{"id":"https://openalex.org/W2608631277","doi":"https://doi.org/10.1109/icmcs.2016.7905575","title":"Modeling of the thickness of a Submerged Tube by the Artificial Neural Network","display_name":"Modeling of the thickness of a Submerged Tube by the Artificial Neural Network","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2608631277","doi":"https://doi.org/10.1109/icmcs.2016.7905575","mag":"2608631277"},"language":"en","primary_location":{"id":"doi:10.1109/icmcs.2016.7905575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmcs.2016.7905575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029230712","display_name":"Youssef Nahraoui","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088687","display_name":"Universit\u00e9 Ibn Zohr","ror":"https://ror.org/006sgpv47","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210088687"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Y. Nahraoui","raw_affiliation_strings":["Facult\u00e9 des sciences, Universit\u00e9 Ibn Zohr, Agadir, Morocco"],"affiliations":[{"raw_affiliation_string":"Facult\u00e9 des sciences, Universit\u00e9 Ibn Zohr, Agadir, Morocco","institution_ids":["https://openalex.org/I4210088687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109237807","display_name":"El Houcein Aassif","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088687","display_name":"Universit\u00e9 Ibn Zohr","ror":"https://ror.org/006sgpv47","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210088687"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"E.H. Aassif","raw_affiliation_strings":["Facult\u00e9 des sciences, Universit\u00e9 Ibn Zohr, Agadir, Morocco"],"affiliations":[{"raw_affiliation_string":"Facult\u00e9 des sciences, Universit\u00e9 Ibn Zohr, Agadir, Morocco","institution_ids":["https://openalex.org/I4210088687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111769380","display_name":"G. Maz\u00e9","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G. Maze","raw_affiliation_strings":["Laboratoire des Ondes et Milieux Complexes, Institut Universitaire de Technologie, Le Havre, France"],"affiliations":[{"raw_affiliation_string":"Laboratoire des Ondes et Milieux Complexes, Institut Universitaire de Technologie, Le Havre, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029230712"],"corresponding_institution_ids":["https://openalex.org/I4210088687"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27806738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"89","issue":null,"first_page":"583","last_page":"587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.9567000269889832,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7944644689559937},{"id":"https://openalex.org/keywords/radius","display_name":"RADIUS","score":0.7926334142684937},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7534416913986206},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.6662720441818237},{"id":"https://openalex.org/keywords/root-mean-square","display_name":"Root mean square","score":0.5785274505615234},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5408926010131836},{"id":"https://openalex.org/keywords/tube","display_name":"Tube (container)","score":0.5345315933227539},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.42138510942459106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3880820572376251},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30224305391311646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2604256272315979},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21606367826461792},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20379525423049927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1647036373615265},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.10068026185035706}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7944644689559937},{"id":"https://openalex.org/C178635117","wikidata":"https://www.wikidata.org/wiki/Q747499","display_name":"RADIUS","level":2,"score":0.7926334142684937},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7534416913986206},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.6662720441818237},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.5785274505615234},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5408926010131836},{"id":"https://openalex.org/C2777551473","wikidata":"https://www.wikidata.org/wiki/Q2093072","display_name":"Tube (container)","level":2,"score":0.5345315933227539},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.42138510942459106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3880820572376251},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30224305391311646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2604256272315979},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21606367826461792},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20379525423049927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1647036373615265},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.10068026185035706},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmcs.2016.7905575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmcs.2016.7905575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1579405111","https://openalex.org/W1598798455","https://openalex.org/W1608292140","https://openalex.org/W1963608499","https://openalex.org/W1978509021","https://openalex.org/W2011406020","https://openalex.org/W2025165882","https://openalex.org/W2042607402","https://openalex.org/W2046574469","https://openalex.org/W2058404385","https://openalex.org/W2062186317","https://openalex.org/W2105087765","https://openalex.org/W2114175524","https://openalex.org/W2132614181","https://openalex.org/W2143070839","https://openalex.org/W2312483339","https://openalex.org/W6634821602","https://openalex.org/W6635577445","https://openalex.org/W6653198980"],"related_works":["https://openalex.org/W4247388746","https://openalex.org/W2314720829","https://openalex.org/W3178576217","https://openalex.org/W4221063543","https://openalex.org/W4285794683","https://openalex.org/W4382982879","https://openalex.org/W4385195237","https://openalex.org/W2180861079","https://openalex.org/W4387072315","https://openalex.org/W135180227"],"abstract_inverted_index":{"Several":[0,101],"theoretical":[1,155],"and":[2,37,85,128,154],"experimental":[3],"studies":[4],"show":[5],"that":[6,137,160],"the":[7,16,20,27,44,53,88,106,133,142,145,150,161,169,171,174],"characterization":[8],"of":[9,19,29,46,55,64,76,79,90,108,144,173,176],"a":[10,56,177],"tube":[11,28,57],"can":[12,164],"be":[13,165],"done":[14],"through":[15],"cut-off":[17],"frequencies":[18],"anti-symmetric":[21,65],"circumferential":[22,66],"waves":[23],"A1":[24],"propagating":[25],"around":[26],"various":[30,62,146],"radius":[31,36],"ratio":[32],"b/a":[33],"(a:":[34],"outer":[35],"b:":[38],"inner":[39],"radius).":[40],"This":[41],"work":[42],"investigates":[43],"abilities":[45],"Artificial":[47],"Neural":[48],"Networks":[49],"ANN":[50,94,153,162],"to":[51,83,86,140],"predict":[52],"thickness":[54,175],"immersed":[58],"in":[59,168,170],"water":[60],"for":[61],"cut-frequency":[63],"wave":[67],"A1.":[68],"The":[69,93,111],"useful":[70],"data":[71],"determinated":[72],"from":[73],"calculated":[74],"trajectories":[75],"natural":[77],"modes":[78],"resonances,":[80],"were":[81,132,138],"used":[82,139],"develop":[84],"test":[87],"performances":[89],"these":[91,109],"models.":[92,147],"model":[95,163],"was":[96,158],"trained":[97],"using":[98],"Levenberg-Marquardt(LM)":[99],"algorithm.":[100],"configurations":[102],"are":[103],"evaluated":[104],"during":[105],"development":[107],"networks.":[110],"Mean":[112,116,124],"Absolute":[113],"Error":[114,118,121,126],"(MAE),":[115],"Relative":[117],"(MRE),":[119],"Standard":[120],"(SE),":[122],"Root":[123],"Square":[125],"(RMSE)":[127],"Correlation":[129],"Coefficient":[130],"(R)":[131],"statistical":[134],"performance":[135],"indices,":[136],"evaluate":[141],"accuracy":[143],"Based":[148],"on":[149],"comparison":[151],"between":[152],"method,":[156],"it":[157],"found":[159],"applied":[166],"successfully":[167],"modeling":[172],"Submerged":[178],"Tube.":[179]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}