{"id":"https://openalex.org/W4414242221","doi":"https://doi.org/10.1145/3768162","title":"Frequency-Modulated Transformer Self-Attention for Advanced Infectious Disease Prediction","display_name":"Frequency-Modulated Transformer Self-Attention for Advanced Infectious Disease Prediction","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4414242221","doi":"https://doi.org/10.1145/3768162"},"language":"en","primary_location":{"id":"doi:10.1145/3768162","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768162","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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/A5016415430","display_name":"Asmita Mahajan","orcid":"https://orcid.org/0000-0001-5658-5851"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Asmita Mahajan","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India","Indian Institute of Technology Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0001-5658-5851","affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]},{"raw_affiliation_string":"Indian Institute of Technology Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082753354","display_name":"Durga Toshniwal","orcid":null},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Durga Toshniwal","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India","Indian Institute of Technology Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0002-7960-4127","affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]},{"raw_affiliation_string":"Indian Institute of Technology Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12403861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"3","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.96670001745224,"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":0.96670001745224,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9645000100135803,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9645000100135803,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/novelty","display_name":"Novelty","score":0.6294000148773193},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5993000268936157},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.5508999824523926},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46779999136924744},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46230000257492065},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4004000127315521},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.39890000224113464},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.37299999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7871000170707703},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.6294000148773193},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5993000268936157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5687000155448914},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.5508999824523926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.39890000224113464},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3499000072479248},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3768162","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768162","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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":39,"referenced_works":["https://openalex.org/W2024760831","https://openalex.org/W2117014758","https://openalex.org/W2125056386","https://openalex.org/W2481916066","https://openalex.org/W2569349941","https://openalex.org/W2794778778","https://openalex.org/W2797846142","https://openalex.org/W2947459187","https://openalex.org/W2971361125","https://openalex.org/W3012354890","https://openalex.org/W3015972374","https://openalex.org/W3034077089","https://openalex.org/W3039232647","https://openalex.org/W3047027743","https://openalex.org/W3102363003","https://openalex.org/W3107979244","https://openalex.org/W3177318507","https://openalex.org/W3209594828","https://openalex.org/W4226418765","https://openalex.org/W4307645833","https://openalex.org/W4315779632","https://openalex.org/W4318819061","https://openalex.org/W4365397734","https://openalex.org/W4378717986","https://openalex.org/W4381715962","https://openalex.org/W4388016610","https://openalex.org/W4388283086","https://openalex.org/W4388531779","https://openalex.org/W4391093143","https://openalex.org/W4391223586","https://openalex.org/W4392222741","https://openalex.org/W4392620905","https://openalex.org/W4393156206","https://openalex.org/W4399504001","https://openalex.org/W4399992273","https://openalex.org/W4400688606","https://openalex.org/W4401448504","https://openalex.org/W4403205087","https://openalex.org/W4404439040"],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1,67,107],"forecasting":[2,35,228],"of":[3,16,46,97,193],"infectious":[4],"diseases":[5],"is":[6,168,197],"crucial":[7],"to":[8,58,76,148],"addressing":[9],"significant":[10],"global":[11],"health":[12,233],"challenges.":[13,61],"The":[14,62,95,126,165,191],"application":[15],"AI,":[17],"particularly":[18],"Deep":[19],"Learning":[20],"(DL)":[21],"algorithms,":[22],"has":[23],"demonstrated":[24],"substantial":[25,218],"success":[26],"in":[27,33,102,157,211,220],"sequence":[28],"modeling;":[29],"however,":[30],"their":[31],"performance":[32],"epidemiological":[34,227],"remains":[36],"constrained":[37],"by":[38,141,200],"the":[39,98,104,115,158],"non-stationary":[40],"nature":[41],"and":[42,86,91,113,136,144,181,187,204,223,229,235],"complex":[43],"frequency":[44],"dynamics":[45],"disease":[47,237],"transmission.":[48],"This":[49,215],"research":[50],"introduces":[51],"a":[52,119],"novel":[53],"Modulated":[54],"Transformer":[55,120],"(FMT)":[56],"framework":[57,64,128],"address":[59],"these":[60],"FMT":[63,127],"decomposes":[65],"time":[66,106],"data":[68],"into":[69,108,118],"distinct":[70],"frequency-modulated":[71],"signals,":[72],"utilizing":[73],"self-attention":[74],"mechanisms":[75],"capture":[77],"temporal":[78,89],"frequencies.":[79],"A":[80],"transformer":[81],"encoder\u2013decoder":[82],"architecture":[83,121],"then":[84],"predicts":[85],"captures":[87],"multi-scale":[88],"dependencies":[90],"makes":[92],"accurate":[93],"predictions.":[94],"novelty":[96],"proposed":[99,166],"approach":[100],"lies":[101],"decomposing":[103],"input":[105],"Intrinsic":[109],"Mode":[110],"Functions":[111],"(IMFs)":[112],"integrating":[114],"frequency-specific":[116],"components":[117],"via":[122],"entropy-based":[123,194],"feature":[124],"selection.":[125],"significantly":[129],"reduces":[130],"Root":[131],"Mean":[132,137],"Square":[133],"Error":[134,139],"(RMSE)":[135],"Absolute":[138],"(MAE)":[140],"approximately":[142],"50%":[143],"65%,":[145],"respectively,":[146],"compared":[147],"conventional":[149],"methods.":[150],"Additionally,":[151],"it":[152],"achieves":[153],"an":[154,178],"8%":[155],"increase":[156],"R2":[159],"score,":[160],"demonstrating":[161],"enhanced":[162],"predictive":[163,213,221],"accuracy.":[164,214],"methodology":[167],"evaluated":[169],"using":[170],"COVID-19":[171],"datasets":[172],"from":[173],"multiple":[174],"countries":[175],"along":[176],"with":[177,203],"influenza":[179],"dataset":[180],"benchmarked":[182],"against":[183],"statistical,":[184],"machine":[185],"learning,":[186],"state-of-the-art":[188],"DL":[189],"baselines.":[190],"contribution":[192],"IMF":[195],"integration":[196],"systematically":[198],"examined":[199],"comparing":[201],"results":[202],"without":[205],"this":[206],"component,":[207],"underscoring":[208],"its":[209],"importance":[210],"improving":[212],"work":[216],"highlights":[217],"improvements":[219],"accuracy":[222],"computational":[224],"efficiency,":[225],"advancing":[226],"supporting":[230],"real-time":[231],"public":[232],"decision-making":[234],"AI-driven":[236],"surveillance":[238],"systems.":[239]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
