Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.

/pdf/improved-naive-bayes-classification-algorithm-for-traffic-mmxu731zwf.pdf

Improved Naive Bayes Classification Algorithm for Traffic Risk Management

Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications’ popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method.

A Comparative Study of Traffic Classification Techniques for Smart City Networks.

Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent years, intelligent fault diagnosis method represented by deep learning (DL) has been receiving increasing attention. In order to ameliorate the full training of the deep network, improve the model accuracy, and perfect the analysis of mechanical vibration signals with huge amount of information, a multi-scale attention mechanism residual network (MSA-ResNet) fault diagnosis method is proposed in this paper. First, an attention mechanism block is introduced to construct a new type of residual block combination. Second, a multi-scale structure is constructed by choosing an appropriate convolution kernel size. Finally, the overall framework of MSA-ResNet is constructed for efficient training and failure pattern recognition. The MSA-ResNet algorithm introduces an attention mechanism in each residual module of the residual network (ResNet), which improves the sensitivity to features. The features of different scales are obtained through the multi-scale convolution kernel, and the multi-scale feature extraction of complex nonlinear mechanical vibration signals is realized. The processing of original vibration signal rarely involves artificial interference, which is more conducive to industrial application of the proposed method. Diagnostic experiments are conducted on bearing datasets from the Case Western Reserve University (CWRU) and the Machinery Failure Prevention Technology (MFPT) to verify the effectiveness of the proposed method. The results illustrating the rolling bearing fault diagnosis method based on MSA-ResNet have advantages in multi-scale feature extraction, noise immunity, and fault classification accuracy.

Multi-scale attention mechanism residual neural network for fault diagnosis of rolling bearings

The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.

Explainable Internet Traffic Classification

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.

Accurate and Efficient Performance Prediction for Mobile IoV Networks Using GWO-GR Neural Network

The number of applications based on the Internet is increasing, which results the traffic becoming more and more complex. Therefore, how to improve the service quality and security of the network is becoming more and more important. This paper studies the application of SVM in traffic identification to classify network traffic. Through data collection and feature generation methods and network traffic feature screening methods, SVM is used as a classifier by using the generalization capability of SVM, and the parameters and kernel functions of the SVM are adjusted and selected based on cross comparison ideas and methods. Using the cross‐validation method to make the most reasonable statistics for the classification and recognition accuracy of the adjusted support vector machine avoids the situation that the classification accuracy of the support vector machine is unstable or the statistics are inaccurate. Finally, a traffic classification and identification system based on SVM is realized. The final recognition rate of encrypted traffic is up to 99.31%, which overcomes the disadvantages of traditional traffic identification and achieves a fairly reliable accuracy.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6314

Retracted : Traffic identification and traffic analysis based on support vector machine

This paper further analyzes the energy consumption model of the LEACH protocol, and proposes a routing algorithm for wireless sensor networks based on energy balance. It uses a more reasonable clustering method; thereby it can further balance energy consumption of the network. In this algorithm the cluster head periodically compute the residual energy of all of members in each work cycle. It takes into full account the residual energy of nodes to determine the route. It chooses an appropriate route under the premise that transmission efficiency and network energy consumption are balanced. Simulation experiments results show that the routing algorithm based on energy balance proposed by this paper can balance the relationship of routing and energy consumption, and can prolong the network lifetime.

Research on Routing Algorithm for Wireless Sensor Network Based on Energy Balance

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanic...

/pdf/extraction-of-local-and-global-features-by-a-convolutional-60rbkcv4t5.pdf

Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults:

In multi-objective particle swarm optimization (MOPSO) algorithms, improving the diversity of solutions is very difficult yet an important problem. In this paper, a new MOPSO algorithm of searching the Pareto-optimal solution is introduced, called multi-objective particle swarm optimization algorithm based on self-update strategy (SU-MOPSO). The mainly strategy of SU-MOPSO is that improving the diversity of each particle local best position (usually called pbest) to satisfy the swarm update's needs, and fundamentally enhances the diversity of Pareto set by rising the candidate quantity. The proposed SU-MOPSO algorithm has been compared with ES-MOPSO algorithm. The results demonstrate that the SU-MOPSO algorithm has gained better convergence with even distributing and diversity of Pareto set.

Multi-objective Particle Swarm Optimization Algorithm Based on Self-update Strategy

To develop a new composite material excavator dipper teeth,which matches the holder of excavators in service on worksites,all design data of old tooth should be available.The excavator dipper tooth is a functional component with irregular shape,fitting constraints in inner cavity,and complex figure.It is extremely time-consuming to measure the size of teeth through general instruments,meanwhile the measuring accuracy can not be guaranteed,and especially its inner cavity and pinhole position was even harder to measure.So the material object reverse engineering technique was used to obtain the sizes and 3D model of the tooth exactly.The methods for extracting feature curves needed in the following tooth modeling from measured 3D coordinate points of the tooth were proposed,including the tooth 3D modeling based on extracted curves.The procedures and key links during feature extracting using Imageware and solid modeling based on those curves in Pro/E were also presented.First-trial-production dipper teeth according to the drawing generated from reconstructed CAD model can match with its holder and work well on the worksite,it indicates that the model reconstruction method mentioned above is effective and practical.

Wang Jianguo

Papers

Retracted : Traffic identification and traffic analysis based on support vector machine

Research on Routing Algorithm for Wireless Sensor Network Based on Energy Balance

Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults:

Multi-objective Particle Swarm Optimization Algorithm Based on Self-update Strategy

Geometric reverse resolution and model reconstruction of excavator dipper teeth based on feature extraction