{"id":"https://openalex.org/W4282970011","doi":"https://doi.org/10.1080/0952813x.2022.2084566","title":"Chaotic whale-atom search optimization-based deep stacked auto encoder for crowd behaviour recognition","display_name":"Chaotic whale-atom search optimization-based deep stacked auto encoder for crowd behaviour recognition","publication_year":2022,"publication_date":"2022-06-15","ids":{"openalex":"https://openalex.org/W4282970011","doi":"https://doi.org/10.1080/0952813x.2022.2084566"},"language":"en","primary_location":{"id":"doi:10.1080/0952813x.2022.2084566","is_oa":false,"landing_page_url":"https://doi.org/10.1080/0952813x.2022.2084566","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental &amp; Theoretical Artificial Intelligence","raw_type":"journal-article"},"type":"article","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/A5034218707","display_name":"Juginder Pal Singh","orcid":"https://orcid.org/0000-0003-1967-9546"},"institutions":[{"id":"https://openalex.org/I82571370","display_name":"GLA University","ror":"https://ror.org/05fnxgv12","country_code":"IN","type":"education","lineage":["https://openalex.org/I82571370"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Juginder Pal Singh","raw_affiliation_strings":["Computer Engineering &amp; Applications, GLA University, Mathura India"],"raw_orcid":"https://orcid.org/0000-0003-1967-9546","affiliations":[{"raw_affiliation_string":"Computer Engineering &amp; Applications, GLA University, Mathura India","institution_ids":["https://openalex.org/I82571370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100335586","display_name":"Manoj Kumar","orcid":"https://orcid.org/0000-0001-6909-6424"},"institutions":[{"id":"https://openalex.org/I82571370","display_name":"GLA University","ror":"https://ror.org/05fnxgv12","country_code":"IN","type":"education","lineage":["https://openalex.org/I82571370"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manoj Kumar","raw_affiliation_strings":["Computer Engineering &amp; Applications, GLA University, Mathura India"],"raw_orcid":"https://orcid.org/0000-0001-6909-6424","affiliations":[{"raw_affiliation_string":"Computer Engineering &amp; Applications, GLA University, Mathura India","institution_ids":["https://openalex.org/I82571370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5034218707"],"corresponding_institution_ids":["https://openalex.org/I82571370"],"apc_list":null,"apc_paid":null,"fwci":0.9185,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78138735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"36","issue":"2","first_page":"187","last_page":"211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7955036163330078},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7506928443908691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7308584451675415},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6393904685974121},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.599381148815155},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5925970673561096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.58584064245224},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5190420746803284},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5082249641418457},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48749566078186035},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33385729789733887}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7955036163330078},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7506928443908691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7308584451675415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6393904685974121},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.599381148815155},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5925970673561096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.58584064245224},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5190420746803284},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5082249641418457},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48749566078186035},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33385729789733887},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/0952813x.2022.2084566","is_oa":false,"landing_page_url":"https://doi.org/10.1080/0952813x.2022.2084566","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental &amp; Theoretical Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1962468782","https://openalex.org/W1969914528","https://openalex.org/W2003968027","https://openalex.org/W2057285138","https://openalex.org/W2079023123","https://openalex.org/W2153275382","https://openalex.org/W2248550502","https://openalex.org/W2274407394","https://openalex.org/W2571410400","https://openalex.org/W2581926493","https://openalex.org/W2592767432","https://openalex.org/W2617113099","https://openalex.org/W2668061517","https://openalex.org/W2765633310","https://openalex.org/W2765729126","https://openalex.org/W2770131355","https://openalex.org/W2782136875","https://openalex.org/W2784204938","https://openalex.org/W2791019495","https://openalex.org/W2801165650","https://openalex.org/W2807174997","https://openalex.org/W2808205569","https://openalex.org/W2884689635","https://openalex.org/W2889545660","https://openalex.org/W2891290365","https://openalex.org/W2913662717","https://openalex.org/W2915334726","https://openalex.org/W2936081713","https://openalex.org/W2943979008","https://openalex.org/W2963837866","https://openalex.org/W2964014730","https://openalex.org/W3088283184","https://openalex.org/W3106011443","https://openalex.org/W3126976006","https://openalex.org/W4212858312","https://openalex.org/W4235032559","https://openalex.org/W4248880341","https://openalex.org/W7014191107"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4321789545"],"abstract_inverted_index":{"The":[0,13,53,135],"activity":[1,16,106],"recognition":[2,17],"gained":[3],"immense":[4],"popularity":[5],"due":[6],"to":[7,19,58,63,88],"increasing":[8],"number":[9],"of":[10,15,26,61,111,167,184,187,193],"surveillance":[11],"cameras.":[12],"purpose":[14],"is":[18,47,114,118,120,139,155],"detect":[20],"the":[21,24,30,59,65,70,77,93,99,109,146,149,176,181,196],"actions":[22],"from":[23],"series":[25],"examination":[27],"by":[28,122,174],"varying":[29],"environmental":[31],"condition.":[32],"In":[33,74],"this":[34,75,178],"paper,":[35],"Chaotic":[36,130],"Whale":[37,131],"Atom":[38,124],"Search":[39],"Optimisation":[40],"(CWASO)-based":[41],"Deep":[42],"stacked":[43,101],"autoencoder":[44],"(CWASO-Deep":[45],"SAE)":[46,104],"proposed":[48,136],"for":[49,105,157,195],"crowd":[50],"behaviour":[51],"recognition.":[52],"key":[54],"frames":[55],"are":[56,86,96],"subjected":[57],"descriptor":[60],"feature":[62],"extort":[64],"features,":[66,79],"which":[67],"bring":[68],"out":[69],"classifier":[71],"input":[72],"vector.":[73],"model,":[76],"statistical":[78],"optical":[80],"flow":[81],"features":[82,85,95],"and":[83,129,163,170,189],"visual":[84],"conducted":[87],"extract":[89],"important":[90],"features.":[91],"Furthermore,":[92],"significant":[94],"shown":[97],"in":[98],"deep":[100,112],"auto-encoder":[102],"(Deep":[103],"recognition,":[107],"as":[108],"guidance":[110],"SAE":[113],"performed":[115],"byCWASO,":[116],"that":[117,154],"planned":[119],"designed":[121],"adjoining":[123],"search":[125],"optimisation":[126,132],"(ASO)":[127],"algorithm":[128,133],"(CWOA).":[134],"systems\u2019":[137],"performance":[138,153],"analysed":[140],"using":[141],"two":[142],"datasets.":[143],"By":[144],"considering":[145,175],"training":[147],"data,":[148],"projected":[150],"method":[151,179],"attains":[152,180],"high":[156],"dataset-1":[158],"with":[159,164,190],"maximum":[160,182],"precision,":[161],"sensitivity,":[162],"specific":[165,191],"value":[166],"96.826%,":[168],"96.790%,":[169],"99.395%,":[171],"respectively.":[172],"Similarly,":[173],"K-Fold,":[177],"precision":[183],"96.897%,":[185],"sensitivity":[186],"96.885%,":[188],"values":[192],"97.245%":[194],"dataset-1.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
