{"id":"https://openalex.org/W4413855422","doi":"https://doi.org/10.1142/s0218488525500254","title":"Dynamically Stabilized Recurrent Neural Network Optimized with Binary Emperor Penguin Optimization Algorithm Fostered Sentiment Classification and Information Retrieval","display_name":"Dynamically Stabilized Recurrent Neural Network Optimized with Binary Emperor Penguin Optimization Algorithm Fostered Sentiment Classification and Information Retrieval","publication_year":2025,"publication_date":"2025-08-30","ids":{"openalex":"https://openalex.org/W4413855422","doi":"https://doi.org/10.1142/s0218488525500254"},"language":"en","primary_location":{"id":"doi:10.1142/s0218488525500254","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218488525500254","pdf_url":null,"source":{"id":"https://openalex.org/S69518169","display_name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","issn_l":"0218-4885","issn":["0218-4885","1793-6411"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","raw_type":"journal-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/A5015557641","display_name":"R. Jothilakshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I154935518","display_name":"Royal College of Emergency Medicine","ror":"https://ror.org/04zkbxs23","country_code":"GB","type":"education","lineage":["https://openalex.org/I154935518"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"R. Jothilakshmi","raw_affiliation_strings":["Department of Information Technology, RMD Engineering College, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, RMD Engineering College, India","institution_ids":["https://openalex.org/I154935518"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049378099","display_name":"P. Chinniah","orcid":null},"institutions":[{"id":"https://openalex.org/I197979904","display_name":"Saint Joseph's College","ror":"https://ror.org/01k2x9g80","country_code":"US","type":"education","lineage":["https://openalex.org/I197979904"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"P. Chinniah","raw_affiliation_strings":["Department of Electronics and Communication Engineering, ST. Joseph College of Engineering, Sriperumbudur-602117, Tamil Nadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, ST. Joseph College of Engineering, Sriperumbudur-602117, Tamil Nadu, India","institution_ids":["https://openalex.org/I197979904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111873557","display_name":"Rajesh Sharma","orcid":null},"institutions":[{"id":"https://openalex.org/I53465836","display_name":"Bangalore University","ror":"https://ror.org/050j2vm64","country_code":"IN","type":"education","lineage":["https://openalex.org/I53465836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. Rajesh Sharma","raw_affiliation_strings":["Department of Computer Science and Engineering, School of Engineering, Allianc University, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, School of Engineering, Allianc University, Bangalore, India","institution_ids":["https://openalex.org/I53465836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003495033","display_name":"T. R. Vijaya Lakshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I4401200368","display_name":"Mahatma Gandhi Institute of Technology","ror":"https://ror.org/011skn125","country_code":null,"type":"education","lineage":["https://openalex.org/I4401200368"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"T. R. Vijaya Lakshmi","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, India","institution_ids":["https://openalex.org/I4401200368"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":25.1279,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.99495603,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"33","issue":"06","first_page":"761","last_page":"786"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9930999875068665,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9930999875068665,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9807999730110168,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5810765027999878},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5780215859413147},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5205334424972534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5108025074005127},{"id":"https://openalex.org/keywords/emperor","display_name":"Emperor","score":0.4188601076602936},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3762994408607483},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3431267738342285},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3373051881790161},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3287232518196106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13413399457931519},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.10092458128929138}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5810765027999878},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5780215859413147},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5205334424972534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5108025074005127},{"id":"https://openalex.org/C2776501734","wikidata":"https://www.wikidata.org/wiki/Q527496","display_name":"Emperor","level":2,"score":0.4188601076602936},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3762994408607483},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3431267738342285},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3373051881790161},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3287232518196106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13413399457931519},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.10092458128929138},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C195244886","wikidata":"https://www.wikidata.org/wiki/Q41493","display_name":"Ancient history","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218488525500254","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218488525500254","pdf_url":null,"source":{"id":"https://openalex.org/S69518169","display_name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","issn_l":"0218-4885","issn":["0218-4885","1793-6411"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W3006617969","https://openalex.org/W3011184914","https://openalex.org/W3081987387","https://openalex.org/W3083435909","https://openalex.org/W3096616939","https://openalex.org/W3101155479","https://openalex.org/W3115795817","https://openalex.org/W3117864197","https://openalex.org/W3119665018","https://openalex.org/W3124640783","https://openalex.org/W3127044632","https://openalex.org/W3127056209","https://openalex.org/W3127541398","https://openalex.org/W3132746979","https://openalex.org/W3148172507","https://openalex.org/W3155127099","https://openalex.org/W3158755204","https://openalex.org/W3159597990","https://openalex.org/W3160518394","https://openalex.org/W3164947024","https://openalex.org/W3173452903","https://openalex.org/W3175234726","https://openalex.org/W3176923149","https://openalex.org/W3193514658","https://openalex.org/W3198531509","https://openalex.org/W3208556199","https://openalex.org/W3210828003","https://openalex.org/W4200536610","https://openalex.org/W4220726338","https://openalex.org/W4220864080","https://openalex.org/W4223480525","https://openalex.org/W4242735564","https://openalex.org/W4286203318","https://openalex.org/W4307298796","https://openalex.org/W4365145812","https://openalex.org/W4391997951","https://openalex.org/W4400057350"],"related_works":["https://openalex.org/W2349267267","https://openalex.org/W2476374733","https://openalex.org/W2392853075","https://openalex.org/W2750731007","https://openalex.org/W2389889657","https://openalex.org/W2352497308","https://openalex.org/W2990241349","https://openalex.org/W2350388836","https://openalex.org/W647728696","https://openalex.org/W2384099095"],"abstract_inverted_index":{"Due":[0],"to":[1,26,95,111,154,182,190,208,230,235,249,269],"the":[2,43,97,113,150,155,159,164,192,209,213,232,251,256,271,283,285,308],"widespread":[3],"increase":[4],"of":[5,45,58,101,307,361,379],"online":[6,380],"reviews,":[7],"sentiment":[8,40,76,102,122,144,238,257,349,359,377],"classification":[9,77],"is":[10,24,48,132,152,180,247,267,277,303],"now":[11],"a":[12,55,69,367],"fascinating":[13],"study":[14],"in":[15,31,121,279],"both":[16],"academic":[17],"and":[18,53,67,75,99,109,119,140,174,184,218,351,366,372],"industrial":[19],"research.":[20],"Annotated":[21],"training":[22],"data":[23,131,151,179,364],"difficult":[25],"obtain,":[27],"so":[28],"reviews":[29,138,170],"help":[30],"many":[32],"domains.":[33],"Numerous":[34],"methods":[35,229],"have":[36],"been":[37],"created":[38],"for":[39,72,143,348,376],"classification,":[41],"but":[42],"process":[44,166],"retrieving":[46],"information":[47,73,147,352],"not":[49,224],"exact,":[50],"less":[51],"effective,":[52],"has":[54],"slower":[56],"rate":[57,334],"convergence.":[59],"To":[60,281],"overcome":[61],"these":[62],"problems,":[63],"this":[64],"research":[65],"proposes":[66],"evaluates":[68],"novel":[70],"technique":[71],"retrieval":[74],"called":[78],"Dynamically":[79],"Stabilized":[80],"Recurrent":[81],"Neural":[82],"Network":[83,264],"Optimized":[84],"with":[85,146,337],"Binary":[86,241],"Emperor":[87,242],"Penguin":[88,243],"Optimization":[89,244],"Algorithm":[90,245],"(SCIR-DSRNN-BEPOA).":[91],"This":[92],"approach":[93,310],"seeks":[94],"enhance":[96],"accuracy":[98],"efficiency":[100],"analysis":[103,350,360,378],"by":[104,171],"integrating":[105],"advanced":[106],"optimization":[107,227,346],"techniques":[108],"also":[110],"address":[112],"challenges":[114],"such":[115,194],"as":[116,195],"slow":[117],"convergence":[118],"inefficiency":[120],"analysis,":[123],"ultimately":[124],"achieving":[125],"superior":[126],"performance":[127,286,306],"metrics.":[128],"The":[129,203,274,305],"input":[130],"gathered":[133],"through":[134],"Amazon":[135],"unlocked":[136],"mobile":[137],"database":[139],"telecom":[141],"tweets":[142],"categorization":[145],"retrieval.":[148],"Afterward,":[149],"fed":[153,181],"preprocessing.":[156],"By":[157],"using":[158],"Unscented":[160],"Trainable":[161],"Kalman":[162],"Filter,":[163],"preprocessing":[165],"removes":[167],"noise":[168],"from":[169],"fixing":[172],"typos":[173],"removing":[175],"ellipsis.":[176],"These":[177],"pre-processed":[178],"Adaptive":[183],"Concise":[185],"Empirical":[186],"Wavelet":[187],"Transform":[188],"(ACEWT)":[189],"extract":[191,270],"features,":[193],"elongated":[196],"words,":[197],"punctuation,":[198],"hash":[199],"tag,":[200],"numerical":[201],"values.":[202],"extracted":[204],"features":[205],"are":[206],"given":[207],"DSRNN":[210,222,252],"which":[211],"classifies":[212],"sentiment,":[214,284],"like":[215,288],"neutral,":[216],"positive":[217],"negative.":[219],"In":[220],"general,":[221],"does":[223],"show":[225],"any":[226],"adaption":[228],"determine":[231],"optimum":[233],"parameter":[234],"offer":[236],"accurate":[237],"classification.":[239],"Therefore,":[240],"(BEPOA)":[246],"proposed":[248,275],"optimize":[250],"classifier":[253],"that":[254],"categorizes":[255],"precisely.":[258],"Attention":[259],"Enhanced":[260],"Temporal":[261],"Graph":[262],"Convolutional":[263],"(AETGCN)":[265],"method":[266],"used":[268],"necessary":[272],"review.":[273],"SCIR-DSRNN-BEPOA":[276,309],"implemented":[278],"MATLAB.":[280],"classify":[282],"metrics,":[287],"Precision,":[289],"Accuracy,":[290],"F1-score,":[291],"Recall":[292],"(Sensitivity),":[293],"Specificity,":[294],"Error":[295],"rate,":[296],"Computation":[297],"time,":[298,322],"False":[299],"Alarm":[300],"Rate,":[301],"RoC":[302],"considered.":[304],"attains":[311],"20.11%,":[312],"21.12%,":[313],"27.73%":[314],"higher":[315],"accuracy,":[316],"11.13%,":[317],"23.04%,":[318],"9.51%":[319],"lesser":[320],"computation":[321],"15.29%,":[323],"19.43%,":[324],"12.45%":[325],"greater":[326],"ROC,":[327],"28.65%,":[328],"23.98%,":[329],"27.03%":[330],"lower":[331],"false":[332],"alarm":[333],"when":[335],"compared":[336],"existing":[338],"methods:":[339],"Deep":[340,355],"Learning":[341,356],"improved":[342],"spider":[343],"monkey":[344],"crow":[345],"algorithm":[347],"recovery":[353],"(SCIR-DRNN),":[354],"topical":[357],"level":[358],"social":[362],"media":[363],"(SCIR-LSTM),":[365],"machine":[368],"learning-based":[369],"term":[370],"weighting":[371],"feature":[373],"selection":[374],"strategy":[375],"product":[381],"evaluations":[382],"(SCIR-ENN).":[383]},"counts_by_year":[{"year":2026,"cited_by_count":18}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}