{"id":"https://openalex.org/W3108754989","doi":"https://doi.org/10.3390/s20226666","title":"Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks","display_name":"Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks","publication_year":2020,"publication_date":"2020-11-21","ids":{"openalex":"https://openalex.org/W3108754989","doi":"https://doi.org/10.3390/s20226666","mag":"3108754989","pmid":"https://pubmed.ncbi.nlm.nih.gov/33233358"},"language":"en","primary_location":{"id":"doi:10.3390/s20226666","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20226666","pdf_url":"https://www.mdpi.com/1424-8220/20/22/6666/pdf?version=1605945678","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/22/6666/pdf?version=1605945678","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034373932","display_name":"Kamil Ksi\u0105\u017cek","orcid":"https://orcid.org/0000-0002-0201-6220"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Kamil Ksi\u0105\u017cek","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0002-0201-6220","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068582854","display_name":"Micha\u0142 Romaszewski","orcid":"https://orcid.org/0000-0002-8227-929X"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Micha\u0142 Romaszewski","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0002-8227-929X","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061607408","display_name":"Przemys\u0142aw G\u0142omb","orcid":"https://orcid.org/0000-0002-0215-4674"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Przemys\u0142aw G\u0142omb","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0002-0215-4674","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073001649","display_name":"Bartosz Grabowski","orcid":"https://orcid.org/0000-0002-2364-6547"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Bartosz Grabowski","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0002-2364-6547","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037375081","display_name":"Micha\u0142 Cholewa","orcid":"https://orcid.org/0000-0001-6549-1590"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Micha\u0142 Cholewa","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0001-6549-1590","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, 44-100 Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5034373932"],"corresponding_institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":3.8249,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.94392988,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"20","issue":"22","first_page":"6666","last_page":"6666"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12388","display_name":"Identification and Quantification in Food","score":0.9521999955177307,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9119489192962646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8331620097160339},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7341965436935425},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6988998651504517},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6925654411315918},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6540331244468689},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5772035121917725},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5650137662887573},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5551356673240662},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4778123199939728},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42567190527915955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4233686327934265},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.41912755370140076},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2388037145137787}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9119489192962646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8331620097160339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7341965436935425},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6988998651504517},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6925654411315918},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6540331244468689},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5772035121917725},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5650137662887573},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5551356673240662},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4778123199939728},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42567190527915955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4233686327934265},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.41912755370140076},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2388037145137787},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000081862","descriptor_name":"Hyperspectral Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001801","descriptor_name":"Blood