KOHONEN NEURAL NETWORK CLASSIFIER FOR VOLTAGE COLLAPSE MARGIN ESTIMATION

1997 ◽  
Vol 25 (6) ◽  
pp. 607-619 ◽  
Author(s):  
S. CHAUHAN ◽  
M. P. DAVE
Keyword(s):  
Neural Network ◽  
Voltage Collapse ◽  
Neural Network Classifier ◽  
Kohonen Neural Network

scholarly journals Classification of economic data into multiple classes by means of evolutionary methods

2013 ◽  
Vol 61 (7) ◽  
pp. 2445-2449
Author(s):  
Jiří Lýsek ◽  
Jiří Šťastný
Keyword(s):  
Neural Network ◽  
Czech Republic ◽  
Multilayer Perceptron ◽  
Consumer Behaviour ◽  
Neural Network Classifier ◽  
Economic Data ◽  
Kohonen Neural Network ◽  
Grammar Rules ◽  
Food Market

In this contribution we deal with an automatic classification of economic data into multiple classes. A classifier created by grammatical evolution is used to determine the data sample membership into one of the defined classes. The grammar rules used for classifier structure creation are presented. The performance of our classifier is compared with multilayer perceptron neural network classifier and Kohonen neural network classifier. We used a survey data of consumer behaviour in food market in Czech Republic.

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.

Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier

2019 ◽  
Vol 14 ◽  
Author(s):  
M. Madhumalini ◽  
T. Meera Devi
Keyword(s):  
Neural Network ◽  
Signal Transduction ◽  
Deep Neural Network ◽  
Neural Network Classifier ◽  
Current Signal ◽  
Automated Diagnosis ◽  
Signal Transduction Therapy ◽  
Copyright Permission ◽  
Legal Right

The article has been withdrawn on the request of the authors and the editor of the journal Current Signal Transduction Therapy. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers, if and when the article is accepted for publication.

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