{"id":"https://openalex.org/W4220787670","doi":"https://doi.org/10.3233/idt-200167","title":"On the domain aided performance boosting technique for deep predictive networks: A COVID-19 scenario","display_name":"On the domain aided performance boosting technique for deep predictive networks: A COVID-19 scenario","publication_year":2022,"publication_date":"2022-03-15","ids":{"openalex":"https://openalex.org/W4220787670","doi":"https://doi.org/10.3233/idt-200167"},"language":"en","primary_location":{"id":"doi:10.3233/idt-200167","is_oa":true,"landing_page_url":"https://doi.org/10.3233/idt-200167","pdf_url":"https://content.iospress.com:443/download/intelligent-decision-technologies/idt200167?id=intelligent-decision-technologies%2Fidt200167","source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://content.iospress.com:443/download/intelligent-decision-technologies/idt200167?id=intelligent-decision-technologies%2Fidt200167","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091017389","display_name":"Soumya Jyoti Raychaudhuri","orcid":null},"institutions":[{"id":"https://openalex.org/I302410947","display_name":"M S Ramaiah University of Applied Sciences","ror":"https://ror.org/02anh8x74","country_code":"IN","type":"education","lineage":["https://openalex.org/I302410947"]},{"id":"https://openalex.org/I4210146082","display_name":"Sify Technologies (India)","ror":"https://ror.org/03r8r2789","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210146082"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Soumya Jyoti Raychaudhuri","raw_affiliation_strings":["MS Ramaiah University of Applied Sciences, India","Univer-sity of Applied Sciences, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MS Ramaiah University of Applied Sciences, India","institution_ids":["https://openalex.org/I302410947"]},{"raw_affiliation_string":"Univer-sity of Applied Sciences, India","institution_ids":["https://openalex.org/I4210146082"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112598534","display_name":"C. Narendra Babu","orcid":null},"institutions":[{"id":"https://openalex.org/I302410947","display_name":"M S Ramaiah University of Applied Sciences","ror":"https://ror.org/02anh8x74","country_code":"IN","type":"education","lineage":["https://openalex.org/I302410947"]},{"id":"https://openalex.org/I4210146082","display_name":"Sify Technologies (India)","ror":"https://ror.org/03r8r2789","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210146082"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"C. Narendra Babu","raw_affiliation_strings":["MS Ramaiah University of Applied Sciences, India","Univer-sity of Applied Sciences, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MS Ramaiah University of Applied Sciences, India","institution_ids":["https://openalex.org/I302410947"]},{"raw_affiliation_string":"Univer-sity of Applied Sciences, India","institution_ids":["https://openalex.org/I4210146082"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112598534"],"corresponding_institution_ids":["https://openalex.org/I302410947","https://openalex.org/I4210146082"],"apc_list":null,"apc_paid":null,"fwci":0.1302,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39495322,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"16","issue":"1","first_page":"111","last_page":"125"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.7072502970695496},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6952687501907349},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6374782919883728},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6295244097709656},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6100245118141174},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6080909967422485},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5857274532318115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5369683504104614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5112369060516357},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.48614761233329773},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.46982622146606445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44474470615386963},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39895105361938477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38204172253608704},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3742808997631073},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2218325138092041},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1450939178466797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7072502970695496},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6952687501907349},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6374782919883728},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6295244097709656},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6100245118141174},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6080909967422485},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5857274532318115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5369683504104614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5112369060516357},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.48614761233329773},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.46982622146606445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44474470615386963},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39895105361938477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38204172253608704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3742808997631073},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2218325138092041},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1450939178466797},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/idt-200167","is_oa":true,"landing_page_url":"https://doi.org/10.3233/idt-200167","pdf_url":"https://content.iospress.com:443/download/intelligent-decision-technologies/idt200167?id=intelligent-decision-technologies%2Fidt200167","source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3233/idt-200167","is_oa":true,"landing_page_url":"https://doi.org/10.3233/idt-200167","pdf_url":"https://content.iospress.com:443/download/intelligent-decision-technologies/idt200167?id=intelligent-decision-technologies%2Fidt200167","source":{"id":"https://openalex.org/S119727669","display_name":"Intelligent Decision Technologies","issn_l":"1872-4981","issn":["1872-4981","1875-8843"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Decision Technologies","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220787670.pdf","grobid_xml":"https://content.openalex.org/works/W4220787670.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1974479059","https://openalex.org/W2060239884","https://openalex.org/W2105149323","https://openalex.org/W2253429366","https://openalex.org/W2479217928","https://openalex.org/W2715071750","https://openalex.org/W2884352268","https://openalex.org/W2914633988","https://openalex.org/W2947870382","https://openalex.org/W2968862935","https://openalex.org/W3004529643","https://openalex.org/W3010336234","https://openalex.org/W3010500276","https://openalex.org/W3012189167","https://openalex.org/W3012916860","https://openalex.org/W3013474995","https://openalex.org/W3013547516","https://openalex.org/W3014226016","https://openalex.org/W3014725478","https://openalex.org/W3016143592","https://openalex.org/W3016966855","https://openalex.org/W3017117984","https://openalex.org/W3022885394","https://openalex.org/W3096718527","https://openalex.org/W3104810384","https://openalex.org/W4205360373","https://openalex.org/W4226318754","https://openalex.org/W6774979536","https://openalex.org/W6873698495"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4231274751","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1518215897","https://openalex.org/W3208495680","https://openalex.org/W3210053092"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2,180,195],"are":[3,118],"one":[4],"of":[5,87,99],"the":[6,24,31,85,94,129,141,171,175,187],"widely":[7],"used":[8],"techniques":[9],"for":[10,47,79,120,139,156],"forecasting":[11],"time":[12,97,189],"series":[13],"data":[14,46,131],"in":[15,43,124,197],"various":[16,88],"applications.":[17],"It":[18],"has":[19,106,132],"already":[20],"been":[21,107,133,150,201],"established":[22],"that":[23,170],"Recurrent":[25,37],"Neural":[26,115],"Networks":[27,116],"(RNN)":[28],"such":[29],"as":[30],"Long":[32],"Short-Term":[33],"Memory":[34],"(LSTM),":[35],"Gated":[36],"Units":[38],"(GRU),":[39],"etc.,":[40],"perform":[41],"well":[42],"analyzing":[44,140],"sequence":[45,179],"accurate":[48],"time-series":[49,130],"predictions.":[50],"But,":[51],"these":[52],"specialized":[53],"recurrent":[54],"architectures":[55],"suffer":[56],"from":[57,127],"certain":[58],"drawbacks":[59],"due":[60],"to":[61],"their":[62,67,121],"computational":[63],"complexity":[64],"and":[65,90,96,184,213],"also":[66],"dependency":[68],"on":[69,93,109,152],"short":[70],"term":[71],"historical":[72],"data.":[73,112],"Hence,":[74],"there":[75],"is":[76],"a":[77,136,153,160],"scope":[78],"further":[80,157,185],"improvement.":[81],"This":[82],"paper":[83],"analyzes":[84],"effects":[86],"optimizers":[89],"hyper-parameter":[91],"tuning,":[92],"precision":[95],"efficiency":[98],"different":[100],"deep":[101],"neural":[102],"architectures.":[103],"The":[104,146,166,193],"analysis":[105,158],"conducted":[108],"COVID-19":[110],"pandemic":[111],"Since":[113],"Convolutional":[114],"(CNN)":[117],"known":[119],"super-human":[122],"ability":[123],"identifying":[125],"patterns":[126,143,148],"images,":[128],"transformed":[134],"into":[135],"slope-information":[137],"domain":[138,147],"slope":[142],"over":[144,177],"time.":[145],"have":[149,200],"projected":[151],"2D":[154],"plane":[155],"using":[159,203],"restricted":[161],"recursive":[162],"CNN":[163],"(RRCNN)":[164],"algorithm.":[165],"experimental":[167],"results":[168],"reveal":[169],"proposed":[172],"methodology":[173],"reduces":[174,186],"error":[176],"benchmarked":[178],"by":[181,190],"almost":[182],"20%":[183],"training":[188],"nearly":[191],"50%.":[192],"prediction":[194],"considered":[196],"this":[198],"study":[199],"evaluated":[202],"Root":[204],"Mean":[205,209,214],"Squared":[206],"Error":[207,211,217],"(RMSE),":[208],"Absolute":[210,215],"(MAE),":[212],"Percentage":[216],"(MAPE%).":[218]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
