{"id":"https://openalex.org/W7133341644","doi":"https://doi.org/10.48550/arxiv.2603.00931","title":"Learning to Weigh Waste: A Physics-Informed Multimodal Fusion Framework and Large-Scale Dataset for Commercial and Industrial Applications","display_name":"Learning to Weigh Waste: A Physics-Informed Multimodal Fusion Framework and Large-Scale Dataset for Commercial and Industrial Applications","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133341644","doi":"https://doi.org/10.48550/arxiv.2603.00931"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00931","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00931","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00931","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081556145","display_name":"Md. Adnanul Islam","orcid":"https://orcid.org/0000-0002-0278-7738"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Islam, Md. Adnanul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117758008","display_name":"Wasimul Karim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karim, Wasimul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035991911","display_name":"Md. Mahbub Alam","orcid":"https://orcid.org/0000-0001-7937-8556"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alam, Md Mahbub","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046997559","display_name":"S M Asif Ur Rahman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Subhey Sadi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127994529","display_name":"Md. Abdur Rahman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Md. Abdur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083784672","display_name":"Arefin Ittesafun Abian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abian, Arefin Ittesafun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055795711","display_name":"Mohaimenul Azam Khan Raiaan","orcid":"https://orcid.org/0009-0006-4793-5382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raiaan, Mohaimenul Azam Khan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080142991","display_name":"Kheng Cher Yeo","orcid":"https://orcid.org/0000-0002-0453-3248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeo, Kheng Cher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020105069","display_name":"Deepika Mathur","orcid":"https://orcid.org/0000-0001-6247-7996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mathur, Deepika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062716310","display_name":"Sami Azam","orcid":"https://orcid.org/0000-0001-7572-9750"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azam, Sami","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.2685999870300293,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.2685999870300293,"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"}},{"id":"https://openalex.org/T11108","display_name":"Municipal Solid Waste Management","score":0.16779999434947968,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing 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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.07199999690055847,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5738999843597412},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.538100004196167},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48249998688697815},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.47609999775886536},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4359000027179718},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4275999963283539},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.40369999408721924},{"id":"https://openalex.org/keywords/logarithm","display_name":"Logarithm","score":0.3804999887943268},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.375900000333786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.620199978351593},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5738999843597412},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.538100004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5336999893188477},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48249998688697815},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3953000009059906},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3596000075340271},{"id":"https://openalex.org/C2777742833","wikidata":"https://www.wikidata.org/wiki/Q1964083","display_name":"Reciprocal","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00931","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00931","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00931","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00931","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5493561029434204,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"weight":[1,46,88,150],"estimation":[2,15],"of":[3,185],"commercial":[4],"and":[5,25,59,76,85,104,111,124,137,181,204,234],"industrial":[6],"waste":[7,45,83,212],"is":[8],"important":[9],"for":[10,101,109,192,210,244],"efficient":[11],"operations,":[12],"yet":[13],"image-based":[14],"remains":[16],"difficult":[17],"because":[18],"similar-looking":[19],"objects":[20,139,194],"may":[21],"have":[22],"different":[23],"densities,":[24],"the":[26,132,148,154,162,165,196,214],"visible":[27],"size":[28],"changes":[29],"with":[30,51,116,200,218],"camera":[31,57,60],"distance.":[32],"Addressing":[33],"this":[34],"problem,":[35],"we":[36,152,222],"propose":[37],"Multimodal":[38],"Weight":[39],"Predictor":[40],"(MWP)":[41],"framework":[42],"that":[43,121],"estimates":[44],"by":[47],"combining":[48,114],"RGB":[49],"images":[50],"physics-informed":[52],"metadata,":[53],"including":[54],"object":[55],"dimensions,":[56],"distance,":[58],"height.":[61],"We":[62],"also":[63],"introduce":[64],"Waste-Weight-10K,":[65],"a":[66,86,98,105,224,235],"real-world":[67],"dataset":[68,80],"containing":[69],"10,421":[70],"synchronized":[71],"image-metadata":[72],"collected":[73],"from":[74,90],"logistics":[75],"recycling":[77],"sites.":[78],"The":[79,187],"covers":[81],"11":[82],"categories":[84],"wide":[87,149],"range":[89,199,217],"3.5":[91],"to":[92,140,239],"3,450":[93],"kg.":[94],"Our":[95],"model":[96,133,155,188,238],"uses":[97],"Vision":[99],"Transformer":[100],"visual":[102,123],"features":[103],"dedicated":[106],"metadata":[107],"encoder":[108],"geometric":[110],"category":[112],"information,":[113],"them":[115],"Stacked":[117],"Mutual":[118],"Attention":[119],"Fusion":[120],"allows":[122],"physical":[125],"cues":[126],"guide":[127],"each":[128,245],"other.":[129],"This":[130],"helps":[131],"manage":[134],"perspective":[135],"effects":[136],"link":[138],"material":[141],"properties.":[142],"To":[143],"ensure":[144],"stable":[145],"performance":[146,209],"across":[147],"range,":[151],"train":[153],"using":[156,229],"Mean":[157,171,176],"Squared":[158],"Logarithmic":[159],"Error.":[160],"On":[161],"test":[163],"set,":[164],"proposed":[166],"method":[167],"achieves":[168],"88.06":[169],"kg":[170,198,202,216],"Absolute":[172,177],"Error":[173,179],"(MAE),":[174],"6.39%":[175],"Percentage":[178],"(MAPE),":[180],"an":[182],"R2":[183],"coefficient":[184],"0.9548.":[186],"shows":[189],"strong":[190],"accuracy":[191],"light":[193],"in":[195,213],"0-100":[197],"2.38":[201],"MAE":[203],"3.1%":[205],"MAPE,":[206],"maintaining":[207],"reliable":[208],"heavy":[211],"1000-2000":[215],"11.1%":[219],"MAPE.":[220],"Finally,":[221],"incorporate":[223],"physically":[225],"grounded":[226],"explanation":[227],"module":[228],"Shapley":[230],"Additive":[231],"Explanations":[232],"(SHAP)":[233],"large":[236],"language":[237],"provide":[240],"clear,":[241],"human-readable":[242],"explanations":[243],"prediction.":[246]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}