scholarly journals A failure diagnosis system based on a neural network classifier for the Space Shuttle main engine

1990 ◽  
Author(s):  
A. Duyar ◽  
W. Merrill
Keyword(s):  
Neural Network ◽  
Space Shuttle ◽  
Failure Diagnosis ◽  
Neural Network Classifier ◽  
Diagnosis System ◽  
Main Engine

scholarly journals TB Diagnosis System using Genetic Particle Swarm Optimization Based Neural Network Classifier

2019 ◽  
Vol 8 (10) ◽  
pp. 3833-3839
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Rough Set ◽  
Rough Set Theory ◽  
Particle Swarm ◽  
Previous Method ◽  
Neural Network Classifier ◽  
Selection Scheme ◽  
Diagnosis System

Classification of medical image is an important task in the diagnosis of any disease. It even helps doctors in their diagnosis decisions. Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. Recently, several techniques are applied to diagnosis the TB diseases. Unfortunately, diagnosing TB is still a major challenge. Therefore, in this paper an efficient Tuberculosis diagnosis system is proposed using Multi Kernel Fuzzy C Means Rough Set (MKFCMRS) based feature selection and optimal neural network classifier. Our proposed method comprised of four stages namely, feature extraction, feature selection, classification and Region identification. Initially the TB images are extracted from the given input database and that each of the input images are given to feature extraction process, in which statistical, structural and gray level dependent features are extracted. After that, the feature selection scheme is carried out through multi-kernel FCM based rough-set theory. Then, selected features are given to optimal neural network classifier to optimize the weight values of the neural network. In this work proposed classifier is Particle Genetic Swarm Neural Network classifier (GPSO-NN) which Integrates the characteristics of both genetic and particle swarm methods. The proposed system is implemented in the working platform of MATLAB. Compared to previous method our proposed technique is improved in terms of accuracy, sensitivity and specificity

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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