{"id":"https://openalex.org/W3205084102","doi":"https://doi.org/10.12720/jcm.16.10.450-456","title":"Enhanced Error Reduction of Signal Power Loss During Electromagnetic Propagation: Architectural Composition and Learning Rate Selection","display_name":"Enhanced Error Reduction of Signal Power Loss During Electromagnetic Propagation: Architectural Composition and Learning Rate Selection","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3205084102","doi":"https://doi.org/10.12720/jcm.16.10.450-456","mag":"3205084102"},"language":"en","primary_location":{"id":"doi:10.12720/jcm.16.10.450-456","is_oa":true,"landing_page_url":"https://doi.org/10.12720/jcm.16.10.450-456","pdf_url":null,"source":{"id":"https://openalex.org/S184821498","display_name":"Journal of Communications","issn_l":"1796-2021","issn":["1796-2021","2374-4367"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4323867253","host_organization_name":"International Communication Association","host_organization_lineage":["https://openalex.org/P4323867253"],"host_organization_lineage_names":["International Communication Association"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.12720/jcm.16.10.450-456","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059824929","display_name":"Virginia Chika Ebhota","orcid":"https://orcid.org/0000-0003-0764-7846"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Virginia C. Ebhota","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025174207","display_name":"Viranjay M. Srivastava","orcid":"https://orcid.org/0000-0001-5231-7403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Viranjay M. Srivastava","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5253,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.62240664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"450","last_page":"456"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11665","display_name":"Animal Vocal Communication and Behavior","score":0.9067000150680542,"subfield":{"id":"https://openalex.org/subfields/1309","display_name":"Developmental Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11665","display_name":"Animal Vocal Communication and Behavior","score":0.9067000150680542,"subfield":{"id":"https://openalex.org/subfields/1309","display_name":"Developmental Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.7707023620605469},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.7138893604278564},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6800047159194946},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6469413042068481},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6158879399299622},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5807157158851624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5020883083343506},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.501605749130249},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4628441035747528},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.45363152027130127},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4508025348186493},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.44340577721595764},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.43435490131378174},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.43192556500434875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35742098093032837},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19219011068344116},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19068723917007446},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.05764302611351013}],"concepts":[{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.7707023620605469},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.7138893604278564},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6800047159194946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469413042068481},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6158879399299622},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5807157158851624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5020883083343506},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.501605749130249},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4628441035747528},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.45363152027130127},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4508025348186493},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.44340577721595764},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.43435490131378174},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.43192556500434875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35742098093032837},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19219011068344116},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19068723917007446},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.05764302611351013},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.12720/jcm.16.10.450-456","is_oa":true,"landing_page_url":"https://doi.org/10.12720/jcm.16.10.450-456","pdf_url":null,"source":{"id":"https://openalex.org/S184821498","display_name":"Journal of Communications","issn_l":"1796-2021","issn":["1796-2021","2374-4367"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4323867253","host_organization_name":"International Communication Association","host_organization_lineage":["https://openalex.org/P4323867253"],"host_organization_lineage_names":["International Communication Association"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Communications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.12720/jcm.16.10.450-456","is_oa":true,"landing_page_url":"https://doi.org/10.12720/jcm.16.10.450-456","pdf_url":null,"source":{"id":"https://openalex.org/S184821498","display_name":"Journal of Communications","issn_l":"1796-2021","issn":["1796-2021","2374-4367"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4323867253","host_organization_name":"International Communication Association","host_organization_lineage":["https://openalex.org/P4323867253"],"host_organization_lineage_names":["International Communication Association"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Communications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W847255729","https://openalex.org/W1513524247","https://openalex.org/W1806891645","https://openalex.org/W1990157560","https://openalex.org/W2017358747","https://openalex.org/W2051805298","https://openalex.org/W2058536250","https://openalex.org/W2083993351","https://openalex.org/W2096032932","https://openalex.org/W2107093743","https://openalex.org/W2114367267","https://openalex.org/W2137983211","https://openalex.org/W2147016173","https://openalex.org/W2155837945","https://openalex.org/W2160276773","https://openalex.org/W2161649813","https://openalex.org/W2336110470","https://openalex.org/W2491272799","https://openalex.org/W2511016831","https://openalex.org/W2583739345","https://openalex.org/W2766736793","https://openalex.org/W2798205935","https://openalex.org/W2807731816","https://openalex.org/W2808860853","https://openalex.org/W2897361856","https://openalex.org/W2898590421","https://openalex.org/W2911546748","https://openalex.org/W2955707520","https://openalex.org/W2964395550","https://openalex.org/W2966937130","https://openalex.org/W3114558865","https://openalex.org/W3123328636","https://openalex.org/W3126423379","https://openalex.org/W3164640984"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2766585573","https://openalex.org/W4387490624","https://openalex.org/W4286643620"],"abstract_inverted_index":{"This":[0],"research":[1],"work":[2],"analyses":[3],"the":[4,7,20,23,54,122,151,168,174],"effect":[5,21],"of":[6,10,22,29,45,53,78,90,108,113,153,155,171,191],"architectural":[8],"composition":[9],"Multi-Layer":[11],"Perceptron":[12],"(MLP)":[13],"Artificial":[14],"Neural":[15],"Network":[16],"(ANN)":[17],"combined":[18],"with":[19,87,105,150,178],"learning":[24,116,169,192],"rate":[25,170,193],"for":[26,63,75,121,134,157,162,195],"effective":[27],"prediction":[28,138],"signal":[30,35,135],"power":[31,136],"loss":[32,137],"during":[33],"electromagnetic":[34],"propagation.":[36],"A":[37],"single":[38,80,197],"hidden":[39,43,72,101,146,164,201],"layer":[40,73,81,198],"and":[41,92,110,160,183,199],"two":[42,100,145,163,200],"layers":[44,102,147,165,202],"MLP":[46,65,130,204],"ANN":[47],"have":[48,67],"been":[49,68],"considered.":[50],"Different":[51,115],"configurations":[52],"neural":[55,131],"network":[56,83,123,132,142,149,159],"architecture":[57],"ranging":[58],"from":[59],"4":[60],"to":[61,144,188],"100":[62],"both":[64,196],"networks":[66],"analyzed.":[69],"The":[70,125],"required":[71],"neurons":[74,86],"optimal":[76],"training":[77,133,176,185],"a":[79],"multi-layer":[82,103],"were":[84,118],"40":[85],"0.99670":[88,156],"coefficient":[89,107,152],"correlation":[91,109,154],"1.28020":[93],"standard":[94,111],"deviations,":[95],"while":[96],"[68":[97],"72]":[98],"trained":[99],"perceptron":[104,141,148,203],"0.98880":[106],"deviation":[112],"1.42820.":[114],"rates":[117],"also":[119],"adopted":[120],"training.":[124],"results":[126],"further":[127],"validate":[128],"better":[129],"using":[139],"single-layer":[140,158],"compared":[143,187],"0.9888":[161],"network.":[166],"Furthermore,":[167],"0.003":[172],"shows":[173],"best":[175],"capability":[177],"lower":[179],"mean":[180],"squared":[181],"error":[182],"higher":[184],"regression":[186],"other":[189],"values":[190],"used":[194],"networks.":[205]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2021-10-25T00:00:00"}