{"id":"https://openalex.org/W4210278687","doi":"https://doi.org/10.1142/s0219265921500250","title":"West Nile Virus Prediction Based on Data Mining","display_name":"West Nile Virus Prediction Based on Data Mining","publication_year":2022,"publication_date":"2022-01-31","ids":{"openalex":"https://openalex.org/W4210278687","doi":"https://doi.org/10.1142/s0219265921500250"},"language":"en","primary_location":{"id":"doi:10.1142/s0219265921500250","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219265921500250","pdf_url":null,"source":{"id":"https://openalex.org/S63112013","display_name":"Journal of Interconnection Networks","issn_l":"0219-2659","issn":["0219-2659","1793-6713"],"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":"Journal of Interconnection Networks","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/A5102895869","display_name":"Wei Meng","orcid":"https://orcid.org/0000-0001-7806-6905"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Meng","raw_affiliation_strings":["Informatization Office, Fudan University, Shanghai 200433, P. R. China"],"affiliations":[{"raw_affiliation_string":"Informatization Office, Fudan University, Shanghai 200433, P. R. China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102895869"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48930163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"22","issue":"04","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9829000234603882,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9829000234603882,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9383000135421753,"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":0.9305999875068665,"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/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7738286256790161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7330795526504517},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6316020488739014},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6231849789619446},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6039655804634094},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5673633813858032},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5262355208396912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5222933292388916},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49121373891830444},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.48574739694595337},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4563473165035248},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4346253275871277},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.41418296098709106},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34739136695861816},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.33866897225379944},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3311842083930969}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7738286256790161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7330795526504517},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6316020488739014},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6231849789619446},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6039655804634094},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5673633813858032},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5262355208396912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5222933292388916},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49121373891830444},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.48574739694595337},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4563473165035248},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4346253275871277},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.41418296098709106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34739136695861816},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.33866897225379944},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3311842083930969}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219265921500250","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219265921500250","pdf_url":null,"source":{"id":"https://openalex.org/S63112013","display_name":"Journal of Interconnection Networks","issn_l":"0219-2659","issn":["0219-2659","1793-6713"],"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":"Journal of Interconnection Networks","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1535111405","https://openalex.org/W2283632926","https://openalex.org/W2473526392","https://openalex.org/W2586068811","https://openalex.org/W2593914038","https://openalex.org/W3134478096","https://openalex.org/W4232665025","https://openalex.org/W4237390225"],"related_works":["https://openalex.org/W4280611221","https://openalex.org/W2754510604","https://openalex.org/W3168994312","https://openalex.org/W1915129189","https://openalex.org/W4316082230","https://openalex.org/W3199032340","https://openalex.org/W4221021152","https://openalex.org/W2979979539","https://openalex.org/W4315783552","https://openalex.org/W4293062801"],"abstract_inverted_index":{"This":[0],"paper":[1,33,55,70],"performed":[2,34],"some":[3],"exploratory":[4],"data":[5,9,17,35,42,73],"visualization":[6],"on":[7,40,60],"this":[8,29,32,41],"set.":[10,68],"The":[11,54,69],"nature":[12],"and":[13,21,37,96],"representation":[14],"of":[15,48,51],"input":[16],"was":[18,26],"found":[19],"out":[20],"the":[22,49,61,66,99,105,110],"preliminary":[23],"feature":[24,38],"selection":[25],"conducted":[27],"in":[28,65],"step.":[30],"And":[31],"preprocessing":[36],"engineering":[39],"set,":[43],"which":[44],"had":[45],"critical":[46],"importance":[47],"accuracy":[50],"prediction":[52,64],"results.":[53],"built":[56],"multiple":[57],"regression":[58],"models":[59],"missing":[62],"values":[63],"testing":[67],"implemented":[71],"various":[72],"mining":[74],"algorithms":[75],"to":[76],"build":[77],"predictive":[78],"models,":[79],"including":[80],"Gaussian":[81],"Naive":[82],"Bayes":[83],"classifier,":[84],"K-Nearest":[85],"Neighbors":[86],"(K-NN)":[87],"algorithm,":[88],"Multi-layer":[89],"Perceptron":[90],"(MLP),":[91],"Logistic":[92],"regression,":[93],"random":[94],"forest":[95],"XGBoost.":[97],"After":[98],"experiments,":[100],"XGBoost":[101],"classifier":[102],"could":[103],"give":[104],"best":[106],"result":[107],"among":[108],"all":[109],"models.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
