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Paper Abstract and Keywords
Presentation 2018-10-30 09:25
[Invited Talk] Opportunistic Network Informatics using Deep Learning
Jiradett Kerdsri (Bank of Thai)
Abstract (in Japanese) (See Japanese page) 
(in English) Opportunistic Networks is a self-organized network where the data communication among nodes are generated by their movements utilizing store-carry- forward paradigm. The difficulty of data transmission under this extreme environment is the limited resources of each node and the dynamic topology of the network. Therefore, such opportunistic network requires high efficient routing scheme in order to cope with those restrictions. With the rise of artificial intelligence, opportunistic node can make better routing decision. This paper aims to explore the use of deep learning technique in the opportunistic environment. Consequently, we will propose initial essential information to use with training model of the intelligent nodes.
Keyword (in Japanese) (See Japanese page) 
(in English) Opportunistic Network / Artificial Intelligence / Machine Learning / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep.
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Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
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Conference Information
Committee SR RCS  
Conference Date 2018-10-30 - 2018-10-31 
Place (in Japanese) (See Japanese page) 
Place (in English) Mandarin Hotel, Bangkok, Thailand 
Topics (in Japanese) (See Japanese page) 
Topics (in English) SmartCom2018 
Paper Information
Registration To SR 
Conference Code 2018-10-SR-RCS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Opportunistic Network Informatics using Deep Learning 
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Keyword(1) Opportunistic Network  
Keyword(2) Artificial Intelligence  
Keyword(3) Machine Learning  
Keyword(4) Deep Learning  
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1st Author's Name Jiradett Kerdsri  
1st Author's Affiliation Bank of Thailand (Bank of Thai)
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Speaker Author-1 
Date Time 2018-10-30 09:25:00 
Presentation Time 25 minutes 
Registration for SR 
Paper #  
Volume (vol) vol.118 
Number (no) no.274 
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