{"id":"https://openalex.org/W2904214450","doi":"https://doi.org/10.1109/ist.2018.8577193","title":"Object Analysis Using Machine Learning to Solve Inverse Problem in Electrical Impedance Tomography","display_name":"Object Analysis Using Machine Learning to Solve Inverse Problem in Electrical Impedance Tomography","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2904214450","doi":"https://doi.org/10.1109/ist.2018.8577193","mag":"2904214450"},"language":"en","primary_location":{"id":"doi:10.1109/ist.2018.8577193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist.2018.8577193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Imaging Systems and Techniques (IST)","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/A5066003040","display_name":"Tomasz Rymarczyk","orcid":"https://orcid.org/0000-0002-3524-9151"},"institutions":[{"id":"https://openalex.org/I2799788900","display_name":"University of Economics and Innovation","ror":"https://ror.org/012a85e51","country_code":"PL","type":"education","lineage":["https://openalex.org/I2799788900"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Tomasz Rymarczyk","raw_affiliation_strings":["Research and Development Center, Netrix S.A., Lublin, University of Economics and Innovation in Lublin Lublin, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Center, Netrix S.A., Lublin, University of Economics and Innovation in Lublin Lublin, Poland","institution_ids":["https://openalex.org/I2799788900"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073447410","display_name":"Edward Koz\u0142owski","orcid":"https://orcid.org/0000-0002-7147-4903"},"institutions":[{"id":"https://openalex.org/I8264552","display_name":"Lublin University of Technology","ror":"https://ror.org/024zjzd49","country_code":"PL","type":"education","lineage":["https://openalex.org/I8264552"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Edward Kozlowski","raw_affiliation_strings":["Faculty of Management Lublin University of Technology Lublin, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Management Lublin University of Technology Lublin, Poland","institution_ids":["https://openalex.org/I8264552"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070349657","display_name":"Grzegorz K\u0142osowski","orcid":"https://orcid.org/0000-0001-7927-3674"},"institutions":[{"id":"https://openalex.org/I8264552","display_name":"Lublin University of Technology","ror":"https://ror.org/024zjzd49","country_code":"PL","type":"education","lineage":["https://openalex.org/I8264552"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Grzegorz Klosowski","raw_affiliation_strings":["Faculty of Management Lublin University of Technology Lublin, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Management Lublin University of Technology Lublin, Poland","institution_ids":["https://openalex.org/I8264552"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.505,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.68006508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T12537","display_name":"Flow Measurement and Analysis","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9587000012397766,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/estimator","display_name":"Estimator","score":0.6498774290084839},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6021863222122192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.556236207485199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5246572494506836},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.5226709246635437},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5111010670661926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5094519257545471},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.49158844351768494},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.48798006772994995},{"id":"https://openalex.org/keywords/least-squares-support-vector-machine","display_name":"Least squares support vector machine","score":0.4590839445590973},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43566960096359253},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.42352184653282166},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41130390763282776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4054740369319916},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37582749128341675},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2681383490562439},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23024144768714905}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6498774290084839},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6021863222122192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.556236207485199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5246572494506836},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.5226709246635437},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5111010670661926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5094519257545471},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.49158844351768494},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.48798006772994995},{"id":"https://openalex.org/C145828037","wikidata":"https://www.wikidata.org/wiki/Q17086219","display_name":"Least