{"id":"https://openalex.org/W7163031275","doi":"https://doi.org/10.48550/arxiv.2605.30388","title":"A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI","display_name":"A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7163031275","doi":"https://doi.org/10.48550/arxiv.2605.30388"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30388","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30388","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137566806","display_name":"Ismet Gocer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gocer, Ismet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137552722","display_name":"Zakirul Bhuiyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhuiyan, Zakirul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065521530","display_name":"Raza Hasan","orcid":"https://orcid.org/0000-0002-8089-837X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hasan, Raza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137572511","display_name":"Shakeel Ahmad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmad, Shakeel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11622","display_name":"Maritime Navigation and Safety","score":0.8863999843597412,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11622","display_name":"Maritime Navigation and Safety","score":0.8863999843597412,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.09239999949932098,"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":0.006300000008195639,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8036999702453613},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6672000288963318},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6101999878883362},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6092000007629395},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5562000274658203},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.49869999289512634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3418000042438507}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8036999702453613},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6672000288963318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6624000072479248},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6101999878883362},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6092000007629395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5684000253677368},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5139999985694885},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4618000090122223},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30388","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30388","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Life below water","score":0.7973462343215942,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,63,78,169,231],"new":[4],"systematic":[5,52],"framework":[6,98,121,167,229],"for":[7,44,177,236],"detecting":[8,45,211],"anomalies":[9,18,109,213],"in":[10,187,210,241],"maritime":[11,242],"Automatic":[12],"Identification":[13],"System":[14],"(AIS)":[15],"datasets.":[16],"These":[17,143],"include":[19],"abnormal":[20,189],"vessel":[21,47,190],"behaviours":[22],"related":[23],"to":[24,82,103],"speed,":[25],"position":[26],"jumps,":[27],"time":[28],"gaps,":[29],"and":[30,53,107,114,138,153,198,204,214,224,233],"turn":[31],"angles.":[32],"Although":[33],"unsupervised":[34,178,238],"learning":[35,90],"algorithms":[36],"such":[37],"as":[38],"Isolation":[39],"Forest":[40],"are":[41,145,222],"widely":[42],"used":[43],"anomalous":[46],"movements,":[48],"they":[49],"often":[50],"lack":[51],"meaningful":[54,234],"evaluation":[55,120,152],"measures.":[56],"To":[57],"address":[58],"this":[59],"limitation,":[60],"we":[61],"propose":[62],"novel":[64],"quality":[65],"metric":[66],"called":[67],"Maritime":[68],"Anomaly":[69,126],"Detection":[70],"Quality":[71],"Index":[72],"(MADQI).":[73],"The":[74,96,117],"prosed":[75],"MADQI":[76,119,170,195],"is":[77],"composite":[79],"index":[80],"designed":[81],"evaluate":[83],"the":[84,160,165,183,193,227],"anomaly":[85,179,216,239],"detection":[86,240],"performance":[87],"of":[88,172,202],"machine":[89],"models":[91],"without":[92],"requiring":[93],"labelled":[94],"data.":[95,244],"proposed":[97,118,166,228],"uses":[99],"Haversine":[100],"distance":[101],"calculations":[102],"analyse":[104],"AIS":[105,161,243],"datasets":[106],"identify":[108],"based":[110],"on":[111,159],"their":[112],"spatial":[113],"behavioural":[115],"characteristics.":[116],"integrates":[122],"four":[123],"interconnected":[124],"metrics:":[125],"Rate":[127],"Consistency":[128],"(ARC),":[129],"Physical":[130],"Plausibility":[131],"Score":[132,134],"(PPS),":[133],"Distribution":[135],"Separation":[136],"(SDS),":[137],"Extreme":[139],"Case":[140],"Evidence":[141],"(ECE).":[142],"metrics":[144],"combined":[146],"through":[147],"automatic":[148],"normalisation":[149],"using":[150],"multi-chunk":[151],"adaptive":[154],"scaling":[155],"techniques.":[156],"Experimental":[157],"results":[158,221],"dataset":[162],"show":[163],"that":[164,226],"achieved":[168,200],"score":[171],"80.37%,":[173],"demonstrating":[174],"its":[175],"effectiveness":[176],"detection.":[180],"In":[181],"particular,":[182],"algorithm":[184],"performed":[185],"strongly":[186],"identifying":[188],"behaviour.":[191],"Among":[192],"individual":[194],"components,":[196],"ECE":[197],"ARC":[199],"scores":[201],"0.907":[203],"1.000,":[205],"respectively,":[206],"indicating":[207],"excellent":[208],"capability":[209],"extreme":[212],"maintaining":[215],"rate":[217],"consistency.":[218],"Overall,":[219],"these":[220],"encouraging":[223],"demonstrate":[225],"provides":[230],"reliable":[232],"approach":[235],"evaluating":[237]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
