Effect of data compression of ERP sign preprocessed by FWT algorithm upon a neural network classifier

1990 ◽  
Vol 20 (4) ◽  
pp. 63-71 ◽  
Author(s):  
S. DasGupta ◽  
M. Hohenberger ◽  
Len Trejo ◽  
T. Kaylani
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Neural Network Classifier

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Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier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The article has been withdrawn on the request of the authors and the editor of the journal Current Signal Transduction Therapy.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

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

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