Evaluating the Deep Learning Software Tools for Large-Scale Enterprises Using a Novel Todiffa-Mcdm Framework

dc.authorid Pamucar, Dragan/0000-0001-8522-1942
dc.authorid Simic, Vladimir/0000-0001-5709-3744
dc.authorid Gligoric, Milos/0000-0002-1745-3765
dc.authorscopusid 31267527600
dc.authorscopusid 57194545622
dc.authorscopusid 26221765900
dc.authorscopusid 54080216100
dc.authorscopusid 7005545253
dc.authorscopusid 56382942700
dc.authorwosid Görçün, Ömer/ABG-9628-2020
dc.authorwosid Simic, Vladimir/B-8837-2011
dc.authorwosid Pamucar, Dragan/AAG-8288-2019
dc.contributor.author Gligoric, Zoran
dc.contributor.author Görçün, Ömer Faruk
dc.contributor.author Gorcun, Omer Faruk
dc.contributor.author Gligoric, Milos
dc.contributor.author Pamucar, Dragan
dc.contributor.author Simic, Vladimir
dc.contributor.author Kucukonder, Hande
dc.contributor.other Business Administration
dc.date.accessioned 2024-06-23T21:39:24Z
dc.date.available 2024-06-23T21:39:24Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp [Gligoric, Zoran; Gligoric, Milos] Univ Belgrade, Fac Min & Geol, Belgrade 11000, Serbia; [Gorcun, Omer Faruk] Kadir Has Univ, Dept Business Adm, Cibali Av Kadir Has St Fatih, TR-34083 Istanbul, Turkiye; [Pamucar, Dragan] Univ Belgrade, Fac Org Sci, Dept Operat Res & Stat, Jove Ilica 154, Belgrade 11000, Serbia; [Simic, Vladimir] Univ Belgrade, Fac Transport & Traff Engn, Vojvode Stepe 305, Belgrade 11010, Serbia; [Kucukonder, Hande] Bartin Univ, Fac Econ & Adm Sci, Dept Numer Methods, Bartin, Turkiye en_US
dc.description Pamucar, Dragan/0000-0001-8522-1942; Simic, Vladimir/0000-0001-5709-3744; Gligoric, Milos/0000-0002-1745-3765 en_US
dc.description.abstract Deep learning (DL) is one of the most promising technological developments emerging in the fourth industrial revolution era for businesses to improve processes, increase efficiency, and reduce errors. Accordingly, hierarchical learning software selection is one of the most critical decision-making problems in integrating neural network applications into business models. However, selecting appropriate reinforcement learning software for integrating deep learning applications into enterprises' business models takes much work for decision-makers. There are several reasons for this: first, practitioners' limited knowledge and experience of DL makes it difficult for decision-makers to adapt this technology into their enterprises' business model and significantly increases complex uncertainties. Secondly, according to the authors' knowledge, no study in the literature addresses deep structured learning solutions with the help of MCDM approaches. Consequently, making inferences concerning criteria that should be considered in an evaluation process is impossible by considering the studies in the relevant literature. Considering these gaps, this study presents a novel decision-making approach developed by the authors. It involves the combination of two new decision-making approaches, MAXC (MAXimum of Criterion) and TODIFFA (the total differential of alternative), which were developed to solve current decision-making problems. When the most important advantages of this model are considered, it associates objective and subjective approaches and eliminates some critical limitations of these methodologies. Besides, it has an easily followable algorithm without the need for advanced mathematical knowledge for practitioners and provides highly stable and reliable results in solving complex decision-making problems. Another novelty of the study is that the criteria are determined with a long-term negotiation process that is part of comprehensive fieldwork with specialists. When the conclusions obtained using this model are briefly reviewed, the C2 "Data Availability and Quality" criterion is the most influential in selecting deep learning software. The C7 "Time Constraints" criterion follows the most influential factor. Remarkably, prior research has overlooked the correlation between the performance of Deep Learning (DL) platforms and the quality and accessibility of data. The findings of this study underscore the necessity for DL platform developers to devise solutions to enable DL platforms to operate effectively, notwithstanding the availability of clean, high-quality, and adequate data. Finally, the robustness check carried out to test the validity of the proposed model confirms the accuracy and robustness of the results obtained by implementing the suggested model. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.jksuci.2024.102079
dc.identifier.issn 1319-1578
dc.identifier.issn 2213-1248
dc.identifier.issue 5 en_US
dc.identifier.scopus 2-s2.0-85195065345
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.jksuci.2024.102079
dc.identifier.volume 36 en_US
dc.identifier.wos WOS:001261064000001
dc.identifier.wosquality Q1
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Journal of King Saud University - Computer and Information Sciences en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 0
dc.subject Deep learning (DL) en_US
dc.subject Large-scale industries en_US
dc.subject MAXC (MAXimum of Criterion) en_US
dc.subject TODIFFA (the total differential of alternative) en_US
dc.subject DL software selection en_US
dc.title Evaluating the Deep Learning Software Tools for Large-Scale Enterprises Using a Novel Todiffa-Mcdm Framework en_US
dc.type Article en_US
dc.wos.citedbyCount 1
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