{"id":"https://openalex.org/W2994774846","doi":"https://doi.org/10.1109/tencon.2019.8929695","title":"Deep convolutional Neural Network in Smart Assistant for Blinds","display_name":"Deep convolutional Neural Network in Smart Assistant for Blinds","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2994774846","doi":"https://doi.org/10.1109/tencon.2019.8929695","mag":"2994774846"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-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/A5101649510","display_name":"Rashmi Kapoor","orcid":"https://orcid.org/0000-0002-0651-485X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rashmi Kapoor","raw_affiliation_strings":["Department of EEEE, VNRVJIET, Hyderabad"],"affiliations":[{"raw_affiliation_string":"Department of EEEE, VNRVJIET, Hyderabad","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034189872","display_name":"M. Bharathi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M.Aruna Bharathi","raw_affiliation_strings":["Dept. of EEE, GCET, Medchal, Hyderabad"],"affiliations":[{"raw_affiliation_string":"Dept. of EEE, GCET, Medchal, Hyderabad","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047162242","display_name":"M. Sushama","orcid":null},"institutions":[{"id":"https://openalex.org/I10874241","display_name":"Jawaharlal Nehru Technological University, Hyderabad","ror":"https://ror.org/002tchr49","country_code":"IN","type":"education","lineage":["https://openalex.org/I10874241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Sushama","raw_affiliation_strings":["Dept. of EE, JNTUHCE, Kukatpally, Hyderabad"],"affiliations":[{"raw_affiliation_string":"Dept. of EE, JNTUHCE, Kukatpally, Hyderabad","institution_ids":["https://openalex.org/I10874241"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101649510"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.181,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58699983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1697","last_page":"1701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9925000071525574,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8204622864723206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7732549905776978},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6062623858451843},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5355154871940613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.531780481338501},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.512208878993988},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47821280360221863},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41817569732666016},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3911179304122925}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8204622864723206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732549905776978},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6062623858451843},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5355154871940613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.531780481338501},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.512208878993988},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47821280360221863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41817569732666016},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3911179304122925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2114449851","https://openalex.org/W2137718414","https://openalex.org/W2159807109","https://openalex.org/W2166949156","https://openalex.org/W6680140823"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Increasing":[0],"pollution":[1],"and":[2,80,118,141,148],"changing":[3],"life":[4,51],"styles":[5],"has":[6,19],"severely":[7],"affected":[8],"human":[9],"health":[10],"specially":[11],"our":[12],"sense":[13],"organs.":[14],"More":[15],"exposure":[16],"to":[17,37,47,64,107,136,152],"screen":[18],"increased":[20],"vision":[21,46,55],"related":[22],"problems":[23],"even":[24,119],"at":[25],"very":[26,44,73,89],"early":[27],"age":[28],"of":[29,82],"life.":[30],"The":[31,78,128],"developing":[32,103],"technologies":[33],"should":[34],"be":[35,62,72,88],"utilized":[36,63],"help":[38,108],"the":[39,126],"persons":[40,94],"with":[41],"no":[42],"or":[43],"less":[45],"lead":[48],"an":[49],"independent":[50],"in":[52,98,110],"society.":[53],"Computer":[54],"is":[56,145],"one":[57],"such":[58],"field":[59],"that":[60,70,144],"can":[61,71,87],"develop":[65],"some":[66],"cost":[67],"effective":[68],"products":[69],"useful":[74,90],"for":[75,91,114],"these":[76],"scenarios.":[77],"detection":[79,140],"recognition":[81,142],"text":[83,139],"from":[84],"natural":[85],"image":[86],"visually":[92],"imparted":[93],"as":[95,97,150],"well":[96],"various":[99],"other":[100],"applications":[101],"like":[102],"a":[104,138],"smart":[105],"system":[106,143],"driver":[109],"getting":[111],"voice":[112],"signal":[113],"every":[115],"road":[116],"sign,":[117],"warning":[120],"if":[121],"we":[122],"did":[123],"not":[124],"follow":[125],"one.":[127],"proposed":[129],"work":[130],"uses":[131],"deep":[132],"convolutional":[133],"neural":[134],"network":[135],"implement":[137],"much":[146],"simpler":[147],"faster":[149],"compare":[151],"traditional":[153],"hand":[154],"crafted":[155],"feature":[156],"based":[157],"methods.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