Stains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001801","descriptor_name":"Blood Stains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001801","descriptor_name":"Blood Stains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001801","descriptor_name":"Blood Stains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005554","descriptor_name":"Forensic Medicine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005554","descriptor_name":"Forensic Medicine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005554","descriptor_name":"Forensic Medicine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005554","descriptor_name":"Forensic Medicine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s20226666","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20226666","pdf_url":"https://www.mdpi.com/1424-8220/20/22/6666/pdf?version=1605945678","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:33233358","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33233358","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:6c80a8f5fd434951ae55bcf20d530ba7","is_oa":true,"landing_page_url":"https://doaj.org/article/6c80a8f5fd434951ae55bcf20d530ba7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 22, p 6666 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/22/6666/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s20226666","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 20; Issue 22; Pages: 6666","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7700311","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7700311","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20226666","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20226666","pdf_url":"https://www.mdpi.com/1424-8220/20/22/6666/pdf?version=1605945678","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G2001933235","display_name":null,"funder_award_id":"POWR.03.02.00-00-I029","funder_id":"https://openalex.org/F4320338080","funder_display_name":"European Social Fund"},{"id":"https://openalex.org/G487174310","display_name":null,"funder_award_id":"DI2017 013847","funder_id":"https://openalex.org/F4320322733","funder_display_name":"Ministerstwo Edukacji i Nauki"}],"funders":[{"id":"https://openalex.org/F4320322733","display_name":"Ministerstwo Edukacji i Nauki","ror":"https://ror.org/05dwvd537"},{"id":"https://openalex.org/F4320338080","display_name":"European Social Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W1521436688","https://openalex.org/W1560724230","https://openalex.org/W1604005662","https://openalex.org/W1977791453","https://openalex.org/W1988386267","https://openalex.org/W2011301426","https://openalex.org/W2020708554","https://openalex.org/W2053154970","https://openalex.org/W2054517318","https://openalex.org/W2058618453","https://openalex.org/W2096422372","https://openalex.org/W2097117768","https://openalex.org/W2101234009","https://openalex.org/W2118188395","https://openalex.org/W2136251662","https://openalex.org/W2163886442","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2282859112","https://openalex.org/W2405457405","https://openalex.org/W2490270993","https://openalex.org/W2495971917","https://openalex.org/W2500751094","https://openalex.org/W2528211483","https://openalex.org/W2548173722","https://openalex.org/W2572303978","https://openalex.org/W2577238056","https://openalex.org/W2603834682","https://openalex.org/W2605006852","https://openalex.org/W2614256707","https://openalex.org/W2620547787","https://openalex.org/W2624194194","https://openalex.org/W2753290709","https://openalex.org/W2770315464","https://openalex.org/W2794284562","https://openalex.org/W2794811744","https://openalex.org/W2802231127","https://openalex.org/W2808098982","https://openalex.org/W2809113079","https://openalex.org/W2897090465","https://openalex.org/W2914851866","https://openalex.org/W2942454403","https://openalex.org/W2946817927","https://openalex.org/W2957173096","https://openalex.org/W2962770389","https://openalex.org/W2964383635","https://openalex.org/W2970971581","https://openalex.org/W2978771119","https://openalex.org/W2991616716","https://openalex.org/W2996457415","https://openalex.org/W3007444020","https://openalex.org/W3017845037","https://openalex.org/W3035965352","https://openalex.org/W3081140350","https://openalex.org/W3097217077","https://openalex.org/W3098388691","https://openalex.org/W3099878876","https://openalex.org/W3100011500","https://openalex.org/W3103753223","https://openalex.org/W3105357426","https://openalex.org/W4240485910","https://openalex.org/W6607672814","https://openalex.org/W6675354045","https