{"id":"https://openalex.org/W4360585209","doi":"https://doi.org/10.1109/ic3i56241.2022.10072576","title":"A Hybrid Deep Transfer Learning Approach For The Detection Of Vector-Borne Diseases","display_name":"A Hybrid Deep Transfer Learning Approach For The Detection Of Vector-Borne Diseases","publication_year":2022,"publication_date":"2022-12-14","ids":{"openalex":"https://openalex.org/W4360585209","doi":"https://doi.org/10.1109/ic3i56241.2022.10072576"},"language":"en","primary_location":{"id":"doi:10.1109/ic3i56241.2022.10072576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3i56241.2022.10072576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","raw_type":"proceedings-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/A5100696982","display_name":"Inderpreet Kaur","orcid":"https://orcid.org/0000-0002-8307-0113"},"institutions":[{"id":"https://openalex.org/I101407740","display_name":"Chandigarh University","ror":"https://ror.org/05t4pvx35","country_code":"IN","type":"education","lineage":["https://openalex.org/I101407740"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Inderpreet Kaur","raw_affiliation_strings":["Chandigarh University,UIC,Gharuan,Mohali,India","UIC, Chandigarh University, Gharuan, Mohali, India"],"affiliations":[{"raw_affiliation_string":"Chandigarh University,UIC,Gharuan,Mohali,India","institution_ids":["https://openalex.org/I101407740"]},{"raw_affiliation_string":"UIC, Chandigarh University, Gharuan, Mohali, India","institution_ids":["https://openalex.org/I101407740"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103227455","display_name":"Amanpreet Kaur Sandhu","orcid":null},"institutions":[{"id":"https://openalex.org/I101407740","display_name":"Chandigarh University","ror":"https://ror.org/05t4pvx35","country_code":"IN","type":"education","lineage":["https://openalex.org/I101407740"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amanpreet Kaur Sandhu","raw_affiliation_strings":["Chandigarh University,UIC,Gharuan,Mohali,India","UIC, Chandigarh University, Gharuan, Mohali, India"],"affiliations":[{"raw_affiliation_string":"Chandigarh University,UIC,Gharuan,Mohali,India","institution_ids":["https://openalex.org/I101407740"]},{"raw_affiliation_string":"UIC, Chandigarh University, Gharuan, Mohali, India","institution_ids":["https://openalex.org/I101407740"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103732070","display_name":"Yogesh Kumar","orcid":"https://orcid.org/0009-0001-1121-1829"},"institutions":[{"id":"https://openalex.org/I33586908","display_name":"Pandit Deendayal Petroleum University","ror":"https://ror.org/02nsv5p42","country_code":"IN","type":"education","lineage":["https://openalex.org/I33586908"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yogesh Kumar","raw_affiliation_strings":["Pandit Deendayal Energy University, Gandhinagar,School of Technology,Department of CSE,Gujarat,India","Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Pandit Deendayal Energy University, Gandhinagar,School of Technology,Department of CSE,Gujarat,India","institution_ids":["https://openalex.org/I33586908"]},{"raw_affiliation_string":"Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India","institution_ids":["https://openalex.org/I33586908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100696982"],"corresponding_institution_ids":["https://openalex.org/I101407740"],"apc_list":null,"apc_paid":null,"fwci":2.1405,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88989061,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2189","last_page":"2194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.98580002784729,"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/T10166","display_name":"Mosquito-borne diseases and control","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7586874961853027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6789805889129639},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6563543677330017},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6517082452774048},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5970869660377502},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5247907042503357},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4446389675140381},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4235437214374542},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38167569041252136},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0831136405467987},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.06352400779724121}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7586874961853027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6789805889129639},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6563543677330017},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6517082452774048},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5970869660377502},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5247907042503357},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4446389675140381},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4235437214374542},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38167569041252136},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0831136405467987},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.06352400779724121},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic3i56241.2022.10072576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3i56241.2022.10072576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5600000023841858,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2047767991","https://openalex.org/W2462812633","https://openalex.org/W2617959447","https://openalex.org/W2768038330","https://openalex.org/W2799742832","https://openalex.org/W2895290351","https://openalex.org/W2905190038","https://openalex.org/W2907050328","https://openalex.org/W2907880088","https://openalex.org/W2910904027","https://openalex.org/W3022592629","https://openalex.org/W3104703055","https://openalex.org/W3165296532","https://openalex.org/W3174251985","https://openalex.org/W4205623765","https://openalex.org/W4211112722","https://openalex.org/W4212786016","https://openalex.org/W4214876503","https://openalex.org/W4287009075","https://openalex.org/W6719069357","https://openalex.org/W6757943633","https://openalex.org/W6758110463","https://openalex.org/W6777324705"],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W3192840557","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3023427754","https://openalex.org/W4375928479","https://openalex.org/W3131673289","https://openalex.org/W4380075502","https://openalex.org/W3198847674"],"abstract_inverted_index":{"Vector-borne":[0],"diseases":[1,33],"considerably":[2],"impact":[3],"the":[4,44,64,73,92,95,101,110],"worldwide":[5],"population\u2019s":[6],"health":[7],"and":[8,18,48,58,89,108],"economic":[9],"well-being.":[10],"However,":[11],"training":[12,19],"deep-learning":[13],"models":[14],"requires":[15],"significant":[16],"time":[17],"data.":[20],"Therefore,":[21],"a":[22],"unique":[23],"hybrid":[24,65,74],"transfer":[25,75],"learning":[26,76],"approach":[27],"was":[28,78],"proposed":[29,96],"for":[30],"detecting":[31],"vector-borne":[32],"(VBD)":[34],"to":[35,63],"solve":[36],"these":[37],"issues":[38],"while":[39],"retaining":[40],"high":[41],"accuracy.":[42],"In":[43],"first":[45],"phase,":[46],"malaria":[47,93],"Lyme":[49,111],"benchmark":[50],"datasets":[51],"were":[52,61],"obtained.":[53],"Then":[54],"VGG16,":[55],"VGG19,":[56],"MobileNetV2,":[57],"DenseNet":[59],"169":[60],"compared":[62],"model":[66,97],"results":[67],"(MobileNetV2+DenseNet":[68,98],"169).":[69],"The":[70],"effectiveness":[71],"of":[72,106],"method":[77],"evaluated":[79],"using":[80],"several":[81],"performance":[82],"measures,":[83],"namely":[84],"precision,":[85],"loss,":[86],"accuracy,":[87],"AUC":[88],"RMSE.":[90],"On":[91],"dataset,":[94,112],"169)":[99],"achieved":[100],"most":[102],"excellent":[103],"classification":[104],"accuracy":[105],"99.9%,":[107],"on":[109],"99.3%.":[113]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}