• Login
    View Item 
    •   DSpace Home
    • 2-DERGİLER
    • 03) Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
    • Cilt 12, Sayı 3 (2023)
    • View Item
    •   DSpace Home
    • 2-DERGİLER
    • 03) Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
    • Cilt 12, Sayı 3 (2023)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    CNNTuner: Image Classification with A Novel CNN Model Optimized Hyperparameters

    Thumbnail
    View/Open
    Tam Metin/Full Text (1.611Mb)
    Date
    2023
    Author
    ÇETİNER, Halit
    METLEK, Sedat
    Metadata
    Show full item record
    Abstract
    Today, the impact of deep learning in computer vision applications is growing every day. Deep learning techniques apply in many areas such as clothing search, automatic product recommendation. The main task in these applications is to perform the classification process automatically. But, high similarities between multiple apparel objects make classification difficult. In this paper, a new deep learning model based on convolutional neural networks (CNNs) is proposed to solve the classification problem. These networks can extract features from images using convolutional layers, unlike traditional machine learning algorithms. As the extracted features are highly discriminative, good results can be obtained in terms of classification performance. Performance results vary according to the number of filters and window sizes in the convolution layers that extract the features. Considering that there is more than one parameter that influences the performance result, the parameter that gives the best result can be determined after many experimental studies. The specified parameterization process is a difficult and laborious process. To address this issue, the parameters of a newly proposed CNN-based deep learning model were optimized using the Keras Tuner tool on the Fashion MNIST (F-MNIST) dataset containing multi-class fashion images. The performance results of the model were obtained using the data separated according to the cross-validation technique 5. At the same time, to measure the impact of the optimized parameters on classification, the performance results of the proposed model, called CNNTuner, are compared with state-of-the-art (SOTA) studies.
    URI
    http://dspace.beu.edu.tr:8080/xmlui/handle/123456789/14900
    Collections
    • Cilt 12, Sayı 3 (2023) [31]





    Creative Commons License
    DSpace@BEU by Bitlis Eren University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     




    | Yönerge | Rehber | İletişim |

    sherpa/romeo

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV