Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model

Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is a free-distribution estimator because there's no assumptions that have to be satisfied. However, modeling data using ANN has some problems such as selection of network topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.Keywords:Neural Network, Genetic Algorithm, Ordinary Least Square

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Prediction of wetland area and depth using linear regression model and artificial neural network based cellular automata

Ecological Informatics ◽

10.1016/j.ecoinf.2021.101272 ◽

2021 ◽

pp. 101272

Author(s):

Tamal Kanti Saha ◽

Swades Pal ◽

Raju Sarkar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Cellular Automata ◽

Regression Model ◽

Linear Regression Model ◽

Wetland Area ◽

Artificial Neural

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Forecasting the changes in daily stock prices in Shanghai Stock Exchange using Neural Network and Ordinary Least Squares Regression

Investment Management and Financial Innovations ◽

10.21511/imfi.17(3).2020.22 ◽

2020 ◽

Vol 17 (3) ◽

pp. 292-307

Author(s):

Sunantha Prime

Keyword(s):

Neural Network ◽

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Stock Prices ◽

Global Financial Crisis ◽

Stock Exchange ◽

Multiple Linear Regression Model ◽

Ordinary Least Squares

The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.

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Skill Comparison of Multiple-Linear Regression Model and Artificial Neural Network Model in Seasonal Rainfall Prediction-North East Nigeria

Asian Research Journal of Mathematics ◽

10.9734/arjom/2018/44049 ◽

2018 ◽

Vol 11 (2) ◽

pp. 1-10

Author(s):

Ebiendele Ebosele Peter ◽

Ebiendele Eromosele Precious

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Regression Model ◽

Neural Network Model ◽

Linear Regression Model ◽

Artificial Neural Network Model ◽

Multiple Linear Regression Model ◽

Rainfall Prediction ◽

North East

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Comparison of Artificial Neural Network Models and Multiple Linear Regression Models in Cargo Port Performance Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3570 ◽

2011 ◽

Vol 403-408 ◽

pp. 3570-3577 ◽

Cited By ~ 1

Author(s):

P.Oliver Jayaprakash ◽

K. Gunasekaran ◽

S. Muralidharan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Multiple Linear Regression ◽

Service Time ◽

Multiple Linear Regression Model ◽

Linear Regression Models ◽

Neural Network Models ◽

Operational Characteristics ◽

Artificial Neural

Cargo ports operational performance was specified typically through revenue earned, quantum of cargo handled and number of ships serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over pre-berth waiting time of vessels waiting and berthing time of ships at a port. An Indian port’s ship movement and port operational characteristics had been studied for five years (2005-2009). Ship’s service time was the crucial parameter used to quantify the port performance. This paper focused on building an artificial neural network technique based model to illustrate the relationship between service time and port operational characteristics. Validations of ANN model, comparing multiple linear regression model outputs were reported.

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