{"id":"https://openalex.org/W4416252370","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227579","title":"Novel AI Frameworks for ESG Performance Prediction: ESG-RAG and SSA-iLSTM","display_name":"Novel AI Frameworks for ESG Performance Prediction: ESG-RAG and SSA-iLSTM","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252370","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227579"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5040365728","display_name":"Liangyu Zhao","orcid":"https://orcid.org/0009-0002-1572-3407"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyan Zhao","raw_affiliation_strings":["Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036451396","display_name":"Xin Tang","orcid":"https://orcid.org/0000-0003-1443-3766"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tang","raw_affiliation_strings":["Southern University of Science and Technology,National Center for Applied Mathematics Shenzhen,Shenzhen,China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,National Center for Applied Mathematics Shenzhen,Shenzhen,China,518055","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101200166","display_name":"Yongqiang Ma","orcid":"https://orcid.org/0000-0002-6063-5601"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongqiang Ma","raw_affiliation_strings":["Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101748860","display_name":"Wei Huang","orcid":"https://orcid.org/0000-0002-4315-8487"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Huang","raw_affiliation_strings":["Southern University of Science and Technology,National Center for Applied Mathematics Shenzhen,Shenzhen,China,518055"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,National Center for Applied Mathematics Shenzhen,Shenzhen,China,518055","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047405956","display_name":"Nanning Zheng","orcid":"https://orcid.org/0000-0003-1608-8257"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanning Zheng","raw_affiliation_strings":["Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong University,State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics,Xi’an,China,710049","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.45453588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.08730000257492065,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.08730000257492065,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.058400001376867294,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14413","display_name":"Advanced Technologies in Various Fields","score":0.050700001418590546,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5073000192642212},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4431999921798706},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4203000068664551},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4169999957084656},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4065999984741211},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4065000116825104},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.37790000438690186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459999918937683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5960000157356262},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5073000192642212},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44519999623298645},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212000072002411},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C39389867","wikidata":"https://www.wikidata.org/wiki/Q380767","display_name":"Corporate governance","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C2779433544","wikidata":"https://www.wikidata.org/wiki/Q1435226","display_name":"Focal point","level":3,"score":0.2791000008583069},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2000982976","https://openalex.org/W2019900743","https://openalex.org/W2025727209","https://openalex.org/W3003405602","https://openalex.org/W3036494589","https://openalex.org/W3216306687","https://openalex.org/W4220755019","https://openalex.org/W4281296357","https://openalex.org/W4310117749","https://openalex.org/W4310747947","https://openalex.org/W4367598556","https://openalex.org/W4376566946","https://openalex.org/W4378907409","https://openalex.org/W4381051247","https://openalex.org/W4384821897","https://openalex.org/W4387378152","https://openalex.org/W4389339114","https://openalex.org/W4389523787","https://openalex.org/W4390776100","https://openalex.org/W4391376033","https://openalex.org/W4396758761","https://openalex.org/W4400912354","https://openalex.org/W4403525331","https://openalex.org/W4404117823","https://openalex.org/W4404371137","https://openalex.org/W4410394620"],"related_works":[],"abstract_inverted_index":{"Environmental,":[0],"Social,":[1],"and":[2,18,32,47,59,110,133,164,171],"Governance":[3],"(ESG)":[4],"issues":[5],"are":[6],"becoming":[7],"a":[8,20,25,43,98],"focal":[9],"point":[10],"in":[11,55,177],"global":[12],"investment":[13,199],"decision-making.":[14],"However,":[15],"accurately":[16],"analyzing":[17],"predicting":[19,178],"company\u2019s":[21],"ESG":[22,36,90,179,194],"performance":[23],"remains":[24],"complex":[26],"challenge":[27],"due":[28],"to":[29,92],"the":[30,74,84,119,125,137,144],"fragmented":[31],"heterogeneous":[33],"nature":[34],"of":[35,86],"data.":[37],"Traditional":[38],"approaches":[39],"often":[40],"rely":[41],"on":[42],"single":[44],"predictive":[45],"model":[46],"conventional":[48],"time":[49],"series":[50],"techniques,":[51],"which":[52],"face":[53],"limitations":[54],"handling":[56],"low-frequency,":[57],"discontinuous,":[58],"unstructured":[60],"data":[61,94],"while":[62,128],"lacking":[63],"standardized":[64],"processing":[65,134],"methods.":[66],"To":[67],"address":[68],"these":[69],"challenges,":[70],"this":[71],"study":[72],"proposes":[73],"ESG-RAG":[75,170],"(Retrieval-Augmented":[76],"Generation)":[77],"framework":[78,117],"for":[79],"in-depth":[80],"information":[81],"mining,":[82],"enabling":[83,149],"extraction":[85],"implicit":[87],"insights":[88],"from":[89,146,160],"reports":[91],"alleviate":[93],"sparsity":[95],"issues.":[96],"Additionally,":[97],"novel":[99],"temporal":[100,122],"prediction":[101,186],"framework,":[102],"SSA-iLSTM":[103,172],"(Singular":[104],"Spectrum":[105],"Analysis":[106],"integrated":[107],"with":[108,136,192],"iTransformer":[109,126],"Long":[111],"Short-Term":[112],"Memory),":[113],"is":[114],"introduced.":[115],"The":[116],"processes":[118],"target":[120],"variable\u2019s":[121],"signals":[123],"through":[124],"module":[127],"decomposing":[129],"covariates":[130],"using":[131,158],"SSA":[132],"them":[135],"LSTM":[138],"model.":[139],"A":[140],"cross-attention":[141],"mechanism":[142],"fuses":[143],"outputs":[145],"both":[147],"modules,":[148],"dynamic":[150],"feature":[151],"integration":[152],"across":[153],"multiple":[154],"variables.":[155],"Experimental":[156],"results":[157],"datasets":[159],"Wind":[161],"(Chinese":[162],"companies)":[163,167],"Refinitiv":[165],"(US":[166],"demonstrate":[168],"that":[169],"significantly":[173],"outperform":[174],"baseline":[175],"models":[176],"performance.":[180],"This":[181],"approach":[182],"not":[183],"only":[184],"enhances":[185],"accuracy":[187],"but":[188],"also":[189],"provides":[190],"investors":[191],"valuable":[193],"insights,":[195],"supporting":[196],"more":[197],"informed":[198],"decisions.":[200]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}