Prediction Model Grade Point Average using Backpropagation Neural Network and Multiple Linear Regression

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

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Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

SN Applied Sciences ◽

10.1007/s42452-020-03974-7 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Charles Gbenga Williams ◽

Oluwapelumi O. Ojuri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydraulic Conductivity ◽

Linear Regression ◽

Multiple Linear Regression ◽

Coarse Grained ◽

Soil Types ◽

Wide Range ◽

Artificial Neural ◽

Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽

10.3390/ma14112998 ◽

2021 ◽

Vol 14 (11) ◽

pp. 2998

Author(s):

Xinyong Zhang ◽

Liwei Sun

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Prediction Model ◽

Bayesian Regularization ◽

Backpropagation Neural Network ◽

Swarm Optimization ◽

Modal Frequency ◽

Supporting Structure ◽

First Order ◽

Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

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