{"id":"https://openalex.org/W7115334027","doi":null,"title":"Evaluation of the Image Datasets Complexity Using Convolutional Autoencoders","display_name":"Evaluation of the Image Datasets Complexity Using Convolutional Autoencoders","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7115334027"},"language":"en","primary_location":{"id":"pmh:oai:www.doria.fi:10024/189265","doi":null,"is_oa":true,"landing_page_url":"https://www.doria.fi/handle/10024/189265","pdf_url":"https://www.doria.fi/bitstream/10024/189265/2/vatandoust_monire.pdf","source":{"id":"https://openalex.org/S4377196760","display_name":"Doria (University of Helsinki)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I133731052","host_organization_name":"University of Helsinki","host_organization_lineage":["https://openalex.org/I133731052"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Diplomarbete"},"volume":null,"type":"other","is_traditional":false,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.doria.fi/bitstream/10024/189265/2/vatandoust_monire.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Vatandoust, Monire","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130217899","display_name":"\u00c5bo Akademi University","ror":"https://ror.org/029pk6x14","country_code":"FI","type":"education","lineage":["https://openalex.org/I130217899"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Vatandoust, Monire","raw_affiliation_strings":["Faculty of Science and Engineering Master's degree Programme in Information technology , Computer Science \u00c5bo Akademi University 2024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Engineering Master's degree Programme in Information technology , Computer Science \u00c5bo Akademi University 2024","institution_ids":["https://openalex.org/I130217899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130217899"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6098999977111816,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6098999977111816,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.20029999315738678,"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/T10320","display_name":"Neural Networks and Applications","score":0.03909999877214432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dataset-complexity","display_name":"dataset complexity","score":0.9490000009536743},{"id":"https://openalex.org/keywords/convolutional-autoencoder","display_name":"convolutional autoencoder","score":0.9990000128746033},{"id":"https://openalex.org/keywords/reconstruction-loss","display_name":"reconstruction loss","score":0.9860000014305115},{"id":"https://openalex.org/keywords/latent-space-dimensionality","display_name":"latent space dimensionality","score":0.9919999837875366},{"id":"https://openalex.org/keywords/coco-dataset","display_name":"COCO dataset","score":0.9440000057220459},{"id":"https://openalex.org/keywords/entropy-information-theory","display_name":"entropy (information theory)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/fractal-dimension","display_name":"fractal dimension","score":0.8999999761581421},{"id":"https://openalex.org/keywords/kolmogorov-complexity","display_name":"Kolmogorov complexity","score":0.9929999709129333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6948000192642212},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6887000203132629},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.621999979019165},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.597100019454956},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.503000020980835},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.436599999666214},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3813000023365021},{"id":"https://openalex.org/C26546657","wikidata":"https://www.wikidata.org/wiki/Q1412452","display_name":"Fractal dimension","level":3,"score":0.3411000072956085},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.272599995136261}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:www.doria.fi:10024/189265","doi":null,"is_oa":true,"landing_page_url":"https://www.doria.fi/handle/10024/189265","pdf_url":"https://www.doria.fi/bitstream/10024/189265/2/vatandoust_monire.pdf","source":{"id":"https://openalex.org/S4377196760","display_name":"Doria (University of Helsinki)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I133731052","host_organization_name":"University of Helsinki","host_organization_lineage":["https://openalex.org/I133731052"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Diplomarbete"}],"best_oa_location":{"id":"pmh:oai:www.doria.fi:10024/189265","doi":null,"is_oa":true,"landing_page_url":"https://www.doria.fi/handle/10024/189265","pdf_url":"https://www.doria.fi/bitstream/10024/189265/2/vatandoust_monire.pdf","source":{"id":"https://openalex.org/S4377196760","display_name":"Doria (University of Helsinki)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I133731052","host_organization_name":"University of Helsinki","host_organization_lineage":["https://openalex.org/I133731052"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Diplomarbete"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7115334027.pdf","grobid_xml":"https://content.openalex.org/works/W7115334027.