{"id":"https://openalex.org/W3032426652","doi":"https://doi.org/10.1145/3383972.3384017","title":"Urban Residential Land Price Appraisal via Quantifying Impact Factors Based on Deep Belief Networks","display_name":"Urban Residential Land Price Appraisal via Quantifying Impact Factors Based on Deep Belief Networks","publication_year":2020,"publication_date":"2020-02-15","ids":{"openalex":"https://openalex.org/W3032426652","doi":"https://doi.org/10.1145/3383972.3384017","mag":"3032426652"},"language":"en","primary_location":{"id":"doi:10.1145/3383972.3384017","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3384017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","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/A5036036924","display_name":"Hua Ai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117337","display_name":"Neijiang Normal University","ror":"https://ror.org/02bc8tz70","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Ai","raw_affiliation_strings":["School of Literature, Neijiang Normal University, Neijiang, Sichuan, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Literature, Neijiang Normal University, Neijiang, Sichuan, P.R. China","institution_ids":["https://openalex.org/I4210117337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409520","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0003-1123-6193"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100756716","display_name":"Yuxin Jiang","orcid":"https://orcid.org/0000-0002-8388-7262"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Jiang","raw_affiliation_strings":["School of Economics, Hefei University of Technology, Hefei, Anhui, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics, Hefei University of Technology, Hefei, Anhui, P.R. China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109506594","display_name":"Jing He","orcid":"https://orcid.org/0000-0001-6867-4122"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing He","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7157,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.59551448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"29","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9750000238418579,"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":0.9750000238418579,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9664999842643738,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9373000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.5247129201889038},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5200552344322205},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.5017049312591553},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.48292800784111023},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.48102647066116333},{"id":"https://openalex.org/keywords/land-price","display_name":"Land price","score":0.47061091661453247},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.46142369508743286},{"id":"https://openalex.org/keywords/environmental-economics","display_name":"Environmental economics","score":0.40680399537086487},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3019631505012512},{"id":"https://openalex.org/keywords/agricultural-economics","display_name":"Agricultural economics","score":0.2647194266319275},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2389460802078247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19968783855438232},{"id":"https://openalex.org/keywords/civil-engineering","display_name":"Civil engineering","score":0.18840402364730835},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16459956765174866},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10792693495750427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5247129201889038},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5200552344322205},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.5017049312591553},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.48292800784111023},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.48102647066116333},{"id":"https://openalex.org/C2993127880","wikidata":"https://www.wikidata.org/wiki/Q512599","display_name":"Land price","level":2,"score":0.47061091661453247},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.46142369508743286},{"id":"https://openalex.org/C134560507","wikidata":"https://www.wikidata.org/wiki/Q753291","display_name":"Environmental economics","level":1,"score":0.40680399537086487},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3019631505012512},{"id":"https://openalex.org/C48824518","wikidata":"https://www.wikidata.org/wiki/Q396340","display_name":"Agricultural economics","level":1,"score":0.2647194266319275},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2389460802078247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19968783855438232},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.18840402364730835},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16459956765174866},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10792693495750427},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3383972.3384017","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3384017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W2107091340","https://openalex.org/W2183341477","https://openalex.org/W2202505358","https://openalex.org/W2359803630","https://openalex.org/W2374733472","https://openalex.org/W2377141711","https://openalex.org/W2774057992"],"related_works":["https://openalex.org/W2585432886","https://openalex.org/W2165991108","https://openalex.org/W3082895349","https://openalex.org/W2565516711","https://openalex.org/W4385544042","https://openalex.org/W3004069267","https://openalex.org/W4327774331","https://openalex.org/W3136021864","https://openalex.org/W2572334665","https://openalex.org/W2248239756"],"abstract_inverted_index":{"The":[0,49],"relationship":[1],"between":[2],"urban":[3,27,45,106],"residential":[4,28,46,75,107],"land":[5,29,47,76,108],"prices":[6,77,109],"and":[7,85,88,114],"the":[8,22,34,57,94,115],"explanatory":[9],"variables":[10],"is":[11,18,101],"highly":[12],"complex.":[13],"This":[14,31],"causes":[15],"that":[16],"it":[17],"difficult":[19],"to":[20,55,92,104],"quantify":[21],"impact":[23,42,73,112],"factors":[24,43],"for":[25,40],"appraising":[26],"prices.":[30,48],"paper":[32],"explores":[33],"use":[35],"of":[36,44,70],"Deep":[37],"Belief":[38],"Networks":[39],"quantifying":[41],"proposed":[50],"approach":[51],"applies":[52],"grid":[53],"cells":[54],"express":[56],"samples":[58],"finely":[59],"458":[60],"features":[61],"are":[62,78,90],"extracted":[63],"as":[64,83],"input":[65],"from":[66],"collected":[67],"raw":[68],"data":[69],"37":[71],"important":[72],"factors,":[74,113],"divided":[79],"into":[80],"9":[81],"levels":[82],"output,":[84],"both":[86],"BBRBM":[87],"GBRBM":[89],"utilized":[91],"form":[93],"network.":[95],"A":[96],"deep":[97],"belief":[98],"network":[99],"model":[100],"finally":[102],"employed":[103],"appraise":[105],"via":[110],"their":[111],"average":[116],"accuracy":[117],"can":[118],"achieve":[119],"about":[120],"90%.":[121]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}