A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things

2020 ◽  
Author(s):  
Mehdi Hosseinzadeh ◽  
Omed Hassan Ahmed ◽  
Marwan Yassin Ghafour ◽  
Fatemeh Safara ◽  
Hawkar kamaran hama ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Multilayer Perceptron ◽  
Thyroid Disease ◽  
Learning Algorithm ◽  
Disease Diagnosis ◽  
The Internet ◽  
Internet Of Medical Things ◽  
Thyroid Disease Diagnosis

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

scholarly journals Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0182070 ◽  
Author(s):  
LiMin Wang ◽  
FangYuan Cao ◽  
ShuangCheng Wang ◽  
MingHui Sun ◽  
LiYan Dong
Keyword(s):  
Thyroid Disease ◽  
Disease Diagnosis ◽  
Clinical Variables ◽  
Thyroid Disease Diagnosis

scholarly journals Use of a Multilayer Perceptron to Automate Terrain Assessment for the Needs of the Armed Forces

2018 ◽  
Vol 7 (11) ◽  
pp. 430 ◽  
Author(s):  
Krzysztof Pokonieczny
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Learning Algorithm ◽  
Armed Forces ◽  
Shuttle Radar Topography Mission ◽  
Terrain Classification ◽  
The Neural Network ◽  
Data Configuration ◽  
Trained Neural Network

The classification of terrain in terms of passability plays a significant role in the process of military terrain assessment. It involves classifying selected terrain to specific classes (GO, SLOW-GO, NO-GO). In this article, the problem of terrain classification to the respective category of passability was solved by applying artificial neural networks (multilayer perceptron) to generate a continuous Index of Passability (IOP). The neural networks defined this factor for primary fields in two sizes (1000 × 1000 m and 100 × 100 m) based on the land cover elements obtained from Vector Smart Map (VMap) Level 2 and Shuttle Radar Topography Mission (SRTM). The work used a feedforward neural network consisting of three layers. The paper presents a comprehensive analysis of the reliability of the neural network parameters, taking into account the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions.

An adaptive learning algorithm for a wavelet neural network

2005 ◽  
Vol 22 (5) ◽  
pp. 235-240 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Nataliya Lamonova ◽  
Iryna Pliss ◽  
Olena Vynokurova
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Wavelet Neural Network
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