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W43871266","https://openalex.org/W197865394","https://openalex.org/W1498436455","https://openalex.org/W1901129140","https://openalex.org/W1988950592","https://openalex.org/W2022477494","https://openalex.org/W2025768430","https://openalex.org/W2078772598","https://openalex.org/W2100495367","https://openalex.org/W2112796928","https://openalex.org/W2117731089","https://openalex.org/W2125877832","https://openalex.org/W2136922672","https://openalex.org/W2145094598","https://openalex.org/W2163922914","https://openalex.org/W2666784499","https://openalex.org/W2780433069","https://openalex.org/W2892341857","https://openalex.org/W2899142335","https://openalex.org/W2919115771","https://openalex.org/W2941813310","https://openalex.org/W2962949934","https://openalex.org/W2963386266","https://openalex.org/W2963736028","https://openalex.org/W3016302360","https://openalex.org/W3027227991","https://openalex.org/W3034856945","https://openalex.org/W3094482296","https://openalex.org/W3094547167","https://openalex.org/W3109072952","https://openalex.org/W3112652185","https://openalex.org/W3129089864","https://openalex.org/W3133533225","https://openalex.org/W3140854437","https://openalex.org/W3215055741","https://openalex.org/W4205743778","https://openalex.org/W4206307172","https://openalex.org/W4206437855","https://openalex.org/W4243621612","https://openalex.org/W4255546427","https://openalex.org/W4282974630","https://openalex.org/W4294237199","https://openalex.org/W4294723731","https://openalex.org/W4294975018","https://openalex.org/W4322624822","https://openalex.org/W4378418355","https://openalex.org/W4387723962"],"related_works":["https://openalex.org/W3016302360","https://openalex.org/W4417053373","https://openalex.org/W4312364648","https://openalex.org/W2950255552","https://openalex.org/W7163193165","https://openalex.org/W4292794125","https://openalex.org/W3090297001","https://openalex.org/W4390270434","https://openalex.org/W4380685139","https://openalex.org/W4403790838"],"abstract_inverted_index":{"Abstract":[0],"Background:":[1],"Dataset":[2,307],"complexity":[3,34,98,117,157,183,249,274,297],"significantly":[4],"impacts":[5],"the":[6,27,39,47,87,93,108,116,165,182,205,210,227,231,239,248,288],"performance":[7,41,160],"of":[8,29,32,89,96,110,118,131,150,167,184,212,250,290,295],"machine":[9],"learning":[10],"(ML)":[11],"models":[12,258],"and":[13,26,42,57,158,190,222,278,298],"is":[14,35,99,253,284],"influenced":[15],"by":[16],"factors":[17],"such":[18],"as":[19,147],"dataset":[20,33,65,80,97,151,156,171,233,273,296],"size,":[21],"feature":[22],"dimensionality,":[23],"data":[24],"distribution,":[25],"presence":[28],"noises.":[30],"Evaluation":[31],"crucial":[36],"to":[37,43,73,92,163,196,286,299],"optimize":[38],"model\u2019s":[40],"anticipate":[44],"challenges":[45],"during":[46],"process.":[48],"Various":[49],"measures,":[50],"including":[51],"entropy,":[52],"fractal":[53],"dimension,":[54],"Kolmogorov":[55],"complexity,":[56,81,132,308],"neural":[58],"network-based":[59],"approaches":[60],"like":[61],"autoencoders,":[62],"can":[63,179,269],"quantify":[64],"complexity.":[66,152,172,245],"Among":[67],"these,":[68],"Autoencoders":[69],"(AE)":[70],"are":[71],"found":[72],"be":[74],"a":[75,148,188,276],"promising":[76],"method":[77],"for":[78,83,219,230,238,255,259],"evaluating":[79,115],"particularly":[82],"image":[84,119,126,185,251],"datasets.":[85,120],"However,":[86,281],"effectiveness":[88,109,166,289],"AEs":[90,291],"compared":[91,195],"other":[94,197,293],"measures":[95,294],"still":[100],"an":[101],"open":[102],"question.":[103],"Methods:":[104],"This":[105],"study":[106,200],"investigates":[107],"convolutional":[111],"autoencoders":[112],"(CAE)":[113],"in":[114,169,271,303],"Experiments":[121],"were":[122],"conducted":[123],"on":[124,140],"different":[125],"datasets":[127,252],"with":[128,292],"varying":[129],"levels":[130],"using":[133,144],"various":[134,304],"AE":[135],"architectures.":[136],"A":[137],"methodology":[138],"based":[139],"CAE":[141,159,168,224,268],"was":[142,161,234],"developed,":[143],"reconstruction":[145,217],"loss":[146,218,229],"measure":[149],"The":[153,174,199,263],"relationship":[154],"between":[155],"analyzed":[162],"assess":[164],"measuring":[170],"Results:":[173],"findings":[175,265],"indicate":[176],"that":[177,203,267],"CAEs":[178],"effectively":[180],"evaluate":[181],"datasets,":[186,221],"offering":[187],"simpler":[189],"less":[191],"mathematically":[192],"intensive":[193],"approach":[194],"measures.":[198],"also":[201],"suggests":[202],"increasing":[204],"latent":[206],"space":[207],"dimension":[208],"improves":[209],"quality":[211],"reconstructed":[213],"images,":[214],"consistently":[215,235],"reduced":[216],"both":[220],"enhances":[223],"performance.":[225],"Notably,":[226],"validation":[228],"ABOships":[232],"lower":[236,244],"than":[237],"COCO":[240],"dataset,":[241],"indicating":[242],"its":[243],"Conclusion:":[246],"Evaluating":[247],"critical":[254],"developing":[256],"efficient":[257,279],"computer":[260],"vision":[261],"tasks.":[262],"study's":[264],"suggest":[266],"assist":[270],"evaluation":[272],"providing":[275],"reliable":[277],"approach.":[280],"further":[282],"research":[283],"needed":[285],"compare":[287],"investigate":[300],"their":[301],"application":[302],"domains.":[305],"Keywords:":[306],"Convolutional":[309],"Autoencoder,":[310],"Reconstructing":[311],"loss,":[312],"Latent":[313],"Space":[314],"dimensionality":[315]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-12-15T00:00:00"}