scholarly journals HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

2016 ◽  
Vol 38 (1) ◽  
pp. 151-157 ◽  
Author(s):  
BIANCA MACHADO CAMPOS ◽  
ALEXANDRE PIO VIANA ◽  
SILVANA SILVA RED QUINTAL ◽  
CIBELLE DEGEL BARBOSA ◽  
ROGÉRIO FIGUEIREDO DAHER
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Group Formation ◽  
Psidium Guajava ◽  
Optimal Number ◽  
Heterotic Group ◽  
Linear Discriminant ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Slum Dwellers Perception Towards Ujjwala Yojana, a Government-Lead LPG Intervention Programme in India: An Empirical Investigation

2021 ◽  
pp. 239448112110203
Author(s):  
Koustubh Kanti Ray
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Support Vector ◽  
Intervention Programme ◽  
Linear Discriminant ◽  
Customer Perception ◽  
Slum Dwellers ◽  
Functional Factors ◽  
Artificial Neural

Numerous studies are available in the academic literature that investigates the customer perception under different contexts. In the present research the researcher tries to investigate the customer perception towards the Indian Government-sponsored social programme from the slum dwellers’ prospective. The author believes that the customer perception towards the government-lead liquefied petroleum gas intervention programme is influenced by multiple functional factors. The functional factors include both process or delivery variables and the outcome factors. In order to test the hypothesis, machine learning binary classifiers like logit, support vector machine, linear discriminant analysis, quadratic discriminant analysis and artificial neural network models are adopted. The binary classifier model efficiencies are analysed with multiple performance measurement parameters like accuracy rate, error rate, F-score, precision, kappa coefficient, Matthews correlation coefficient and area under receiver operating characteristic. While evaluating between the degree of accuracy between actual and predicted cases, the model efficiency results indicate a better predictive power of the classifier models. In relative performance of classifier models, artificial neural network outperformed the other models adopted in the empirical research.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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