://openalex.org/W6713881377"],"related_works":["https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3192962470","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"In":[0,155,175],"recent":[1],"years,":[2],"growing":[3],"interest":[4],"in":[5,44,281,326,345],"deep":[6,89,301,333,425],"learning":[7,90,302,334],"neural":[8,222,227,426],"networks":[9,223],"has":[10,104],"raised":[11],"a":[12,74,101,142,164,167,183,189,225,231,254,327,337,367,373,376],"question":[13],"on":[14,61,100,259],"how":[15],"they":[16],"can":[17],"be":[18],"used":[19,43],"for":[20,77,93,196,313,320,380,393],"effective":[21],"processing":[22],"of":[23,39,49,52,80,112,117,153,204,206,237,245,274,300,348,370,378,386],"high-dimensional":[24],"datasets":[25],"produced":[26],"by":[27,354,358],"hyperspectral":[28,84,139,430],"imaging":[29],"(HSI).":[30],"HSI,":[31],"traditionally":[32],"viewed":[33],"as":[34],"being":[35],"within":[36],"the":[37,50,62,78,88,135,156,172,176,202,211,238,282,288,291,314,321,346,351,355,391,402,405,424],"scope":[38],"remote":[40],"sensing,":[41],"is":[42,55,64,186,193,311,344,396,432],"non-invasive":[45],"substance":[46,59],"classification.":[47],"One":[48],"areas":[51],"potential":[53],"application":[54],"forensic":[56,207],"science,":[57],"where":[58],"classification":[60,91,140],"scenes":[63],"important.":[65],"An":[66,384],"example":[67],"problem":[68,95],"from":[69,171,188,201,410],"that":[70,82,265,280,390,404,422],"area-blood":[71],"stain":[72],"classification-is":[73],"case":[75],"study":[76,212],"evaluation":[79,256],"methods":[81],"process":[83],"data.":[85],"To":[86,132],"investigate":[87],"performance":[92,236],"this":[94,110,400],"we":[96,147],"have":[97,148,251,293,365],"performed":[98],"experiments":[99],"dataset":[102,115],"which":[103,192],"not":[105],"been":[106],"previously":[107],"tested":[108,382],"using":[109,213],"kind":[111],"model.":[113],"This":[114],"consists":[116],"several":[118,214],"images":[119],"with":[120,242,296],"blood":[121],"and":[122,141,166,219,230,261,270,290,318,372,401,417],"blood-like":[123],"substances":[124],"like":[125,216],"ketchup,":[126],"tomato":[127],"concentrate,":[128],"artificial":[129],"blood,":[130],"etc.":[131],"test":[133,168,184],"both":[134,163],"classic":[136],"approach":[137],"to":[138,268,360,429],"more":[143,194,199,322,328],"realistic":[144],"application-oriented":[145],"scenario,":[146],"prepared":[149],"two":[150,411],"different":[151,190,412],"sets":[152],"experiments.":[154],"first":[157],"one,":[158,178],"Hyperspectral":[159,179],"Transductive":[160],"Classification":[161,181],"(HTC),":[162],"training":[165],"set":[169,185,353],"come":[170,409],"same":[173],"image.":[174],"second":[177],"Inductive":[180],"(HIC),":[182],"derived":[187],"image,":[191],"challenging":[195,329],"classifiers":[197],"but":[198],"useful":[200],"point":[203],"view":[205],"investigators.":[208],"We":[209,250,364],"conducted":[210],"architectures":[215,335],"1D,":[217],"2D":[218],"3D":[220],"convolutional":[221],"(CNN),":[224],"recurrent":[226],"network":[228,427],"(RNN)":[229],"multilayer":[232],"perceptron":[233],"(MLP).":[234],"The":[235,304],"models":[239,310,357,408],"was":[240],"compared":[241],"baseline":[243,352],"results":[244,278,371,420],"Support":[246],"Vector":[247],"Machine":[248],"(SVM).":[249],"also":[252],"presented":[253,366],"model":[255,275],"method":[257],"based":[258],"t-SNE":[260],"confusion":[262],"matrix":[263],"analysis":[264,369,385],"allows":[266],"us":[267],"detect":[269],"eliminate":[271],"some":[272],"cases":[273],"undertraining.":[276],"Our":[277],"show":[279],"transductive":[283],"case,":[284,331],"all":[285,309],"models,":[286],"including":[287,375],"MLP":[289],"SVM,":[292],"comparative":[294],"performance,":[295],"no":[297],"clear":[298],"advantage":[299],"models.":[303],"Overall":[305,342],"Accuracy":[306,343],"range":[307,347],"across":[308],"98-100%":[312],"easier":[315],"image":[316],"set,":[317],"74-94%":[319],"difficult":[323],"one.":[324],"However,":[325],"inductive":[330],"selected":[332],"offer":[336],"significant":[338],"advantage;":[339],"their":[340],"best":[341,406],"57-71%,":[349],"improving":[350],"non-deep":[356],"up":[359],"9":[361],"percentage":[362],"points.":[363],"detailed":[368],"discussion,":[374],"summary":[377],"conclusions":[379],"each":[381,394],"architecture.":[383],"per-class":[387],"errors":[388],"shows":[389],"score":[392],"class":[395],"highly":[397],"model-dependent.":[398],"Considering":[399],"fact":[403],"performing":[407],"architecture":[413,428],"families":[414],"(3D":[415],"CNN":[416],"RNN),":[418],"our":[419],"suggest":[421],"tailoring":[423],"data":[431],"still":[433],"an":[434],"open":[435],"problem.":[436]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}