{"id":"https://openalex.org/W4415124632","doi":"https://doi.org/10.1109/icmlt65785.2025.11193272","title":"Comparative Analysis of Deep Neural Networks for Oil-in-Water Concentration Prediction Using PSO-Based Hyperparameter Optimization","display_name":"Comparative Analysis of Deep Neural Networks for Oil-in-Water Concentration Prediction Using PSO-Based Hyperparameter Optimization","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415124632","doi":"https://doi.org/10.1109/icmlt65785.2025.11193272"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193272","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/A5065011139","display_name":"Mahsa Kashani","orcid":"https://orcid.org/0000-0001-9086-8476"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mahsa Kashani","raw_affiliation_strings":["Aalborg University,AAU Energy,Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University,AAU Energy,Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058750599","display_name":"Stefan Jespersen","orcid":"https://orcid.org/0000-0002-5092-5701"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Stefan Jespersen","raw_affiliation_strings":["Aalborg University,AAU Energy,Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University,AAU Energy,Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100433323","display_name":"Zhenyu Yang","orcid":"https://orcid.org/0000-0002-0773-0298"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Zhenyu Yang","raw_affiliation_strings":["Aalborg University,AAU Energy,Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University,AAU Energy,Denmark","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42769231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"182","last_page":"187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9440000057220459,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9440000057220459,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9333999752998352,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7412999868392944},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.718999981880188},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6362000107765198},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5605000257492065},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5285000205039978},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.38850000500679016},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3336000144481659}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7412999868392944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7335000038146973},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.718999981880188},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6362000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6266999840736389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6258000135421753},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5605000257492065},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5285000205039978},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.38850000500679016},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193272","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"},{"id":"pmh:oai:pure.atira.dk:publications/613975cb-57f5-494a-9edd-7fa3a2f93e14","is_oa":false,"landing_page_url":"https://vbn.aau.dk/da/publications/613975cb-57f5-494a-9edd-7fa3a2f93e14","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Kashani, M, Jespersen, S & Yang, Z 2025, Comparative Analysis of Deep Neural Networks for Oil-in-Water Concentration Prediction Using PSO-Based Hyperparameter Optimization. in 2025 10th International Conference on Machine Learning Technologies, ICMLT 2025. IEEE (Institute of Electrical and Electronics Engineers), pp. 182-187, 2025 10th International Conference on Machine Learning Technologies , Helsinki, Finland, 23/05/2025. https://doi.org/10.1109/ICMLT65785.2025.11193272","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1959530414","https://openalex.org/W1965094989","https://openalex.org/W2064675550","https://openalex.org/W2083865948","https://openalex.org/W2152195021","https://openalex.org/W2317744239","https://openalex.org/W2891223108","https://openalex.org/W3047934025","https://openalex.org/W3136946585","https://openalex.org/W4205190314","https://openalex.org/W4210710680","https://openalex.org/W4319939964","https://openalex.org/W4387638646","https://openalex.org/W4392986947"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,111],"of":[2,29,82,103,110,132],"Oil-in-Water":[3],"(OiW)":[4],"concentration":[5],"in":[6,108,135],"de-oiling":[7],"hydrocyclone":[8],"systems":[9],"is":[10,106],"crucial":[11],"for":[12,44,94,139],"maintaining":[13],"environmental":[14],"compliance":[15],"and":[16,37,78,113,128],"optimizing":[17],"offshore":[18,83],"water":[19],"treatment":[20],"operations.":[21],"This":[22],"study":[23],"focuses":[24],"on":[25],"a":[26,50,91],"comparative":[27],"evaluation":[28],"Particle":[30],"Swarm":[31],"Optimization-based":[32],"Long":[33,39],"Short-Term":[34,40],"Memory":[35,41],"(LSTM)":[36],"Auto-Regressive":[38],"(AR-LSTM)":[42],"networks":[43],"modeling":[45],"OiW":[46],"dynamics":[47],"by":[48],"proposing":[49],"new":[51],"optimization":[52,134],"problem":[53],"to":[54,67,75],"fine-tune":[55],"the":[56,76,87,122,125,130],"network":[57],"hyperparameters.":[58],"The":[59,101,116],"AR-LSTM":[60],"model":[61,89],"integrates":[62],"an":[63],"auto-regressive":[64],"feedback":[65,99],"mechanism":[66],"dynamically":[68],"incorporate":[69],"prior":[70],"predictions,":[71],"enhancing":[72],"its":[73],"adaptability":[74],"time-dependent":[77],"irregular":[79],"patterns":[80],"characteristic":[81],"processes.":[84],"In":[85],"contrast,":[86],"LSTM":[88],"provides":[90],"robust":[92],"baseline":[93],"capturing":[95],"temporal":[96],"dependencies":[97],"without":[98],"integration.":[100],"performance":[102],"both":[104],"models":[105],"analyzed":[107],"terms":[109],"accuracy":[112],"computational":[114],"efficiency.":[115],"findings":[117],"provide":[118],"valuable":[119],"insights":[120],"into":[121],"trade-offs":[123],"between":[124],"two":[126],"architectures":[127],"underscore":[129],"role":[131],"hyperparameter":[133],"advancing":[136],"predictive":[137],"capabilities":[138],"complex":[140],"industrial":[141],"systems.":[142]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-14T00:00:00"}
