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Neutrosophy for physiological data compression: in particular by neural nets using deep learning
ISSN: 2690 - 6805
Publisher: author
Neutrosophy for physiological data compression: in particular by neural nets using deep learning
Indexed in
Mathematics and Statistics
ARTICLE-FACTOR
1.3
Article Basics Score: 3
Article Transparency Score: 3
Article Operation Score: 2
Article Articles Score: 2
Article Accessibility Score: 2
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International Category Code (ICC):
ICC-1102
Publisher: International Journal Of Neutrosophic Science Broumi Said
International Journal Address (IAA):
IAA.ZONE/269049756805
eISSN
:
2690 - 6805
VALID
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Abstract
We would like to show the small distance in neutropsophy applications in sciences and humanities, has both finally consider as a terminal user a human. The pace of data production continues to grow, leading to increased needs for efficient storage and transmission. Indeed, the consumption of this information is preferably made on mobile terminals using connections invoiced to the user and having only reduced storage capacities. Deep learning neural networks have recently exceeded the compression rates of algorithmic techniques for text. We believe that they can also significantly challenge classical methods for both audio and visual data (images and videos). To obtain the best physiological compression, i.e. the highest compression ratio because it comes closest to the specificity of human perception, we propose using a neutrosophical representation of the information for the entire compression-decompression cycle. Such a representati...
