About: Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

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About: Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities
Creator	Liviu, Sorin Opritoiu, Flavius Vladutiu, Mircea
topic	covid:6f496cfbf3087e3a2fd0ab72d00315ec0bf0ac70#this
Source	PMC
abstract	This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.
has issue date	2020-05-02(xsd:dateTime)
bibo:doi	10.1007/978-3-030-48791-1_4
has license	no-cc
sha1sum (hex)	6f496cfbf3087e3a2fd0ab72d00315ec0bf0ac70
schema:url	https://doi.org/10.1007/978-3-030-48791-1_4
resource representing a document's title	Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities
has PubMed Central identifier	PMC7251932
schema:publication	Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
resource representing a document's body	covid:6f496cfbf3087e3a2fd0ab72d00315ec0bf0ac70#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/6f496cfbf3087e3a2fd0ab72d00315ec0bf0ac70
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