{"id":"https://openalex.org/W3127666337","doi":"https://doi.org/10.1109/ickii50300.2020.9318975","title":"User Authentication Through Pen Tablet Data Using Imputation and Flatten Function","display_name":"User Authentication Through Pen Tablet Data Using Imputation and Flatten Function","publication_year":2020,"publication_date":"2020-08-21","ids":{"openalex":"https://openalex.org/W3127666337","doi":"https://doi.org/10.1109/ickii50300.2020.9318975","mag":"3127666337"},"language":"en","primary_location":{"id":"doi:10.1109/ickii50300.2020.9318975","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii50300.2020.9318975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"202020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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/A5074311237","display_name":"Md. Azim Hossain Akash","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131323","display_name":"University of Asia Pacific","ror":"https://ror.org/03dk4hf38","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210131323"]}],"countries":["BD"],"is_corresponding":true,"raw_author_name":"Md. Azim Hossain Akash","raw_affiliation_strings":["University of Asia Pacific, Dhaka, Bangladesh"],"affiliations":[{"raw_affiliation_string":"University of Asia Pacific, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I4210131323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016918201","display_name":"Nasima Begum","orcid":"https://orcid.org/0000-0002-7155-5121"},"institutions":[{"id":"https://openalex.org/I4210131323","display_name":"University of Asia Pacific","ror":"https://ror.org/03dk4hf38","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210131323"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Nasima Begum","raw_affiliation_strings":["University of Asia Pacific, Dhaka, Bangladesh"],"affiliations":[{"raw_affiliation_string":"University of Asia Pacific, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I4210131323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076752778","display_name":"Sayma Rahman","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131323","display_name":"University of Asia Pacific","ror":"https://ror.org/03dk4hf38","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210131323"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Sayma Rahman","raw_affiliation_strings":["University of Asia Pacific, Dhaka, Bangladesh"],"affiliations":[{"raw_affiliation_string":"University of Asia Pacific, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I4210131323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005221038","display_name":"Jungpil Shin","orcid":"https://orcid.org/0000-0002-7476-2468"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jungpil Shin","raw_affiliation_strings":["School of Computer Science and Engineering, University of Aizu, Wakamatsu, Fukushima, Japan"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Aizu, Wakamatsu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074158275","display_name":"Md Amiruzzaman","orcid":"https://orcid.org/0000-0002-2292-5798"},"institutions":[{"id":"https://openalex.org/I149910238","display_name":"Kent State University","ror":"https://ror.org/049pfb863","country_code":"US","type":"education","lineage":["https://openalex.org/I149910238"]},{"id":"https://openalex.org/I13805885","display_name":"Vaughn College of Aeronautics and Technology","ror":"https://ror.org/056e22e24","country_code":"US","type":"education","lineage":["https://openalex.org/I13805885"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Amiruzzaman","raw_affiliation_strings":["College of Aeronautics and Engineering, Kent State University, USA"],"affiliations":[{"raw_affiliation_string":"College of Aeronautics and Engineering, Kent State University, USA","institution_ids":["https://openalex.org/I13805885","https://openalex.org/I149910238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755654","display_name":"Md. Rashedul Islam","orcid":"https://orcid.org/0000-0001-8676-6338"},"institutions":[{"id":"https://openalex.org/I4210131323","display_name":"University of Asia Pacific","ror":"https://ror.org/03dk4hf38","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210131323"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Md Rashedul Islam","raw_affiliation_strings":["University of Asia Pacific, Dhaka, Bangladesh"],"affiliations":[{"raw_affiliation_string":"University of Asia Pacific, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I4210131323"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5074311237"],"corresponding_institution_ids":["https://openalex.org/I4210131323"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.43784029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"208","last_page":"211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9932000041007996,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.991100013256073,"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/computer-science","display_name":"Computer science","score":0.7977663278579712},{"id":"https://openalex.org/keywords/handwriting","display_name":"Handwriting","score":0.7801293134689331},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7051370143890381},{"id":"https://openalex.org/keywords/handwriting-recognition","display_name":"Handwriting