{"id":"https://openalex.org/W3117440741","doi":"https://doi.org/10.1109/tencon50793.2020.9293752","title":"MELM-GRBFNN: A modified Extreme Learning Machine trained Gaussian Radial Basis Function Neural Network model for estimating blocking probability of OBS Network","display_name":"MELM-GRBFNN: A modified Extreme Learning Machine trained Gaussian Radial Basis Function Neural Network model for estimating blocking probability of OBS Network","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3117440741","doi":"https://doi.org/10.1109/tencon50793.2020.9293752","mag":"3117440741"},"language":"en","primary_location":{"id":"doi:10.1109/tencon50793.2020.9293752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon50793.2020.9293752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","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/A5028763105","display_name":"Srija Chakraborty","orcid":"https://orcid.org/0000-0002-5701-760X"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Srija Chakraborty","raw_affiliation_strings":["Department of C.S.E., National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Department of C.S.E., National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062490001","display_name":"Ashok Kumar Turuk","orcid":"https://orcid.org/0000-0002-1087-2027"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashok Kumar Turuk","raw_affiliation_strings":["Department of C.S.E., National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Department of C.S.E., National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062056080","display_name":"Bibhudatta Sahoo","orcid":"https://orcid.org/0000-0001-8273-9850"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bibhudatta Sahoo","raw_affiliation_strings":["Department of C.S.E., National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Department of C.S.E., National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028763105"],"corresponding_institution_ids":["https://openalex.org/I16292982"],"apc_list":null,"apc_paid":null,"fwci":0.3082,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.58554297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"478","last_page":"483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12676","display_name":"Machine Learning and ELM","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.698966383934021},{"id":"https://openalex.org/keywords/blocking","display_name":"Blocking (statistics)","score":0.6919004321098328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6642932295799255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5271682143211365},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.4261088967323303},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.41000261902809143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38824015855789185},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10258933901786804}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.698966383934021},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.6919004321098328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6642932295799255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5271682143211365},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.4261088967323303},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41000261902809143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38824015855789185},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10258933901786804},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon50793.2020.9293752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon50793.2020.9293752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1493178709","https://openalex.org/W1963616278","https://openalex.org/W1969847908","https://openalex.org/W1987912254","https://openalex.org/W1988115241","https://openalex.org/W1997535528","https://openalex.org/W2015076781","https://openalex.org/W2020699959","https://openalex.org/W2031586952","https://openalex.org/W2052198784","https://openalex.org/W2114354594","https://openalex.org/W2114531268","https://openalex.org/W2123031198","https://openalex.org/W2134126133","https://openalex.org/W2137983211","https://openalex.org/W2141695047","https://openalex.org/W2164278908","https://openalex.org/W2166116275","https://openalex.org/W2301541953","https://openalex.org/W2334875367","https://openalex.org/W2500228660","https://openalex.org/W2739073682","https://openalex.org/W3106359998","https://openalex.org/W3146803896","https://openalex.org/W4292363360","https://openalex.org/W6629561130","https://openalex.org/W7055364642"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W2583394830","https://openalex.org/W2783385843","https://openalex.org/W4306674287","https://openalex.org/W2944325459","https://openalex.org/W4224009465","https://openalex.org/W2623111183","https://openalex.org/W1629725936","https://openalex.org/W2979979539","https://openalex.org/W2539163683"],"abstract_inverted_index":{"Neural":[0],"networks":[1],"are":[2],"extensively":[3],"used":[4,66],"for":[5,44,67],"determining":[6],"different":[7],"characteristics":[8],"of":[9,17,31,88,141],"optical":[10,18],"burst":[11,19,23,26,32,71,74,113,125],"switching":[12,20],"networks.":[13],"The":[14],"main":[15],"disadvantage":[16],"network":[21,37,62,131,143],"is":[22,65,82,92,102,108],"drop":[24],"and":[25,69,95,119],"contention,":[27],"which":[28],"occurs":[29],"because":[30],"getting":[33],"blocked.":[34],"Using":[35],"neural":[36,61],"approaches,":[38],"blocking":[39,75,114],"probability":[40,115],"can":[41],"be":[42],"pre-determined":[43],"the":[45,78,85,98,112,124,139,142],"upcoming":[46],"traffic.":[47],"In":[48],"this":[49],"paper,":[50],"Log-incremental":[51],"modified":[52],"extreme":[53],"learning":[54],"machine":[55],"trained":[56],"generalized":[57],"radial":[58],"basis":[59],"function":[60],"(MELM-GRBFNN)":[63],"model":[64,91,144],"training":[68],"predicting":[70,111],"contention":[72],"or":[73],"probability.":[76],"From":[77],"obtained":[79],"results,":[80],"it":[81,128],"inferred":[83],"that":[84,104],"prediction":[86],"accuracy":[87,118],"our":[89,105],"proposed":[90,106],"more":[93],"accurate":[94],"faster":[96],"than":[97],"contemporary":[99],"approaches.":[100],"It":[101],"observed":[103],"method":[107],"competent":[109],"in":[110,123],"with":[116],"higher":[117],"indicates":[120],"a":[121,135],"reduction":[122],"loss.":[126],"Thus,":[127],"will":[129],"help":[130],"designers":[132],"to":[133],"have":[134],"preliminary":[136],"idea":[137],"about":[138],"performance":[140],"under":[145],"specific":[146],"configurations.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
