Innovative solutions for convolutional neural network performance: A TRIZ-based reverse engineering approach

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dc.contributor.authorCosgun, Merve
dc.contributor.authorAltun, Koray
dc.date.accessioned2026-02-08T15:11:19Z
dc.date.available2026-02-08T15:11:19Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractConvolutional neural networks (CNNs) are widely used in computer vision for tasks like image classification and detection. These models work well when the number of image classes is small, but as the number of classes increases, accuracy tends to drop due to overfitting. There are several methods to address this issue, such as data augmentation, preprocessing, class weighting, transfer learning, and adjusting technical parameters. This study introduces a novel approach utilizing the theory of inventive problem-solving (TRIZ) methodology to systematically analyze and enhance these existing methods. Using reverse engineering, we deconstructed current solutions and aligned them with TRIZ principles to propose more innovative and effective approaches for improving CNN performance. The results show that TRIZ provides a structured and creative framework for solving accuracy decline issues in CNN models, offering the potential for broader applications in other machine learning architectures. © 2025, Society of Sytematic Innovation. All rights reserved.
dc.identifier.doi10.6977/IJoSI.202506_9(3).0001
dc.identifier.endpage7
dc.identifier.issn2077-7973
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105014113714
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.6977/IJoSI.202506_9(3).0001
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5363
dc.identifier.volume9
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSociety of Sytematic Innovation
dc.relation.ispartofInternational Journal of Systematic Innovation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectConvolutional Neural Network
dc.subjectImage Classification
dc.subjectReverse Engineering
dc.subjectTheory of Inventive Problem Solving
dc.titleInnovative solutions for convolutional neural network performance: A TRIZ-based reverse engineering approach
dc.typeArticle

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