scholarly journals A Biomedical Voice Measurement Diagnosis of Parkinson’s Disease through the utilization of Artificial Neural Network

2021 ◽  
Vol 2071 (1) ◽  
pp. 012038
Author(s):  
N F M Ayap ◽  
B A Eugenio ◽  
J I V Hinolan ◽  
J C V Puno ◽  
R G Baldovino ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Medical Cost ◽  
Early Onset ◽  
External Factors ◽  
Medical Field ◽  
Human Voice ◽  
Slow Progression ◽  
Detection And Diagnosis ◽  
Classification Prediction

Abstract In 2016 alone, there were a total of 120,000 cases of PD diagnosed and documented, however, experts believe that there are still loss cases which remain to be undiagnosed because of external factors such as medical cost and accuracy of diagnosis. The detection and diagnosis of PD on its early onset has become a problem in the medical field because of the slow progression of its symptoms. With the advent of technology, different diagnosing methods are being introduced and explored - one of which is through the concept of Neural Network. This paper highlights the human voice of patients using Multilayer Perceptron Neural Network (MLP) to accurately diagnose individuals who are diagnosed with PD. It was seen that the MLP classification prediction has achieved an average of 91.5% accuracy.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

scholarly journals Human Voice Recognition by Using Hebb Artificial Neural Network Method

2018 ◽  
Vol 19 (1) ◽  
pp. 16
Author(s):  
K Krisna G ◽  
I Gusti Agung Widagda ◽  
Komang Ngurah Suarbawa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Rate ◽  
Training Data ◽  
Human Voice ◽  
Noise Data ◽  
Artificial Neural ◽  
Testing Algorithm ◽  
Voice Data ◽  
Sound Data

It has been created a program to recognize human voice by using artificial neural network (ANN). The ANN method used is Hebb. Hebb was chosen because it is the simplest ANN so the training and testing process is faster than other methods. Designing the program is started by designing Hebb’s architecture and design of GUI (Graphical User Interface) using Matlab R2009a. The design of Hebb's architecture consists of 4500 inputs and 3 outputs. The GUI design of the program consists of three main sections: recording panels to record sample sounds, training panels to determine the weighted value and bias of the training results according to the Hebb training algorithm, and the testing panel to test the test sounds according to the Hebb testing algorithm. After program design, proceed with the testing of the program. Testing of the program starts with the sound recording of samples from 8 different people using the record panel. Each person has 1 voice sample for training data. Then proceed with the Hebb training process using the training panel, weight and bias value displayed on the training panel. After the weight and bias values ??are obtained, proceed with the Hebb testing process using 16 test sound data consisting of 8 sound data equal to training data and 8 noise data. From the testing program process obtained a result of 100% for the level of recognition of the same voice data with training data and for noise data has a recognition rate of 87.5%.

scholarly journals Slantlet transform used for faults diagnosis in robot arm

2022 ◽  
Vol 25 (1) ◽  
pp. 281
Author(s):  
Muhamad Azhar Abdilatef Alobaidy ◽  
Jassim Mohammed Abdul-Jabbar ◽  
Saad Zaghlul Al-khayyt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Detection And Diagnosis ◽  
Discrete Wavelet ◽  
Robot Arm ◽  
Multi Layer Perceptron ◽  
Artificial Neural ◽  
Detection And Diagnosis

<p class="JESTECAbstract">The <span>robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time <br /> (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same </span>purpose.</p>

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