A kind of E-government website evaluation method based on Neural Network

2006 ◽  
Author(s):  
Chun Hua Ju ◽  
Ying Liang ◽  
Dong Sheng Liu
Keyword(s):  
Neural Network ◽  
Evaluation Method ◽  
Website Evaluation ◽  
Government Website

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

scholarly journals Research on Anomalous Behavior Detection and Dangerousness Assessment among Civil Aviation Passengers at Checkpoints

2021 ◽  
Author(s):  
Haibin Shen ◽  
yan Zhang
Keyword(s):  
Neural Network ◽  
Index System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Anomalous Behavior ◽  
Civil Aviation ◽  
Risk Level ◽  
Neural Network Method ◽  
Behavior Detection ◽  
Network Method

Abstract Traditional civil aviation security check measures are focused on baggage rather than passengers. The goal of this study is to enhance the level and effectiveness of security measures. We propose an anomalous behavior detection technique for civil aviation passengers and a passenger risk-assessment method based on a neural network method. A large number of real cases were analyzed and summarized to extract indicators of anomalous behavior of civil aviation passengers, and an index system was developed to detect anomalous behavior of passengers at checkpoints. A neural network method was used to evaluate the passengers and classify the risk level to detect potentially dangerous personnel, monitor people, and create an emergency warning system. The synthetic minority oversampling technique (SMOTE), the conjugate gradient method, and a multilayer perceptron neural network were used to classify the risk level of passengers at checkpoints. The results demonstrated that the proposed index system and evaluation method were well suited to deal with the ambiguity and uncertainty in the recognition process. The anomalous behavior of civil aviation passengers at checkpoints and the associated threat level were accurately identified.

scholarly journals A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Cheng ◽  
Yingying Cai ◽  
Haomai Chen ◽  
Zhuang Cai ◽  
Gang Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Learning Strategies ◽  
Deep Neural Network ◽  
Evaluation Method ◽  
Bloom's Taxonomy ◽  
Automatic Evaluation ◽  
Cognitive Level ◽  
Text Data ◽  
Bloom’S Taxonomy

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

Hybrid Strategy for Product Quality Credit Evaluation Based on Statistics and Artificial Neural Network

2011 ◽  
Vol 361-363 ◽  
pp. 1499-1505 ◽  
Author(s):  
Li Mei Liu ◽  
Heng Qian ◽  
Yong Chao Gao ◽  
Ding Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Product Quality ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Credit System ◽  
Hybrid Strategy ◽  
Credit Evaluation ◽  
Social Credit ◽  
Artificial Neural

In China, quality credit is an important part of the social credit system, and evaluation of quality credit is the key to the construction of quality credit system. In this paper, on the basis of product quality credit factor analysis and evaluation index construction, a hybrid strategy of three stages is proposed according to the different nature of indicators. The emphasis is put on intelligent evaluation model based on statistics and artificial neural network. According to the results of experimental verification, this credit evaluation method shows a high accuracy for the evaluation of quality credit.

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