{"id":"https://openalex.org/W7214732248","doi":"https://doi.org/10.1201/9781042036325-38","title":"AI-Assisted Multicomponent Stress\u2013Strength Reliability Under Adaptive Type-II Hybrid Progressive Censoring Using the Unit Weibull Model","display_name":"AI-Assisted Multicomponent Stress\u2013Strength Reliability Under Adaptive Type-II Hybrid Progressive Censoring Using the Unit Weibull Model","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214732248","doi":"https://doi.org/10.1201/9781042036325-38"},"language":"en","primary_location":{"id":"doi:10.1201/9781042036325-38","doi":"https://doi.org/10.1201/9781042036325-38","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781042036325-38","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence and Sustainable Innovation","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W7214700954","display_name":"Artificial Intelligence and Sustainable Innovation"},"type":"book-chapter","is_traditional":true,"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/A5004091956","display_name":"Mahesh Kumar Panda","orcid":"https://orcid.org/0000-0002-1459-3463","observed_orcids":["https://orcid.org/0000-0002-1459-3463"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahesh Kumar Panda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5153544576","display_name":"Mausumi Dutta","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mausumi Dutta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"249","last_page":"254"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10780","display_name":"Reliability and Maintenance Optimization","score":0.00279999990016222,"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"}},{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/random-removals","display_name":"random removals","score":0.9390000104904175},{"id":"https://openalex.org/keywords/feedforward-neural-networks","display_name":"feedforward neural networks","score":0.9160000085830688},{"id":"https://openalex.org/keywords/highest-posterior-density-intervals","display_name":"highest posterior density intervals","score":0.8460000157356262},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.8169999718666077},{"id":"https://openalex.org/keywords/reliability-function-estimation","display_name":"reliability function estimation","score":0.5299999713897705},{"id":"https://openalex.org/keywords/bathtub-shaped-hazard-rate","display_name":"bathtub-shaped hazard rate","score":0.6710000038146973}],"concepts":[{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.7702000141143799},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.6805999875068665},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.656499981880188},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6485000252723694},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5875999927520752},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5037000179290771},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48510000109672546},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C207103383","wikidata":"https://www.wikidata.org/wiki/Q3930246","display_name":"Hazard ratio","level":3,"score":0.36149999499320984},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33809998631477356},{"id":"https://openalex.org/C121117317","wikidata":"https://www.wikidata.org/wiki/Q4439221","display_name":"Credible interval","level":3,"score":0.32589998841285706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3255999982357025},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C201729545","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability theory","level":3,"score":0.3043999969959259},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2962000072002411},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C205167067","wikidata":"https://www.wikidata.org/wiki/Q3300636","display_name":"Interval estimation","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C44154001","wikidata":"https://www.wikidata.org/wiki/Q754940","display_name":"Mean time between failures","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C199435849","wikidata":"https://www.wikidata.org/wiki/Q3179293","display_name":"Shape parameter","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/9781042036325-38","doi":"https://doi.org/10.1201/9781042036325-38","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781042036325-38","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence and Sustainable Innovation","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3168376913","https://openalex.org/W2912133652","https://openalex.org/W7140421380","https://openalex.org/W4323266481","https://openalex.org/W4211070976","https://openalex.org/W3059004302","https://openalex.org/W4379210655","https://openalex.org/W7161945522","https://openalex.org/W7167671322","https://openalex.org/W3180636119"],"abstract_inverted_index":{"A":[0],"flexible":[1],"lifetime":[2,45],"model":[3],"that":[4,80,122],"can":[5],"accurately":[6],"represent":[7],"a":[8],"variety":[9],"of":[10,36,89,137],"hazard":[11],"rate":[12],"behaviours,":[13],"including":[14],"rising,":[15],"falling,":[16],"and":[17,71,95,108,157,166],"bathtub-shaped":[18],"trends,":[19],"is":[20,63],"the":[21,34,43,86,90,116,141],"unit":[22],"Weibull":[23],"(UW)":[24],"distribution,":[25,46],"which":[26],"has":[27],"two":[28],"shape":[29],"parameters.":[30],"This":[31],"study":[32],"examines":[33],"inference":[35],"multicomponent":[37],"stress-strength":[38],"(MSS)":[39],"system":[40],"reliability":[41,61,91],"using":[42,76],"UW":[44],"utilizing":[47],"an":[48],"adaptive":[49],"Type-II":[50],"hybrid":[51],"progressive":[52],"censoring":[53,167],"method":[54],"with":[55,146],"random":[56],"removals":[57],"(AHPCSBR-TII).":[58],"The":[59],"MSS":[60],"function":[62],"estimated":[64],"through":[65],"both":[66,93,106],"maximum":[67],"likelihood":[68],"(ML)":[69],"estimation":[70],"artificial":[72],"intelligence":[73],"methods,":[74],"particularly":[75,134],"feed-forward":[77],"neural":[78],"networks":[79],"are":[81,103,113],"trained":[82],"via":[83],"backpropagation.":[84],"For":[85],"ML":[87],"estimator":[88],"measure,":[92],"asymptotic":[94,107,132],"Bayesian":[96],"highest":[97],"posterior":[98],"density":[99],"(HPD)":[100],"credible":[101,124],"intervals":[102,112,125],"generated.":[104],"Additionally,":[105],"bootstrap":[109,147],"percentile":[110,148],"confidence":[111],"formulated":[114],"for":[115],"ANN-based":[117],"estimator.":[118],"Simulation":[119],"results":[120],"demonstrate":[121],"HPD":[123],"provide":[126],"enhanced":[127],"coverage":[128,156],"accuracy":[129],"relative":[130],"to":[131],"intervals,":[133],"in":[135],"cases":[136],"significant":[138],"censoring.":[139],"Furthermore,":[140],"ANN":[142],"estimator,":[143],"when":[144],"coupled":[145],"inference,":[149],"displays":[150],"comparable":[151],"efficacy,":[152],"frequently":[153],"producing":[154],"dependable":[155],"more":[158],"concise":[159],"interval":[160],"widths":[161],"as":[162],"sample":[163],"sizes":[164],"expand":[165],"diminishes.":[168]},"counts_by_year":[],"updated_date":"2026-10-10T09:11:00.788812","created_date":"2026-09-30T00:00:00"}
