{"id":"https://openalex.org/W4415124470","doi":"https://doi.org/10.1109/icmlt65785.2025.11193370","title":"Contextual Anomaly Detection in Logistics and Supply Chain Management","display_name":"Contextual Anomaly Detection in Logistics and Supply Chain Management","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415124470","doi":"https://doi.org/10.1109/icmlt65785.2025.11193370"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5072819869","display_name":"Ahmet \u00c7ay","orcid":"https://orcid.org/0000-0002-5370-1463"},"institutions":[{"id":"https://openalex.org/I4210088664","display_name":"Turkish Society of Hematology","ror":"https://ror.org/003pzts41","country_code":"TR","type":"other","lineage":["https://openalex.org/I4210088664"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ahmet \u00c7ay","raw_affiliation_strings":["Data Science Team Hepsijet,&#x0130;stanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Team Hepsijet,&#x0130;stanbul,Turkey","institution_ids":["https://openalex.org/I4210088664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110890292","display_name":"Bar\u0131\u015f Bayram","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088664","display_name":"Turkish Society of Hematology","ror":"https://ror.org/003pzts41","country_code":"TR","type":"other","lineage":["https://openalex.org/I4210088664"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Bar\u0131\u015f Bayram","raw_affiliation_strings":["Data Science Team Hepsijet,&#x0130;stanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Team Hepsijet,&#x0130;stanbul,Turkey","institution_ids":["https://openalex.org/I4210088664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119975365","display_name":"G\u00f6ktu\u011f G\u00f6ky\u0131lmaz","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088664","display_name":"Turkish Society of Hematology","ror":"https://ror.org/003pzts41","country_code":"TR","type":"other","lineage":["https://openalex.org/I4210088664"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"G\u00f6ktu\u011f G\u00f6ky\u0131lmaz","raw_affiliation_strings":["Data Science Team Hepsijet,&#x0130;stanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Team Hepsijet,&#x0130;stanbul,Turkey","institution_ids":["https://openalex.org/I4210088664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116588946","display_name":"Ay\u015fe Dilara T\u00fcrkmen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088664","display_name":"Turkish Society of Hematology","ror":"https://ror.org/003pzts41","country_code":"TR","type":"other","lineage":["https://openalex.org/I4210088664"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ay\u015fe Dilara T\u00fcrkmen","raw_affiliation_strings":["Data Science Team Hepsijet,&#x0130;stanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Team Hepsijet,&#x0130;stanbul,Turkey","institution_ids":["https://openalex.org/I4210088664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102808822","display_name":"Alaeddin T\u00fcrkmen","orcid":"https://orcid.org/0009-0001-7746-8504"},"institutions":[{"id":"https://openalex.org/I4210088664","display_name":"Turkish Society of Hematology","ror":"https://ror.org/003pzts41","country_code":"TR","type":"other","lineage":["https://openalex.org/I4210088664"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Alaeddin T\u00fcrkmen","raw_affiliation_strings":["Data Science Team Hepsijet,&#x0130;stanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Team Hepsijet,&#x0130;stanbul,Turkey","institution_ids":["https://openalex.org/I4210088664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210088664"],"apc_list":null,"apc_paid":null,"fwci":3.346,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93197694,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"427","last_page":"432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9925000071525574,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9925000071525574,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9855999946594238,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9196000099182129,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7947999835014343},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5932999849319458},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5548999905586243},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.5231000185012817},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"Supply chain management","score":0.3889000117778778},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.3749000132083893}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7947999835014343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6151999831199646},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5932999849319458},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.3889000117778778},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C2778964270","wikidata":"https://www.wikidata.org/wiki/Q7097774","display_name":"Operational efficiency","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.31839999556541443},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2058118558","https://openalex.org/W2134255060","https://openalex.org/W2183916895","https://openalex.org/W2586646478","https://openalex.org/W2774134601","https://openalex.org/W2783452787","https://openalex.org/W2901588159","https://openalex.org/W3167729385","https://openalex.org/W4281623203","https://openalex.org/W4281741212","https://openalex.org/W4294775255","https://openalex.org/W4322619127","https://openalex.org/W4400034234"],"related_works":[],"abstract_inverted_index":{"The":[0,155],"rapid":[1],"growth":[2],"of":[3,85,94,118,123,161,172],"the":[4,91,110,113,119,129,158,170],"Internet":[5],"has":[6,27],"transformed":[7],"e-commerce":[8],"and":[9,17,81,98,104,115,131,168,189],"logistics,":[10],"driving":[11],"a":[12,18,73],"surge":[13],"in":[14,142,163],"online":[15],"shopping":[16],"heightened":[19],"demand":[20],"for":[21,153],"efficient":[22],"delivery":[23,60,146,192],"operations.":[24],"This":[25,70,175],"evolution":[26],"introduced":[28],"new":[29],"challenges,":[30],"as":[31],"operational":[32,88,173],"delays":[33],"often":[34],"arise":[35],"from":[36,45,134],"incorrect":[37],"planning":[38],"or":[39,53],"unforeseen":[40],"anomalies,":[41],"which":[42],"can":[43],"originate":[44],"various":[46],"sources,":[47],"including":[48],"shipments,":[49],"carriers,":[50],"vehicles,":[51],"employees,":[52],"specific":[54,92],"tasks.":[55],"While":[56],"some":[57],"anomalies":[58,86,141],"disrupt":[59],"timelines,":[61],"others":[62],"do":[63],"not,":[64],"despite":[65],"exhibiting":[66],"abnormal":[67,166],"job":[68,102,143],"durations.":[69],"study":[71],"introduces":[72],"Contextual":[74],"Anomaly":[75],"Detection":[76],"(CAD)":[77],"framework":[78],"to":[79,139,183],"identify":[80],"analyze":[82,140],"all":[83],"types":[84],"within":[87],"processes,":[89,147,186],"considering":[90],"contexts":[93],"branches,":[95],"locations":[96],"(sender":[97],"receiver":[99],"cities),":[100],"times,":[101],"types,":[103],"units.":[105],"By":[106],"leveraging":[107],"real-world":[108],"data,":[109],"research":[111],"highlights":[112],"reliability":[114],"practical":[116],"applicability":[117],"findings.":[120],"A":[121],"variety":[122],"regression":[124],"models":[125],"are":[126],"employed":[127],"using":[128],"contextual":[130],"behavioral":[132],"features":[133],"on-time":[135],"deliveries":[136],"without":[137],"outliers":[138],"durations":[144],"during":[145],"alongside":[148],"comparisons":[149],"with":[150,180],"unsupervised":[151],"methods":[152],"CAD.":[154],"results":[156],"highlight":[157],"critical":[159],"role":[160],"CAD":[162],"accurately":[164],"identifying":[165],"patterns":[167],"improving":[169],"efficiency":[171],"workflows.":[174],"approach":[176],"provides":[177],"logistics":[178],"companies":[179],"actionable":[181],"insights":[182],"optimize":[184],"their":[185],"address":[187],"inefficiencies,":[188],"enhance":[190],"overall":[191],"performance.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}
