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http://hdl.handle.net/1942/35454
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DC Field | Value | Language |
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dc.contributor.author | Kaja, S | - |
dc.contributor.author | Shakshuki, EM | - |
dc.contributor.author | YASAR, Ansar | - |
dc.contributor.editor | Shakshuki, E | - |
dc.contributor.editor | YASAR, Ansar | - |
dc.date.accessioned | 2021-09-29T09:02:58Z | - |
dc.date.available | 2021-09-29T09:02:58Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2021-09-17T13:16:27Z | - |
dc.identifier.citation | 12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, ELSEVIER SCIENCE BV, p. 461 -468 | - |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | http://hdl.handle.net/1942/35454 | - |
dc.description.abstract | Routing optimization using machine learning has been receiving a lot of attention recently. Additionally, cloud computing is evolving exponentially in processing power and memory units. This paper proposes a routing optimization approach for a Cloud ACKnowledgement Scheme using machine learning techniques. Our proposed approach is based on synthetic generated data for respective node values in a network. Moreover, it involves a variant of Recurrent Neural Network called Long Short-Term Memory (LSTM). The machine learning model is developed using LSTM through a sliding-window technique. The results achieved are very encouraging. They show that the cloud can mostly predict whether the forthcoming transmission of a certain node in the network will be a success. (C) 2021 The Authors. Published by Elsevier B. V. | - |
dc.description.sponsorship | The authors would like to acknowledge the financial support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the research support provided by IMOB Hasselt University Belgium. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.ispartofseries | Procedia CompuProcedia Computer Scienceter Science | - |
dc.rights | 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer -review under responsibility of the Conference Program Chair | - |
dc.subject.other | Machine Learning | - |
dc.subject.other | Long Short-Term Memory | - |
dc.subject.other | Acknowledgement Scheme | - |
dc.subject.other | Acknowledgement Packets | - |
dc.subject.other | Routing | - |
dc.title | Long Short-Term Memory Approach for Routing Optimization in Cloud ACKnowledgement Scheme for Node Network | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.conferencedate | MAR 23-26, 2021 | - |
local.bibliographicCitation.conferencename | 12th International Conference on Ambient Systems, Networks and Technologies (ANT) / 4th International Conference on Emerging Data and Industry 4.0 (EDI40) | - |
local.bibliographicCitation.conferenceplace | Warsaw, POLAND | - |
dc.identifier.epage | 468 | - |
dc.identifier.spage | 461 | - |
local.bibliographicCitation.jcat | C1 | - |
local.publisher.place | SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
local.relation.ispartofseriesnr | 184 | - |
dc.identifier.doi | 10.1016/j.procs.2021.03.058 | - |
dc.identifier.isi | 000672800000057 | - |
dc.identifier.eissn | - | |
local.provider.type | Web of Science | - |
local.bibliographicCitation.btitle | 12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2022 | - |
item.contributor | Kaja, S | - |
item.contributor | Shakshuki, EM | - |
item.contributor | YASAR, Ansar | - |
item.contributor | Shakshuki, E | - |
item.accessRights | Open Access | - |
item.fullcitation | Kaja, S; Shakshuki, EM & YASAR, Ansar (2021) Long Short-Term Memory Approach for Routing Optimization in Cloud ACKnowledgement Scheme for Node Network. In: 12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, ELSEVIER SCIENCE BV, p. 461 -468. | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 1877-0509 | - |
Appears in Collections: | Research publications |
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1-s2.0-S187705092100689X-main.pdf | Published version | 688.42 kB | Adobe PDF | View/Open |
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