scholarly journals Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Li Tiancheng ◽  
Ren Qing-dao-er-ji ◽  
Qiu Ying
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Inner Mongolia ◽  
Prediction Method ◽  
Classification Algorithm ◽  
Bayesian Algorithm ◽  
Naive Bayesian ◽  
Model Based ◽  
Naïve Bayesian

Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

scholarly journals Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 544
Author(s):  
Hai Chen ◽  
Lei He ◽  
Weiqi Qian ◽  
Song Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Model Simulation ◽  
Prediction Method ◽  
Aircraft Design ◽  
Aerodynamic Characteristics ◽  
Aerodynamic Coefficients ◽  
Pitch Moment ◽  
Drag And Lift

Both symmetric and asymmetric airfoils are widely used in aircraft design and manufacture, and they have different aerodynamic characteristics. In order to improve flight performance and ensure flight safety, the aerodynamic coefficients of these airfoils must be obtained. Various methods are used to generate aerodynamic coefficients. The prediction model is a promising method that can effectively reduce cost and time. In this paper, a graphical prediction method for multiple aerodynamic coefficients of airfoils based on a convolutional neural network (CNN) is proposed. First, a transformed airfoil image (TAI) was constructed by using the flow-condition convolution with the airfoil image. Next, TAI was combined with the original airfoil image to form a composite airfoil image (CAI) that is used as the input of the CNN prediction model. Then, the structure and parameters of the prediction model were designed according to CAI features. Finally, a sample set that was generated on the basis of the deformation of symmetrical airfoil NACA 0012 was used to train and test the prediction model. Simulation results showed that the proposed method based on CNN could simultaneously predict the pitch-moment, drag, and lift coefficients, and prediction accuracy was high.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

scholarly journals Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16726-16741 ◽  
Author(s):  
Jiancai Song ◽  
Guixiang Xue ◽  
Xuhua Pan ◽  
Yunpeng Ma ◽  
Han Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Heat Load ◽  
Load Prediction ◽  
Model Based

ACNB

2013 ◽  
Vol 8 (1) ◽  
pp. 23-35 ◽  
Author(s):  
Fadi Odeh ◽  
Nijad Al-Najdawi
Keyword(s):  
Association Rule ◽  
Text Categorization ◽  
Prediction Method ◽  
Associative Classification ◽  
Bayesian Algorithm ◽  
New Approach ◽  
Naive Bayesian ◽  
Association Rule Discovery ◽  
Naïve Bayesian ◽  
Traditional Classification

Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of associative classification on the high dimensional data in text categorization. This research focuses on prediction, a very important step in classification, and introduces a new prediction method called Associative Classification Mining based on Naïve Bayesian method. The running time is decreased by removing the ranking procedure that is usually the first step in ranking the derived Classification Association Rules. The prediction method is enhanced using the Naïve Bayesian Algorithm. The results of the experiments demonstrate high classification accuracy.

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