{"id":"https://openalex.org/W4362654597","doi":"https://doi.org/10.1109/access.2023.3264605","title":"Compressive Wavelet Domain Deep CNN for Image Classification Using Genetic Algorithm Based Sensing Mask Learning","display_name":"Compressive Wavelet Domain Deep CNN for Image Classification Using Genetic Algorithm Based Sensing Mask Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4362654597","doi":"https://doi.org/10.1109/access.2023.3264605"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3264605","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3264605","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10092889.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10092889.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010511885","display_name":"Baba Fakruddin Ali B H","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Baba Fakruddin Ali B. H","raw_affiliation_strings":["School of Electronics Engineering, Vellore Institute of Technology, Vellore, India","School of Electronics Engineering, Vellore Institute of Technology, Vellore, INDIA"],"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, India","institution_ids":["https://openalex.org/I876193797"]},{"raw_affiliation_string":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, INDIA","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058024543","display_name":"Prakash Ramachandran","orcid":"https://orcid.org/0000-0003-4579-8880"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prakash Ramachandran","raw_affiliation_strings":["School of Electronics Engineering, Vellore Institute of Technology, Vellore, India","School of Electronics Engineering, Vellore Institute of Technology, Vellore, INDIA"],"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, India","institution_ids":["https://openalex.org/I876193797"]},{"raw_affiliation_string":"School of Electronics Engineering, Vellore Institute of Technology, Vellore, INDIA","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010511885"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5157,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78248031,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"35567","last_page":"35578"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9987000226974487,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.991100013256073,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.713851809501648},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6479737758636475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6423419713973999},{"id":"https://openalex.org/keywords/haar-wavelet","display_name":"Haar wavelet","score":0.6299898028373718},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5698809027671814},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5330938696861267},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5178056955337524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5157954692840576},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.48139357566833496},{"id":"https://openalex.org/keywords/hadamard-transform","display_name":"Hadamard transform","score":0.4339168071746826},{"id":"https://openalex.org/keywords/haar","display_name":"Haar","score":0.42044901847839355},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4116491675376892},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.40820443630218506},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4060470759868622},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34239673614501953},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33993715047836304},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.2584696412086487}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.713851809501648},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6479737758636475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6423419713973999},{"id":"https://openalex.org/C2780423554","wikidata":"https://www.wikidata.org/wiki/Q766198","display_name":"Haar wavelet","level":5,"score":0.6299898028373718},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5698809027671814},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5330938696861267},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5178056955337524},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5157954692840576},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.48139357566833496},{"id":"https://openalex.org/C60292330","wikidata":"https://www.wikidata.org/wiki/Q1014065","display_name":"Hadamard transform","level":2,"score":0.4339168071746826},{"id":"https://openalex.org/C187029792","wikidata":"https://www.wikidata.org/wiki/Q2179112","display_name":"Haar","level":3,"score":0.42044901847839355},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4116491675376892},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.40820443630218506},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4060470759868622},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34239673614501953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33993715047836304},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.2584696412086487},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3264605","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3264605","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10092889.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3028df6d31d84a509f54d2564c1f9910","is_oa":true,"landing_page_url":"https://doaj.org/article/3028df6d31d84a509f54d2564c1f9910","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 35567-35578 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3264605","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3264605","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10092889.