Evaluation of a training method and of various rejection criteria for a neural network classifier used for off-line signature verification

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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A novel parallel neural network classifier for automatic vehicle license plate recognition

Artificial Intelligence and Industrial Application ◽

10.2495/aiia140151 ◽

2015 ◽

Author(s):

X. Han ◽

H. M. Xie ◽

G. Q. Zhang

Keyword(s):

Neural Network ◽

License Plate ◽

License Plate Recognition ◽

Neural Network Classifier ◽

Vehicle License Plate Recognition

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Squeak and rattle noise classification using radial basis function neural networks

Noise Control Engineering Journal ◽

10.3397/1/376824 ◽

2020 ◽

Vol 68 (4) ◽

pp. 283-293

Author(s):

Oleksandr Pogorilyi ◽

Mohammad Fard ◽

John Davy ◽

Mechanical and Automotive Engineering, School ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Accuracy ◽

Training Method ◽

Vehicle Interior ◽

Trained Classifier ◽

Different Types ◽

Noise Classification ◽

Automatic Tool ◽

Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier

Current Signal Transduction Therapy ◽

10.2174/1574362414666190906112910 ◽

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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