{"id":"https://openalex.org/W4414184175","doi":"https://doi.org/10.32604/cmc.2025.068351","title":"Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na&lt;sub&gt;&lt;b&gt;1/2&lt;/b&gt;&lt;/sub&gt;Bi&lt;sub&gt;&lt;b&gt;1/2&lt;/b&gt;&lt;/sub&gt;TiO&lt;sub&gt;&lt;b&gt;3&lt;/b&gt;&lt;/sub&gt;-Based Ceramic Capacitors","display_name":"Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na&lt;sub&gt;&lt;b&gt;1/2&lt;/b&gt;&lt;/sub&gt;Bi&lt;sub&gt;&lt;b&gt;1/2&lt;/b&gt;&lt;/sub&gt;TiO&lt;sub&gt;&lt;b&gt;3&lt;/b&gt;&lt;/sub&gt;-Based Ceramic Capacitors","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414184175","doi":"https://doi.org/10.32604/cmc.2025.068351"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.068351","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068351","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.068351","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032581581","display_name":"Shige Wang","orcid":"https://orcid.org/0000-0002-7639-6035"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shige Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yalong Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yalong Liang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063287185","display_name":"Liang Huang","orcid":"https://orcid.org/0000-0003-3233-4446"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100439828","display_name":"Pei Li","orcid":"https://orcid.org/0000-0001-6275-1342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei Li","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032581581"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22724342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"2","first_page":"2729","last_page":"2748"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.8065000176429749,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.8065000176429749,"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/T12676","display_name":"Machine Learning and ELM","score":0.7795000076293945,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.7250000238418579,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/cuckoo-search","display_name":"Cuckoo search","score":0.6287999749183655},{"id":"https://openalex.org/keywords/capacitor","display_name":"Capacitor","score":0.6202999949455261},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.578499972820282},{"id":"https://openalex.org/keywords/dielectric","display_name":"Dielectric","score":0.5566999912261963},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5385000109672546},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.42250001430511475},{"id":"https://openalex.org/keywords/energy-storage","display_name":"Energy storage","score":0.40799999237060547},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.38679999113082886},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.3522000014781952}],"concepts":[{"id":"https://openalex.org/C117241572","wikidata":"https://www.wikidata.org/wiki/Q5192379","display_name":"Cuckoo search","level":3,"score":0.6287999749183655},{"id":"https://openalex.org/C52192207","wikidata":"https://www.wikidata.org/wiki/Q5322","display_name":"Capacitor","level":3,"score":0.6202999949455261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C133386390","wikidata":"https://www.wikidata.org/wiki/Q184996","display_name":"Dielectric","level":2,"score":0.5566999912261963},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4465000033378601},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C73916439","wikidata":"https://www.wikidata.org/wiki/Q837718","display_name":"Energy storage","level":3,"score":0.40799999237060547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3970000147819519},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.3781999945640564},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C123299182","wikidata":"https://www.wikidata.org/wiki/Q190837","display_name":"Hysteresis","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C98576551","wikidata":"https://www.wikidata.org/wiki/Q841798","display_name":"Electric potential energy","level":3,"score":0.33660000562667847},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C134132462","wikidata":"https://www.wikidata.org/wiki/Q45621","display_name":"Ceramic","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2919999957084656},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C2777949652","wikidata":"https://www.wikidata.org/wiki/Q5445429","display_name":"Ferroelectric ceramics","level":4,"score":0.26820001006126404},{"id":"https://openalex.org/C172100665","wikidata":"https://www.wikidata.org/wiki/Q7465774","display_name":"Thermal conduction","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C2776810535","wikidata":"https://www.wikidata.org/wiki/Q26381","display_name":"Cuckoo","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2529999911785126}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.32604/cmc.2025.068351","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068351","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},{"id":"pmh:oai:sdu.dk:publications/b5d4a534-e0ff-4c0d-9684-95b01192f1d0","is_oa":true,"landing_page_url":"https://portal.findresearcher.sdu.dk/da/publications/b5d4a534-e0ff-4c0d-9684-95b01192f1d0","pdf_url":null,"source":{"id":"https://openalex.org/S4306400423","display_name":"University of Southern Denmark Research Portal (University of Southern Denmark)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177969490","host_organization_name":"University of Southern Denmark","host_organization_lineage":["https://openalex.org/I177969490"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Wang, S, Liang, Y, Huang, L & Li, P 2025, 'Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na 1/2 Bi 1/2 TiO 3 -Based Ceramic Capacitors', Computers, Materials and Continua, vol. 85, no. 2, pp. 2729-2748. https://doi.org/10.32604/cmc.2025.068351","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:sdu.dk:openaire/b5d4a534-e0ff-4c0d-9684-95b01192f1d0","is_oa":true,"landing_page_url":"https://portal.findresearcher.sdu.dk/files/297218954/TSP_CMC_68351.pdf","pdf_url":"https://findresearcher.sdu.dk/ws/files/297218954/TSP_CMC_68351.pdf","source":{"id":"https://openalex.org/S4306400423","display_name":"University of Southern Denmark Research Portal (University of Southern Denmark)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177969490","host_organization_name":"University of Southern Denmark","host_organization_lineage":["https://openalex.org/I177969490"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Wang, S, Liang, Y, Huang, L & Li, P 2025, 'Cuckoo Search-Deep Neural Network Hybrid Model for Uncertainty Quantification and Optimization of Dielectric Energy Storage in Na 1/2 Bi 1/2 TiO 3 -Based Ceramic Capacitors', Computers, Materials and Continua, vol. 85, no. 2, pp. 2729-2748. https://doi.org/10.32604/cmc.2025.068351","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.068351","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068351","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1976492731","https://openalex.org/W1983587962","https://openalex.org/W1992985800","https://openalex.org/W2003890325","https://openalex.org/W2006083616","https://openalex.org/W2021663838","https://openalex.org/W2030663413","https://openalex.org/W2061675197","https://openalex.org/W2117363206","https://openalex.org/W2122317693","https://openalex.org/W2168745915","https://openalex.org/W2177528407","https://openalex.org/W2347129741","https://openalex.org/W2471982001","https://openalex.org/W2782634521","https://openalex.org/W2971440179","https://openalex.org/W3132825880","https://openalex.org/W3138233895","https://openalex.org/W4214823391","https://openalex.org/W4281766857","https://openalex.org/W4283760207","https://openalex.org/W4304690568","https://openalex.org/W4368368185","https://openalex.org/W4386844631","https://openalex.org/W4389991792","https://openalex.org/W4391719889","https://openalex.org/W4396698062","https://openalex.org/W4399802948","https://openalex.org/W4406272443","https://openalex.org/W4406539888","https://openalex.org/W4407624004","https://openalex.org/W4408387333","https://openalex.org/W4410717147"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"a":[3,79,88,171,205],"hybrid":[4],"Cuckoo":[5],"Search-Deep":[6],"Neural":[7],"Network":[8],"(CS-DNN)":[9],"model":[10,94],"for":[11,151,209],"uncertainty":[12,123],"quantification":[13,124],"and":[14,34,51,57,65,86,113,193],"composition":[15,173],"optimization":[16],"of":[17,27,197,212],"Na1/2Bi1/2TiO3":[18],"(NBT)-based":[19],"dielectric":[20,98,102,105,214],"energy":[21,59,110,223],"storage":[22,60,224],"ceramics.":[23],"Addressing":[24],"the":[25,74,128,157,162,179,186,194,201],"limitations":[26],"traditional":[28],"ferroelectric":[29],"materials\u2014such":[30],"as":[31],"hysteresis":[32],"loss":[33,106],"low":[35],"breakdown":[36],"strength":[37],"under":[38,161],"high":[39],"electric":[40],"fields\u2014we":[41],"fabricate":[42],"(1":[43],"\u2212":[44],"x)NBBT8-xBMT":[45],"solid":[46],"solutions":[47],"via":[48],"chemical":[49],"modification":[50],"systematically":[52],"investigate":[53],"their":[54],"temperature":[55],"stability":[56],"composition-dependent":[58],"performance":[61],"through":[62,216],"XRD,":[63],"SEM,":[64],"electrical":[66],"characterization.":[67],"The":[68],"key":[69],"innovation":[70],"lies":[71],"in":[72,84,139,175,220],"integrating":[73],"CS":[75],"metaheuristic":[76],"algorithm":[77],"with":[78,127],"DNN,":[80],"overcoming":[81],"local":[82],"minima":[83],"training":[85],"establishing":[87],"robust":[89],"composition-property":[90],"prediction":[91,144],"framework.":[92],"Our":[93],"accurately":[95],"predicts":[96],"room-temperature":[97],"constant":[99,103],"(\u03b5r),":[100],"maximum":[101],"(\u03b5max),":[104],"(tan":[107],"\u03b4),":[108],"discharge":[109],"density":[111],"(Wrec),":[112],"charge-discharge":[114],"efficiency":[115],"(\u03b7)":[116],"from":[117],"compositional":[118],"inputs.":[119],"A":[120],"Monte":[121],"Carlo-based":[122],"framework,":[125],"combined":[126],"3\u03c3":[129,163],"statistical":[130],"criterion,":[131],"demonstrates":[132],"that":[133],"CS-DNN":[134,202],"outperforms":[135],"conventional":[136],"DNN":[137],"models":[138],"three":[140],"critical":[141],"aspects:":[142],"Higher":[143],"accuracy":[145],"(R2":[146],"=":[147,191],"0.9717":[148],"vs.":[149],"0.9382":[150],"\u03b5max);":[152],"Tighter":[153],"error":[154],"distribution,":[155],"satisfying":[156],"99.7%":[158],"confidence":[159],"interval":[160],"principle;":[164],"Enhanced":[165],"robustness,":[166],"maintaining":[167],"stable":[168],"predictions":[169],"across":[170],"25%":[172],"span":[174],"generalization":[176,181],"tests.":[177],"While":[178],"model\u2019s":[180],"is":[182],"constrained":[183],"by":[184],"both":[185],"limited":[187],"experimental":[188],"dataset":[189],"(n":[190],"45)":[192],"underlying":[195],"assumptions":[196],"MC-based":[198],"data":[199],"augmentation,":[200],"framework":[203],"establishes":[204],"machine":[206],"learning-guided":[207],"paradigm":[208],"accelerated":[210],"discovery":[211],"high-temperature":[213],"capacitors":[215],"its":[217],"unique":[218],"capability":[219],"quantifying":[221],"composition-level":[222],"uncertainties.":[225]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
