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dcterms:bibliographicCitation <http://dblp.uni-trier.de/rec/bibtex/journals/computers/MarhraouiABALBA22>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Hamdi_Amroun>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Margarita_Anastassova>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Mehdi_Ammi>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Mehdi_Boukallel>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/St%E2%88%9A%C2%A9phane_Bouilland>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Sylvie_Lamy>
dc:creator <https://dblp.l3s.de/d2r/resource/authors/Youness_El_Marhraoui>
foaf:homepage <http://dx.doi.org/doi.org%2F10.3390%2Fcomputers11050058>
foaf:homepage <https://doi.org/10.3390/computers11050058>
dc:identifier DBLP journals/computers/MarhraouiABALBA22 (xsd:string)
dc:identifier DOI doi.org%2F10.3390%2Fcomputers11050058 (xsd:string)
dcterms:issued 2022 (xsd:gYear)
swrc:journal <https://dblp.l3s.de/d2r/resource/journals/computers>
rdfs:label Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures. (xsd:string)
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Hamdi_Amroun>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Margarita_Anastassova>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Mehdi_Ammi>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Mehdi_Boukallel>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/St%E2%88%9A%C2%A9phane_Bouilland>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Sylvie_Lamy>
foaf:maker <https://dblp.l3s.de/d2r/resource/authors/Youness_El_Marhraoui>
swrc:number 5 (xsd:string)
swrc:pages 58 (xsd:string)
owl:sameAs <http://bibsonomy.org/uri/bibtexkey/journals/computers/MarhraouiABALBA22/dblp>
owl:sameAs <http://dblp.rkbexplorer.com/id/journals/computers/MarhraouiABALBA22>
rdfs:seeAlso <http://dblp.uni-trier.de/db/journals/computers/computers11.html#MarhraouiABALBA22>
rdfs:seeAlso <https://doi.org/10.3390/computers11050058>
dc:title Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures. (xsd:string)
dc:type <http://purl.org/dc/dcmitype/Text>
rdf:type swrc:Article
rdf:type foaf:Document
swrc:volume 11 (xsd:string)