Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier

2017 ◽  
Vol 46 (2) ◽  
pp. 681-704 ◽  
Author(s):  
Gholam Ali Montazer ◽  
Davar Giveki
Keyword(s):  
Neural Network ◽  
Neural Network Classifier ◽  
Scene Classification

scholarly journals Head-Mounted Mobility Aid for Low Vision Using Scene Classification Techniques

1998 ◽  
Vol 3 (4) ◽  
pp. 1-10 ◽  
Author(s):  
M. R. Everingham ◽  
B. T. Thomas ◽  
T Troscianko
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Low Vision ◽  
Visual Impairments ◽  
Neural Network Classifier ◽  
Scene Classification ◽  
Head Mounted Display ◽  
Mobility Aid ◽  
Scene Content ◽  
Advanced Computer

This paper describes a new mobility aid for people with severe visual impairments which combines technology from the field of virtual reality with advanced computer vision techniques. A neural-network classifier is used to identify objects in images from a head mounted camera so that scene content specifically important for mobility may be made more visible. Enhanced images are displayed to the user on a head mounted display using a high saturation colour scheme where each type of object has a different colour, resulting in images which are highly visible and easy to interpret. The object classifier achieves a level of accuracy over 90%. Results from a pilot study conducted using people with a range of visual impairments are presented in which performance on a difficult mobility-related task was improved by over 100% using the system.

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.

A Framework for Extractive Text Summarization based on Deep Learning Modified Neural Network Classifier

2020 ◽  
Author(s):  
BalaAnand Muthu ◽  
Sivaparthipan CB ◽  
Priyan Malarvizhi Kumar ◽  
Seifedine Nimer Kadry ◽  
Ching-Hsien Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Summarization ◽  
Neural Network Classifier
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