validation, and the accuracy is 93.81% and 95.43%, respectively. It shows that the deep hybrid model for identifying virulence factors based on residual block and gate recurrent unit is effective and feasible. The codes and datasets are accessible at https://github.com/VFs625/PreVFs-RG.git.

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

The increasingly large employment group in colleges and universities has brought huge employment pressure to the society. Therefore, improving the level of professional ability of college students, providing students with personalized career development direction planning, and helping students to establish a correct concept of career selection are feasible ways to solve the structural contradiction of college students’ employment. The arrival of big data era provides new development opportunities for employment work in colleges and universities. Big data drives the high-quality development of precise employment in colleges and universities, which is inevitably required to reach the precise requirements of work development, information mastering, platform docking, information pushing, and help work. The traditional employment guidance work is backward in means and poor in timeliness, which leads to poor intervention effect on students’ employment expectation. With the deepening of digital campus construction and the development of big data technology, massive educational data has been accumulated but not reasonably utilized. Therefore, in this paper, we take the massive student data accumulated in digital campus as the research object, based on the research method of convolutional neural network, to explore the hidden personalized information of students behind it, predict their future career development direction, and provide scientific and technological support for the work of university education and teaching. Thus, the architecture classical DenseNet was improvised to avoid gradient disappearance and guarantee the classification accuracy, thereby targeting to reduce the number of connections. The study also proposed a 2-DenseNet model, wherein the attention module was embedded into 2-DenseNet followed by the conduction of training and validation on the university employment dataset. The experimental results show that the method proposed in this paper could predict the appropriate career development direction timely and effectively based on multiple perspectives of students’ comprehensive quality. This would enable the students to plan and adjust their employment expectations leading to disseminating of efficient employment guidance in colleges and universities.

Target Tracking Method for Transmission Line Moving Operation Based on Inspection Robot and Edge Computing

Aiming at the problems of low accuracy and high loss rate when the traditional target tracking (TT) method is applied to the TT of the moving operation of the transmission line, a transmission line based on an inspection robot and edge computing (EC) is proposed: the mobile job TT method. First, the basic framework of the TT algorithm is proposed, relying on the edge device to develop the TT system for mobile operations on the transmission line. Video information is collected by an intelligent inspection robot and sent to the target tracking system in the edge device for processing to obtain accurate data. Then, the gradient disappearance and explosion problems caused by the increase of network depth are solved by using the deep residual network. The traditional deep residual network is improved by introducing the improved bidirectional feature reinforcement network and the classification and regression subnet. The loss of position texture information is remedied, and the accurate tracking of the moving target on transmission line is realized. Finally, the real-time data acquisition of mobile operation target is realized by using an intelligent inspection robot, and the experimental verification is conducted. The proposed algorithm is compared and analyzed against the three other algorithms using the same data set through simulation experiments. The results show the precision, recall rate, accuracy, and comprehensive evaluation index F1 value of the proposed algorithm rank highest, reaching 93.8%, 90.2%, 83.8%, and 89.8%, respectively, compared with the other algorithms.

Research on the intelligent path of college students’ network ideological and political education based on big data mining technology

Abstract As big data mining technology is known to directly change the development direction and internal management, and the behaviour of college students’ political wisdom network has also changed significantly, hence the application and analysis of data mining technology becomes indispensable. Bidirectional gated cyclic cell network can solve the gradient disappearance or gradient explosion existing in traditional models, but can also make up for the deficiency of extracting the contextual semantic information of long text effectively. Using the combination of big data-based mining technology and college students’ network ideology and politics, this paper designed a set of BiGRU_Attention framework based on college students’ network ideological wisdom framework and adopted big data mining technology to collect data. First, according to the Internet technology and information collection, design a complete set of college students’ ideological wisdom framework. Then, the GRU structure, BiGRU framework and Attention mechanism combined with BiGRU_Attention model are introduced. Meanwhile, Word2Vec is used to train the Chinese word vector. Finally, the BiGRU_Attention proposed in this paper is compared with several models. Experimental results show that the BiGRU_Attention proposed in this paper has a better accuracy of 96.32% over other models. The experimental results also show that the framework of ideological and political intelligence proposed in this paper can be applied to the campus environment of college students.