scholarly journals Application of Neural Network Methods Based on Genetic Algorithm for Breast Cancer Prediction

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Rino -
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
The Body ◽  
Algorithm Optimization ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Risk Of Cancer

Cancer is a major challenge for mankind. Cancer can affect various parts of the body. This deadly disease can be detected in people of all ages. However, the risk of cancer increases with increasing age. Breast cancer is the most common cancer among women, and form largest cause of death for women as well. Then there are problems in the detection of breast cancer, resulting in the patient experiencing unnecessary treatment and cost. Insimilar studies, there are several methods used but there are problems due to the shape of the cancer cells are nonlinear. Neural networks can solve these problems, but neural network is weak in terms of determining the value of the parameter, so it needs to be optimized. Genetic algorithm is one of the optimization methods is good, therefore the values ​​of the parameters of the neural network will be optimized by using a genetic algorithm so as to get the best value of the parameter. Neural Network-based GA algorithm has the higher accuracy value than just using Neural Network algorithm. This is evident from the increase in value for the accuracy of the model Neural Network algorithm by 95.42% and the accuracy of algorithm-based Neural Network algorithm GA (Genetic Algorithm) of 96.85% with a difference of 1.43% accuracy. So it can be concluded that the application of Genetic Algorithm optimization techniques to improve the accuracy values on Neural Network algorithm.

scholarly journals Optimization of Application of Genetic Algorithm Using C4.5 Method to Predict Breast Cancer Disease

bit-Tech ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Hartana Wijaya
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Techniques ◽  
The Body ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
C4.5 Algorithm ◽  
Parameter Values ◽  
Risk Of Cancer

Cancer is a big challenge for humanity. Cancer can affect various parts of the body. This deadly disease can be found in humans of all ages. However, the risk of cancer increases with age. Breast cancer is the most common cancer among women, and is the biggest cause of death for women. Then there are problems in the detection of breast cancer, causing patients to experience unnecessary treatment and huge costs. In a similar study, there were several methods used but there were problems due to the shape of nonlinear cancer cells. The C4.5 method can solve this problem, but C4.5 is weak in terms of determining parameter values, so it needs to be optimized. Genetic Algorithm is one of the good optimization methods, therefore the parameter values ​​of C4.5 will be optimized using Genetic Algorithms to get the best parameter values. The results of this study are that C4.5 Algorithm based on genetic algorithm optimization has a higher accuracy value (96%) than only using the C4.5 algorithm (94.99%) and which is optimized with the PSO algorithm (95.71%). This is evident from the increase in the value of accuracy of 1.01% for the C4.5 algorithm model that has been optimized with genetic algorithms. So it can be concluded that the application of genetic algorithm optimization techniques can increase the value of accuracy in the C4.5 algorithm.

scholarly journals Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Sun ◽  
Wenjun Yi ◽  
Dandan Yuan ◽  
Jun Guan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Initial Alignment ◽  
Elman Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

scholarly journals Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

2019 ◽  
Vol 32 (2) ◽  
pp. 276-282 ◽  
Author(s):  
Richard Ha ◽  
Simukayi Mutasa ◽  
Jenika Karcich ◽  
Nishant Gupta ◽  
Eduardo Pascual Van Sant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Molecular Subtype ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Evaluation of optimization methods for solving the receptor model for chemical mass balance

2015 ◽  
Vol 80 (2) ◽  
pp. 253-264 ◽  
Author(s):  
N. Anu ◽  
S. Rangabhashiyam ◽  
Antony Rahul ◽  
N. Selvaraju
Keyword(s):  
Genetic Algorithm ◽  
Air Quality ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Quality Analysis ◽  
Algorithm Optimization ◽  
Chi Square ◽  
Source Contribution ◽  
Source Contributions

Balance (CMB) model has been extensively used in order to determine source contribution for particulate matters (size diameters less than 10 ?m and 2.5 ?m) in the air quality analysis. A comparison of the source contribution estimated from the three CMB models (CMB 8.2, CMB-fmincon and CMB-GA) have been carried out through optimization techniques such as ?fmincon? (CMB-fmincon) and genetic algorithm (CMB-GA) using MATLAB. The proposed approach has been validated using San Joaquin Valley Air Quality Study (SJVAQS) California Fresno and Bakersfield PM10 and PM2.5 followed with Oregon PM10 data. The source contribution estimated from CMB-GA was better in source interpretation in comparison with CMB8.2 and CMB-fmincon. The performance accuracy of three CMB approaches were validated using R-square, reduced chi-square and percentage mass tests. The R-square (0.90, 0.67 and 0.81, 0.83), Chi-square (0.36, 0.66 and 0.65, 0.43) and percentage mass (67.36 %, 55.03 % and 94.24 %, 74.85 %) of CMB-GA showed high correlation for PM10, PM2.5 Fresno and Bakersfield data respectively. To make a complete decision, the proposed methodology has been bench marked with Portland, Oregon PM10 data with best fit with R2 (0.99), Chi-square (1.6) and percentage mass (94.4 %) from CMB-GA. Therefore, the study revealed that CMB with genetic algorithm optimization method holds better stability in determining the source contributions.

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