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4362654597.pdf","grobid_xml":"https://content.openalex.org/works/W4362654597.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1828462930","https://openalex.org/W1956120499","https://openalex.org/W1973207880","https://openalex.org/W1984584561","https://openalex.org/W2126131432","https://openalex.org/W2133665775","https://openalex.org/W2517723848","https://openalex.org/W2548198050","https://openalex.org/W2552465432","https://openalex.org/W2597747080","https://openalex.org/W2698813923","https://openalex.org/W2770149305","https://openalex.org/W2786977213","https://openalex.org/W2911737132","https://openalex.org/W2962676454","https://openalex.org/W2981613960","https://openalex.org/W2982853315","https://openalex.org/W3003695885","https://openalex.org/W3089552252","https://openalex.org/W3096831136","https://openalex.org/W3176049912","https://openalex.org/W4284712900","https://openalex.org/W4285065952","https://openalex.org/W4290671759","https://openalex.org/W6729487588","https://openalex.org/W6748692466"],"related_works":["https://openalex.org/W4361795924","https://openalex.org/W984746159","https://openalex.org/W2766849256","https://openalex.org/W1523827626","https://openalex.org/W1999916501","https://openalex.org/W2963278128","https://openalex.org/W2087469844","https://openalex.org/W4288279671","https://openalex.org/W4232769230","https://openalex.org/W2005569824"],"abstract_inverted_index":{"Using":[0],"a":[1,8,51,163,240],"novel":[2],"Genetic":[3],"Algorithm-based":[4],"Compressive":[5],"Learning":[6],"(GACL),":[7],"compressed":[9],"domain-learning":[10],"framework":[11,243,353],"is":[12,15,47,95,154,176,189,200,217,225,246,279,304,316,340,411,423,430],"proposed":[13],"that":[14,151,222,281,413],"implemented":[16],"on":[17,43,110,156,227,232],"the":[18,25,44,61,64,70,76,84,99,121,137,144,183,204,207,220,223,236,244,257,266,269,282,293,328,338,372,414,427,431,435],"Haar":[19],"wavelet":[20,116,126,159],"approximation":[21,65,117,127,160],"coefficient":[22,66,118,161],"images":[23,34,67,86,129,162,206,229,250,256,271,274],"of":[24,60,63,103,115,120,131,136,166,171,323,330,337,347],"standard":[26],"kaggle":[27],"RGB":[28],"cat":[29],"dog":[30],"dataset":[31,46],"with":[32,198],"every":[33],"resized":[35],"to":[36,73,97,203,212,219,262,265,298,311,349,380,425],"256x256x3.":[37],"The":[38,124,147,385],"compressive":[39,111,241,351],"sensing":[40,112],"(CS)":[41,113],"measurements":[42,114],"selected":[45,122],"achieved":[48,174],"by":[49,56,289],"using":[50,75,182,248,254,325,333],"simple":[52],"reduced":[53],"pixel":[54,358],"scheme":[55],"retaining":[57],"only":[58,133,202,334],"P%":[59],"pixels":[62,72],"and":[68,83,168,178,214,230,251,272,308,343,368,374,382,409,417],"forcing":[69],"remaining":[71],"0":[74],"Primitive":[77],"Walsh":[78],"Hadamard":[79],"(PWH)":[80],"binary":[81],"mask":[82],"masked":[85,233,249,255],"are":[87,130,173,275],"used":[88,305,317,400,424],"for":[89,434],"further":[90],"learning.":[91],"A":[92],"numerical":[93,148,192],"experiment":[94,149,193],"conducted":[96],"analyze":[98],"image":[100,119,339],"classification":[101],"performance":[102,436],"deep":[104],"convolution":[105],"neural":[106],"network":[107],"(DCNN)":[108],"learning":[109,153,242,352],"dataset.":[123],"unmasked":[125,158,228],"coefficients":[128],"size":[132],"one":[134],"fourth":[135],"original":[138,145],"image,":[139],"but":[140],"they":[141],"visually":[142],"resembles":[143],"image.":[146,187],"shows":[150],"when":[152,195,252,300,313,421],"done":[155,226],"this":[157,215,321],"training":[164,224,294,331,367],"accuracy":[165,170,209,259,295,332],"97%":[167],"validation":[169,208,258],"77%":[172],"which":[175,292,354,404,429],"remarkable":[177,342],"as":[179,181,345,401,406],"good":[180],"complete":[184],"spatial":[185],"domain":[186],"It":[188],"found":[190],"from":[191],"that,":[194],"PWH":[196,284],"masking":[197],"P=10":[199],"applied":[201],"test":[205,273],"falls":[210],"up":[211,261,297,310],"58%":[213],"fall":[216],"due":[218,264],"fact":[221,267],"tested":[231,253],"images.":[234],"On":[235],"other":[237],"hand":[238],"in":[239,291,306,318,320,366,377],"DCNN":[245],"trained":[247,270],"rises":[260],"62%":[263],"both":[268,365],"masked.":[276],"Further,":[277],"it":[278,410],"demonstrated":[280,412],"best":[283],"masks":[285],"can":[286],"be":[287],"learned":[288],"GACL":[290,307,324,422],"increases":[296,309],"89%":[299],"vertical":[301],"cross":[302],"over":[303],"96%":[312,329],"diagonal":[314,326],"crossover":[315],"GACL.":[319],"case":[322],"crossover,":[327],"(10/4=2.5)":[335],"2.5%":[336],"very":[341,356,360],"stand":[344],"proof":[346],"concept":[348],"implement":[350],"need":[355],"less":[357,361],"thus":[359],"measurement":[362],"rate":[363],"(MR)":[364],"testing":[369],"phase":[370],"minimizing":[371],"bandwidth":[373],"storing":[375],"requirement":[376],"applications":[378],"related":[379],"IoT":[381],"cloud":[383],"solutions.":[384],"average":[386,393,415,418],"SSIM":[387,416],"(":[388,395],"<italic":[389,396],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[390,397],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">S<sub>avg</sub></i>":[391],")and":[392],"PSNR":[394,419],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">PSNR<sub>avg</sub></i>":[398],")are":[399],"quality":[402],"measurements,":[403],"reduce":[405],"P":[407],"reduces,":[408],"improves":[420],"learn":[426],"mask,":[428],"key":[432],"reason":[433],"improvement.":[437]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