recognition","score":0.6336348056793213},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5683912634849548},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.56479811668396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5128605365753174},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5037607550621033},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.48911601305007935},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.47156426310539246},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44786304235458374},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43782278895378113},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3306630253791809},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23963162302970886},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.22630006074905396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7977663278579712},{"id":"https://openalex.org/C2779386606","wikidata":"https://www.wikidata.org/wiki/Q2393642","display_name":"Handwriting","level":2,"score":0.7801293134689331},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7051370143890381},{"id":"https://openalex.org/C112640561","wikidata":"https://www.wikidata.org/wiki/Q2440634","display_name":"Handwriting recognition","level":3,"score":0.6336348056793213},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5683912634849548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.56479811668396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5128605365753174},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5037607550621033},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.48911601305007935},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.47156426310539246},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44786304235458374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43782278895378113},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3306630253791809},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23963162302970886},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.22630006074905396},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickii50300.2020.9318975","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii50300.2020.9318975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"202020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5799999833106995,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1575402736","https://openalex.org/W2062219129","https://openalex.org/W2074812954","https://openalex.org/W2105571589","https://openalex.org/W2116570169","https://openalex.org/W2909407238","https://openalex.org/W6676793307"],"related_works":["https://openalex.org/W3003949997","https://openalex.org/W3199359807","https://openalex.org/W2110485610","https://openalex.org/W3047607512","https://openalex.org/W4390983538","https://openalex.org/W2744690920","https://openalex.org/W2787081548","https://openalex.org/W183832189","https://openalex.org/W2536878212","https://openalex.org/W3005534356"],"abstract_inverted_index":{"Identifying":[0],"a":[1,4,9,65,114,143],"user":[2,45,78,115],"or":[3,25,38],"person":[5,66],"through":[6,80],"handwriting-data":[7],"is":[8,58,198],"popular":[10],"technique.":[11],"Many":[12],"researches":[13],"have":[14],"been":[15],"done":[16],"in":[17],"this":[18,41,154],"area":[19],"most":[20],"of":[21,35,64,142,192,221],"which":[22,217],"are":[23,125,135,149],"image":[24,37],"pattern-based":[26],"analysis.":[27],"The":[28,55,132,146,188,205],"accuracy":[29,86,186,190],"level":[30],"depends":[31],"on":[32,117],"the":[33,36,61,71,96,139,180,219],"quality":[34],"pattern.":[39],"In":[40,95],"paper,":[42],"we":[43,156,210],"proposed":[44,56,97],"authentication":[46,79],"system":[47,57],"using":[48],"an":[49],"individual's":[50],"pen":[51,73,81],"tablet":[52,75,82],"handwriting":[53,67,119],"data.":[54,94],"concerned":[59,137],"with":[60,89,138],"numerical":[62],"value":[63],"data":[68,83,128],"getting":[69],"from":[70],"digital":[72],"and":[74,105,130,174,196,202,214],"device.":[76],"Hence,":[77],"ensures":[84],"more":[85,212],"by":[87],"working":[88],"user's":[90,144],"real":[91],"time":[92],"handwritten":[93],"system,":[98],"24":[99],"persons":[100],"writing":[101],"samples(1262":[102],".csv":[103],"files":[104],"23":[106],"class)are":[107],"used":[108,150],"for":[109,151],"extracting":[110],"features":[111,124,134,148],"to":[112],"identify":[113],"based":[116],"their":[118],"attributes.":[120],"Six":[121],"completely":[122],"separated":[123],"extracted":[126,133,147],"after":[127],"analysis":[129,207],"pre-processing.":[131],"mainly":[136],"vital":[140],"attributes":[141],"handwriting.":[145],"classification.":[152],"With":[153],"concern,":[155],"utilized":[157],"different":[158,182,185],"classification":[159],"algorithms":[160,183],"such":[161],"as":[162],"Support":[163],"Vector":[164],"Machine":[165],"(SVM),":[166],"Logistic":[167],"Regression":[168],"(LR),":[169],"Linear":[170],"Discriminant":[171],"Analysis":[172],"(LDA)":[173],"Random":[175],"Forest":[176],"(RF)":[177],"classifier.":[178],"From":[179],"implementation,":[181],"show":[184],"level.":[187],"testing":[189],"rate":[191],"SVM,":[193],"LR,":[194],"LDA":[195],"RF":[197],"87%,":[199],"85%,":[200],"76%":[201],"77%":[203],"respectively.":[204],"experimental":[206],"shows":[208],"that":[209],"got":[211],"robust":[213],"satisfactory":[215],"results":[216],"ensure":[218],"practicality":[220],"our":[222],"system.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
