{"id":"https://openalex.org/W4416550163","doi":"https://doi.org/10.48550/arxiv.2511.15982","title":"Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models","display_name":"Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models","publication_year":2025,"publication_date":"2025-11-20","ids":{"openalex":"https://openalex.org/W4416550163","doi":"https://doi.org/10.48550/arxiv.2511.15982"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.15982","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.15982","pdf_url":"https://arxiv.org/pdf/2511.15982","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.15982","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009046765","display_name":"ChukwuNonso H. Nwokoye","orcid":"https://orcid.org/0000-0002-2534-3734"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nwokoye, Chukwunonso Henry","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120661456","display_name":"Blessing Oluchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oluchi, Blessing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120467477","display_name":"Sharna Waldron","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Waldron, Sharna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5117806208","display_name":"Peace Oguoguo Ezzeh","orcid":"https://orcid.org/0000-0002-0617-6547"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ezzeh, Peace","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009046765"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":0.19939999282360077,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12391","display_name":"Artificial Immune Systems Applications","score":0.19939999282360077,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.1281999945640564,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.05999999865889549,"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/wireless-sensor-network","display_name":"Wireless sensor network","score":0.645799994468689},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4075999855995178},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.39750000834465027},{"id":"https://openalex.org/keywords/malware","display_name":"Malware","score":0.391400009393692},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.3828999996185303},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37619999051094055},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.36980000138282776},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.36469998955726624},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3580999970436096}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6772000193595886},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6614000201225281},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.645799994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5627999901771545},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4848000109195709},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.2522999942302704},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.15982","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.15982","pdf_url":"https://arxiv.org/pdf/2511.15982","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.15982","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.15982","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.15982","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.15982","pdf_url":"https://arxiv.org/pdf/2511.15982","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,126,150],"lack":[1],"of":[2,60,92,111,120],"epidemiological":[3],"data":[4],"in":[5,14],"wireless":[6],"sensor":[7],"networks":[8],"(WSNs)":[9],"is":[10,114,161],"a":[11,98],"fundamental":[12],"difficulty":[13],"constructing":[15],"robust":[16],"models":[17,34],"to":[18,146],"forecast":[19],"and":[20,26,51,79,87,103,108,123,134,158,180,194],"mitigate":[21],"threats":[22],"such":[23,75],"as":[24,76,97,160],"viruses":[25],"worms.":[27],"Many":[28],"studies":[29],"have":[30],"examined":[31],"different":[32],"epidemic":[33,82],"for":[35,68,89],"WSNs,":[36],"focusing":[37],"on":[38,167,171],"how":[39],"malware":[40],"infections":[41],"spread":[42],"given":[43],"the":[44,61,90,101,109,117,121,147,172,185,198],"network's":[45],"specific":[46],"properties,":[47],"including":[48],"energy":[49],"limits":[50],"node":[52],"mobility.":[53],"In":[54],"this":[55],"study,":[56],"an":[57,143],"agent-based":[58],"implementation":[59],"susceptible-exposed-infected-recovered-vaccinated":[62],"(SEIRV)":[63],"mathematical":[64],"model":[65,165],"was":[66],"employed":[67],"machine":[69],"learning":[70],"(ML)":[71],"predictions.":[72],"Using":[73],"tools":[74],"NetLogo's":[77],"BehaviorSpace":[78],"Python,":[80],"two":[81],"synthetic":[83],"datasets":[84],"were":[85,106,153,183],"generated":[86],"prepared":[88],"application":[91],"several":[93],"ML":[94],"algorithms.":[95],"Posed":[96],"regression":[99,182],"problem,":[100],"infected":[102],"recovered":[104],"nodes":[105],"predicted,":[107],"performance":[110,166],"these":[112],"algorithms":[113],"compared":[115],"using":[116],"error":[118,132],"metrics":[119,133],"train":[122],"test":[124],"sets.":[125],"predictions":[127],"performed":[128],"well,":[129],"with":[130],"low":[131],"high":[135],"R^2":[136],"values":[137,152],"(0.997,":[138],"1.000,":[139],"0.999,":[140],"1.000),":[141],"indicating":[142],"effective":[144],"fit":[145],"training":[148],"set.":[149],"validation":[151],"lower":[154],"(0.992,":[155],"0.998,":[156],"0.971,":[157],"0.999),":[159],"typical":[162],"when":[163],"evaluating":[164],"unseen":[168],"data.":[169],"Based":[170],"recorded":[173],"performances,":[174],"support":[175],"vector,":[176],"linear,":[177],"Lasso,":[178],"Ridge,":[179],"ElasticNet":[181],"among":[184],"worst-performing":[186],"algorithms,":[187],"while":[188],"Random":[189],"Forest,":[190],"XGBoost,":[191],"Decision":[192],"Trees,":[193],"k-nearest":[195],"neighbors":[196],"achieved":[197],"best":[199],"results.":[200]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-23T00:00:00"}
