scholarly journals PREDICTION OF HEART DISEASE USING K-MEANS and ARTIFICIAL NEURAL NETWORK as HYBRID APPROACH to IMPROVE ACCURACY

2017 ◽  
Vol 9 (4) ◽  
pp. 3081-3085 ◽  
Author(s):  
Amita Malav ◽  
Kalyani Kadam ◽  
Pooja Kamat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Hybrid Approach ◽  
Improve Accuracy ◽  
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

scholarly journals A Hybrid Approach to Gender Classification using Speech Signal

2019 ◽  
pp. 17-24
Author(s):  
M. Yasin Pir ◽  
Mohamad Idris Wani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Speech Signal ◽  
Hybrid Approach ◽  
Gender Classification ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Feed Forward Network ◽  
Artificial Neural

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

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