A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models

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dc.authoridAljibawi, Mayas/0000-0003-2864-5540
dc.authoridMohammed, Mazin Abed/0000-0001-9030-8102
dc.authoridShang, Wen-Long/0000-0002-9162-901X
dc.authoridMartinek, Radek/0000-0003-2054-143X
dc.authoridAbdulkareem, Karrar Hameed/0000-0001-7302-2049
dc.authoridSubhi, Mohammed/0000-0002-5387-6247
dc.authorwosidAljibawi, Mayas/AFZ-7084-2022
dc.authorwosidMohammed, Mazin Abed/E-3910-2018
dc.authorwosidShang, Wen-Long/AAC-7166-2021
dc.authorwosidMartinek, Radek/Q-3601-2017
dc.authorwosidAbdulkareem, Karrar Hameed/V-1741-2017
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorSubhi, Mohammed Ahmed
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAljibawi, Mayas
dc.contributor.authorNedoma, Jan
dc.contributor.authorMartinek, Radek
dc.contributor.authorDeveci, Muhammet
dc.date.accessioned2024-05-19T14:41:41Z
dc.date.available2024-05-19T14:41:41Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIncreases in population and prosperity are linked to a worldwide rise in garbage. The classification and recycling of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new twolayer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes - cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decisionmakers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.en_US
dc.description.sponsorshipEuropean Union under the REFRESH -Research Excellence For REgion Sustainability and High-tech Industries project [CZ.10.03.01/00/22_003/0000048]; Ministry of Education, Youth and Sports of the Chezk Republic [SP2024/059, SP2024/081]en_US
dc.description.sponsorshipThis article was co -funded by the European Union under the REFRESH -Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition. Also, this work was supported by the Ministry of Education, Youth and Sports of the Chezk Republic conducted by VSB -Technical University of Ostrava, Czechia under Grants SP2024/059 and SP2024/081.en_US
dc.identifier.doi10.1016/j.engappai.2024.107926
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85183853825en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2024.107926
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5144
dc.identifier.volume132en_US
dc.identifier.wosWOS:001171337600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFusionen_US
dc.subjectBenchmarkingen_US
dc.subjectDeep Learningen_US
dc.subjectInception-Xceptionen_US
dc.subjectWaste Sortingen_US
dc.subjectEntropyen_US
dc.titleA manifold intelligent decision system for fusion and benchmarking of deep waste-sorting modelsen_US
dc.typeArticleen_US

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