Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN.

In the research of network abnormal traffic detection, in view of the characteristics of high dimensionality and redundancy in traffic data and the loss of original information caused by the pooling operation in the convolutional neural network, which leads to the problem of unsatisfactory detection effect, this paper proposes a network abnormal traffic detection algorithm based on RIC-SC-DeCN to improve the above problems. Firstly, a recursive information correlation (RIC) feature selection mechanism is proposed, which reduces data redundancy through the maximum information correlation feature selection algorithm and recursive feature elimination method. Secondly, a skip-connected deconvolutional neural network model (SC-DeCN) is proposed to reduce the information loss by reconstructing the input signal.

Research on an Identification Method for Gas Disaster Risk Based on the Selective Ensemble Classification Model.

To improve the accuracy of gas disaster risk identification, a selective ensemble classification model is proposed based on clustering selection and a new degree of combination fitness (CS-NDCF). First, nine base classifiers for gas disasters are constructed on the training data set, including the backpropagation (BP) neural network classifier, naive Bayes (NB) classifier, -nearest neighbor (KNN) classifier, logistic regression (LR) classifier, decision tree (DT) classifier, support vector machine (SVM) classifier, SVM classifier with cross-validation (SVMCV), random forest (RF) classifier, and gradient boosting DT (GBDT) classifier. Second, the -means clustering algorithm is used to cluster the base classifiers according to their classification performance.

Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network.

To improve the utilization of mine gas concentration monitoring data with deep learning theory, we propose a gas concentration forecasting model with a bidirectional gated recurrent unit neural network (Adamax-BiGRU) using an adaptive moment estimation maximum (Adamax) optimization algorithm. First, we apply the Laida criterion and Lagrange interpolation to preprocess the gas concentration monitoring data. Then, the MSE is used as the loss function to determine the parameters of the hidden layer, hidden nodes, and iterations of the BiGRU model.

Water-processable polyaniline with covalently bonded single-walled carbon nanotubes: enhanced electrochromic properties and impedance analysis.

Hybrid electrochromic materials were readily synthesized via copolymerization of aniline with p-phenylenediamine-functionalized single-walled carbon nanotubes (SWCNTs) in the presence of poly(styrene sulfonate) (PSS) dopant in an aqueous medium. Polyaniline (PANI)-grafted SWCNTs are formed, and they are uniformly dispersed in the PANI/PSS matrix. Impedance analysis shows that the charge-transfer resistances of the hybrids at all states are reduced drastically with increasing SWCNT loading.