squares support vector machine","level":3,"score":0.4590839445590973},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43566960096359253},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.42352184653282166},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41130390763282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4054740369319916},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37582749128341675},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2681383490562439},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23024144768714905},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist.2018.8577193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist.2018.8577193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Imaging Systems and Techniques (IST)","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":33,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W1983053523","https://openalex.org/W1992852838","https://openalex.org/W2022311890","https://openalex.org/W2029780747","https://openalex.org/W2041556066","https://openalex.org/W2042333563","https://openalex.org/W2075324846","https://openalex.org/W2085456730","https://openalex.org/W2086166908","https://openalex.org/W2087216940","https://openalex.org/W2103122975","https://openalex.org/W2130129486","https://openalex.org/W2138886665","https://openalex.org/W2171033594","https://openalex.org/W2465765781","https://openalex.org/W2474031912","https://openalex.org/W2487770199","https://openalex.org/W2525835059","https://openalex.org/W2559216438","https://openalex.org/W2567369551","https://openalex.org/W2577829094","https://openalex.org/W2745725932","https://openalex.org/W2747768591","https://openalex.org/W2748264057","https://openalex.org/W2748423362","https://openalex.org/W2756102342","https://openalex.org/W2789664003","https://openalex.org/W2805890534","https://openalex.org/W2963204665","https://openalex.org/W3123944501","https://openalex.org/W6671934890","https://openalex.org/W6679497484"],"related_works":["https://openalex.org/W1928301487","https://openalex.org/W1963674083","https://openalex.org/W2140039100","https://openalex.org/W2151220638","https://openalex.org/W2725829804","https://openalex.org/W2357762447","https://openalex.org/W2386661643","https://openalex.org/W2370845081","https://openalex.org/W1968036192","https://openalex.org/W2062491388"],"abstract_inverted_index":{"Strongly":[0],"correlated":[1],"predictive":[2],"variables":[3],"in":[4,90,169],"linear":[5],"models":[6],"make":[7],"it":[8,135],"difficult":[9],"to":[10,31,37,51,56,63,124,203],"determine":[11,64],"the":[12,19,42,47,52,58,65,68,91,95,104,121,130,133,139,153,158,161,173,187,200,204],"exact":[13],"influence":[14],"of":[15,25,49,60,67,93,120,129,155,175,189,206],"these":[16],"predictors":[17],"on":[18,46,77],"dependent":[20],"variable":[21],"(output).":[22],"The":[23,117,149],"use":[24],"a":[26,73,163],"simple":[27],"least":[28],"squares":[29],"method":[30,154,165],"estimate":[32],"unknown":[33],"parameters":[34,50],"may":[35],"lead":[36],"an":[38,126],"erroneous":[39],"prediction.":[40],"Adding":[41],"penalty":[43],"factor":[44],"depending":[45],"number":[48],"least-squares":[53],"criterion":[54],"allows":[55],"reduce":[57],"variance":[59],"estimators":[61,66],"and":[62,86,109,193],"load.":[69],"This":[70,183],"article":[71],"proposes":[72],"new":[74],"solution":[75],"based":[76],"machine":[78],"learning":[79],"methods,":[80],"which":[81,170,177],"enabled":[82],"obtaining":[83],"more":[84],"accurate":[85,127],"stable":[87],"reconstruction":[88,128],"results":[89],"process":[92],"solving":[94],"inverse":[96],"problem.":[97],"Image":[98],"reconstructions":[99],"were":[100],"carried":[101],"out":[102,157],"using":[103],"ElasticNET,":[105],"least-angle":[106],"regression":[107],"(LARS)":[108],"ElasticNET":[110,171],"artificial":[111],"neural":[112,191],"networks":[113,192],"(ANN)":[114],"hybrid":[115,164],"algorithms.":[116],"main":[118],"task":[119],"tomography":[122],"is":[123,151,178],"perform":[125],"image.":[131],"During":[132,160],"measurements":[134],"was":[136,166],"found":[137],"that":[138],"measured":[140],"values":[141],"from":[142],"some":[143],"electrode":[144],"pairs":[145],"are":[146],"strongly":[147],"correlated.":[148],"reason":[150],"usually":[152],"carrying":[156],"measurement.":[159],"research,":[162],"also":[167],"presented,":[168],"reduces":[172],"vector":[174],"predictors,":[176],"then":[179],"processed":[180],"by":[181],"ANN.":[182],"approach":[184],"speeds":[185],"up":[186],"processes":[188],"training":[190],"image":[194],"reconstruction,":[195],"as":[196,198],"well":[197],"makes":[199],"system":[201],"immune":[202],"noise":[205],"input":[207],"data.":[208]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
