{"id":"https://openalex.org/W4416862952","doi":"https://doi.org/10.3390/make7040156","title":"Anomaly Detection Based on Markovian Geometric Diffusion","display_name":"Anomaly Detection Based on Markovian Geometric Diffusion","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W4416862952","doi":"https://doi.org/10.3390/make7040156"},"language":"en","primary_location":{"id":"doi:10.3390/make7040156","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040156","pdf_url":null,"source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/make7040156","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060561481","display_name":"Erikson Carlos Ramos","orcid":"https://orcid.org/0000-0002-8960-3072"},"institutions":[{"id":"https://openalex.org/I169045520","display_name":"Universidade Federal da Para\u00edba","ror":"https://ror.org/00p9vpz11","country_code":"BR","type":"education","lineage":["https://openalex.org/I169045520"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Erikson Carlos Ramos","raw_affiliation_strings":["Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-8960-3072","affiliations":[{"raw_affiliation_string":"Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil","institution_ids":["https://openalex.org/I169045520"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021436388","display_name":"Leandro Carlos de Souza","orcid":"https://orcid.org/0000-0002-5318-4814"},"institutions":[{"id":"https://openalex.org/I169045520","display_name":"Universidade Federal da Para\u00edba","ror":"https://ror.org/00p9vpz11","country_code":"BR","type":"education","lineage":["https://openalex.org/I169045520"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leandro Carlos de Souza","raw_affiliation_strings":["Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-5318-4814","affiliations":[{"raw_affiliation_string":"Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil","institution_ids":["https://openalex.org/I169045520"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064704822","display_name":"Gustavo Henrique Matos Bezerra Motta","orcid":"https://orcid.org/0000-0002-5664-5938"},"institutions":[{"id":"https://openalex.org/I169045520","display_name":"Universidade Federal da Para\u00edba","ror":"https://ror.org/00p9vpz11","country_code":"BR","type":"education","lineage":["https://openalex.org/I169045520"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gustavo Henrique Matos Bezerra Motta","raw_affiliation_strings":["Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-5664-5938","affiliations":[{"raw_affiliation_string":"Department of Informatics at the Center for Informatics, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58055-000, Brazil","institution_ids":["https://openalex.org/I169045520"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060561481"],"corresponding_institution_ids":["https://openalex.org/I169045520"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17648991,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":"4","first_page":"156","last_page":"156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.923799991607666,"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":0.923799991607666,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.021900000050663948,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.005499999970197678,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7335000038146973},{"id":"https://openalex.org/keywords/diffusion-map","display_name":"Diffusion map","score":0.6159999966621399},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5587000250816345},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.49970000982284546},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.49079999327659607},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.48399999737739563}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7335000038146973},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.6159999966621399},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5044000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5013999938964844},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43320000171661377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.353300005197525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3100999891757965},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C75438885","wikidata":"https://www.wikidata.org/wiki/Q3403615","display_name":"Large deviations theory","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7040156","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040156","pdf_url":null,"source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b2e4038cefbf49c68c02251a43c8de8e","is_oa":true,"landing_page_url":"https://doaj.org/article/b2e4038cefbf49c68c02251a43c8de8e","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":"Machine Learning and Knowledge Extraction, Vol 7, Iss 4, p 156 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7040156","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7040156","pdf_url":null,"source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W102276202","https://openalex.org/W309312769","https://openalex.org/W1963586106","https://openalex.org/W1971398013","https://openalex.org/W1986332411","https://openalex.org/W1994101233","https://openalex.org/W1994911252","https://openalex.org/W1995875735","https://openalex.org/W2013389316","https://openalex.org/W2041184937","https://openalex.org/W2061240327","https://openalex.org/W2097308346","https://openalex.org/W2097714558","https://openalex.org/W2112796928","https://openalex.org/W2124509324","https://openalex.org/W2128452440","https://openalex.org/W2158698691","https://openalex.org/W2163581538","https://openalex.org/W2171431008","https://openalex.org/W2175173358","https://openalex.org/W2282861635","https://openalex.org/W2296719434","https://openalex.org/W2337344967","https://openalex.org/W2516208336","https://openalex.org/W2541635437","https://openalex.org/W2695617904","https://openalex.org/W2760451156","https://openalex.org/W2782751838","https://openalex.org/W2890686092","https://openalex.org/W2911964244","https://openalex.org/W2947547352","https://openalex.org/W2963469388","https://openalex.org/W2981731882","https://openalex.org/W3040266635","https://openalex.org/W3097072763","https://openalex.org/W3152364157","https://openalex.org/W4213367101","https://openalex.org/W4253461361","https://openalex.org/W4254182148","https://openalex.org/W4313583538","https://openalex.org/W4322743747","https://openalex.org/W4380685430","https://openalex.org/W4389263964","https://openalex.org/W4400077595","https://openalex.org/W4401576913","https://openalex.org/W4403090845","https://openalex.org/W4404572481","https://openalex.org/W4408063684","https://openalex.org/W4410394489"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"anomaly":[1],"detection":[2],"is":[3,41,68,76],"vital":[4],"in":[5,56,121,131,142,152],"domains":[6],"such":[7,115],"as":[8,116],"healthcare,":[9],"finance,":[10],"and":[11,45,105,119],"cybersecurity,":[12],"where":[13],"subtle":[14],"deviations":[15],"may":[16],"signal":[17],"fraud,":[18],"failures,":[19],"or":[20],"impending":[21],"risks.":[22],"This":[23],"paper":[24],"proposes":[25],"an":[26],"unsupervised":[27],"anomaly-detection":[28,144],"method":[29],"called":[30],"Anomaly":[31],"Detection":[32],"Based":[33],"on":[34,103],"Markovian":[35,49],"Geometric":[36,50],"Diffusion":[37,51],"(AD-MGD).":[38],"The":[39,81,135],"technique":[40],"applicable":[42],"to":[43,52,70,97],"uni-":[44],"multidimensional":[46,62],"datasets,":[47],"employing":[48],"uncover":[53],"nonlinear":[54],"structures":[55],"the":[57,64,71,74,92,98,126,138,147],"relationships":[58],"among":[59],"instances.":[60],"For":[61],"data,":[63],"scale":[65,93],"parameter,":[66,94],"which":[67],"crucial":[69],"performance":[72],"of":[73,91,123,140,149],"method,":[75],"tuned":[77],"using":[78],"Shannon":[79],"entropy.":[80],"approach":[82],"includes":[83],"a":[84],"global":[85],"search":[86],"followed":[87],"by":[88],"local":[89],"refinement":[90],"promoting":[95],"adaptability":[96],"data":[99,133,153],"context.":[100],"Experimental":[101],"evaluations":[102],"synthetic":[104],"real":[106],"datasets":[107],"show":[108],"that":[109],"AD-MGD":[110,141],"consistently":[111],"outperforms":[112],"classical":[113],"methods":[114],"KNN,":[117],"LOF,":[118],"IForest":[120],"terms":[122],"area":[124],"under":[125],"ROC":[127],"curve":[128],"(AUC),":[129],"particularly":[130],"heterogeneous":[132],"scenarios.":[134],"results":[136],"highlight":[137],"potential":[139],"critical":[143],"applications,":[145],"advancing":[146],"use":[148],"diffusion":[150],"techniques":[151],"mining.":[154]},"counts_by_year":[],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-12-01T00:00:00"}
