{"id":"https://openalex.org/W4404478880","doi":"https://doi.org/10.1145/3686081.3686130","title":"Machine Learning-Driven Industry Index Rebound Prediction: A New Perspective Through Image Analysis","display_name":"Machine Learning-Driven Industry Index Rebound Prediction: A New Perspective Through Image Analysis","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4404478880","doi":"https://doi.org/10.1145/3686081.3686130"},"language":"en","primary_location":{"id":"doi:10.1145/3686081.3686130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3686081.3686130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3686081.3686130?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science &amp; Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3686081.3686130?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"An Zhao","orcid":"https://orcid.org/0009-0005-2964-5413"},"institutions":[{"id":"https://openalex.org/I21642278","display_name":"Ningxia University","ror":"https://ror.org/04j7b2v61","country_code":"CN","type":"education","lineage":["https://openalex.org/I21642278"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"An Zhao","raw_affiliation_strings":["School of Information Engineering, Ningxia University, Yinchuan, Ningxia, China"],"raw_orcid":"https://orcid.org/0009-0005-2964-5413","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Ningxia University, Yinchuan, Ningxia, China","institution_ids":["https://openalex.org/I21642278"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21642278"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"294"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.8060654401779175},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.6738505363464355},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6029208302497864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5097514986991882},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42575889825820923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4036022126674652},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08130493760108948}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.8060654401779175},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.6738505363464355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029208302497864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5097514986991882},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42575889825820923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036022126674652},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08130493760108948}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3686081.3686130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3686081.3686130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3686081.3686130?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science &amp; Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3686081.3686130","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3686081.3686130","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3686081.3686130?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science &amp; Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404478880.pdf","grobid_xml":"https://content.openalex.org/works/W4404478880.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2025053102","https://openalex.org/W2769487452","https://openalex.org/W2897841117","https://openalex.org/W2993491036","https://openalex.org/W3026499749","https://openalex.org/W3039222266","https://openalex.org/W3171718892","https://openalex.org/W4226161317","https://openalex.org/W4291178314","https://openalex.org/W4308733580","https://openalex.org/W4318615394","https://openalex.org/W4388040845","https://openalex.org/W4389829724"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Addressing":[0],"the":[1,5,35,52,57,65,75,103,120,124,130,140],"challenge":[2],"of":[3,7,74],"predicting":[4,51],"trend":[6,55],"industry":[8],"index":[9,125],"net":[10,53],"value,":[11],"this":[12,128],"paper":[13],"introduces":[14],"a":[15,60,82,89],"statistical":[16],"analysis":[17,99],"method":[18,25],"based":[19],"on":[20],"supervised":[21],"learning":[22,110],"algorithms.":[23],"This":[24],"processes":[26],"data":[27,76],"from":[28],"recent":[29],"years":[30],"for":[31,56,77],"indices":[32],"such":[33],"as":[34],"National":[36],"Semiconductor":[37],"Index":[38,42,48],"(980017),":[39],"Public":[40],"Health":[41],"(980016),":[43],"and":[44,88,96,115,144],"New":[45],"Energy":[46],"Battery":[47],"(980032),":[49],"swiftly":[50],"value":[54],"day":[58],"following":[59],"continuous":[61],"decline.":[62],"Upon":[63],"validating":[64],"model,":[66],"it":[67],"was":[68],"discovered":[69],"that":[70,101],"using":[71],"approximately":[72],"20%":[73],"model":[78],"evaluation":[79],"resulted":[80],"in":[81],"linear":[83,131],"mean":[84,132],"square":[85,133],"error":[86,134],"(MSE)":[87,135],"forecast":[90],"growth":[91],"coefficient":[92,105],"ranging":[93],"between":[94],"2.5":[95],"0.6.":[97],"Further":[98],"indicated":[100],"when":[102],"rebound":[104],"is":[106],"at":[107],"0.5,":[108],"machine":[109],"algorithms":[111],"like":[112],"decision":[113],"trees":[114],"random":[116],"forests":[117],"can":[118],"identify":[119],"main":[121],"factors":[122],"influencing":[123],"trends.":[126],"In":[127],"scenario,":[129],"remained":[136],"around":[137],"1.62,":[138],"confirming":[139],"method'":[141],"s":[142],"effectiveness":[143],"practicality.":[145]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
