scholarly journals Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohamad Haider Abu Yazid ◽  
Haikal Satria ◽  
Shukor Talib ◽  
Novi Azman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Parameter Tuning ◽  
Disease Classification ◽  
Network Parameter ◽  
Artificial Neural

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

Artificial Neural Network for Cardiovascular Disease Prediction

2021 ◽  
Vol 9 (8) ◽  
pp. 2189-2193
Author(s):  
Abhay Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Disease Classification ◽  
Disease Prediction ◽  
Intelligence Gathering ◽  
Binary Model ◽  
Cardio Vascular Disease ◽  
Artificial Neural ◽  
Cardio Vascular ◽  
The Ideal

Abstract: The assurance of coronary ailment a large part of the time depends upon an eccentric mix of clinical and masochist data. Considering this multifaceted nature, there exists a ton of income among clinical specialists and experts with respect to the useful and careful assumption for coronary sickness. In this paper, we cultivate a coronary disease prediction system that can help clinical specialists in expecting coronary ailment status reliant upon the clinical data of patients. Man-made intelligence-gathering strategies are amazingly useful in the clinical field by giving accurate results and quick finishes of ailments. Thusly, these techniques save part of the ideal opportunity for the two trained professionals and patients. The neural associations can be used as classifiers to expect the assurance of Cardiovascular Heart disorder. Keywords: Cardio Vascular disease, Classification, Artificial neural network, Categorical model and Binary model

scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

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