COMPARISON OF WELL-KNOWN CONVOLUTIONAL NEURAL NETWORKS IMAGE CLASSIFICATION MODELS ON THE CIFAR-10 DATASET / (Kayıt no. 291575)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03191nam a22002777a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20231027160910.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 231027d2023 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3165 |
| Cutter no | A46 2023 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Ahed, Umar Bindir |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | COMPARISON OF WELL-KNOWN CONVOLUTIONAL NEURAL NETWORKS IMAGE CLASSIFICATION MODELS ON THE CIFAR-10 DATASET / |
| Sorumluluk Bildirimi | UMAR BINDIR AHMED; SUPERVISOR: ASSOC. PROF. DR. UMAR ÖZGÜNALP |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | xi, 62 sheets; |
| Boyutları | 31 cm. |
| Birlikteki Materyal | 1 CD-ROM |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical and Electronics Engineering Department |
| 504 ## - BİBLİYOGRAFİ NOTU | |
| Bibliyografi Notu | Includes bibliography (sheets 60-62) |
| 520 ## - ÖZET NOTU | |
| Özet notu | ABSTRACT<br/>In this research, a supervised learning method is used to assess the performance of <br/>well-known deep convolutional neural network models for image classification on the <br/>CIFAR-10 dataset. VGGNet, GoogLeNet, ResNet, and AlexNet are the four models <br/>that were trained and evaluated using a variety of measures, including loss and <br/>accuracy.<br/>ResNet and VGGNet obtained the greatest test accuracy of 78.15% when comparing <br/>the performances of deep convolutional neural network models during training and <br/>testing. AlexNet and GoogleNet came in second and third, with the highest test <br/>accuracy of 77.5% and 74.41%, respectively. These outcomes suggest that the deep <br/>convolutional neural network models in a supervised method are capable of <br/>successfully categorizing CIFAR-10 images with reasonably small loss values, as <br/>shown in Table 4.1.<br/>The findings underscore the significance of meticulous planning and execution of <br/>training and testing methodologies for deep convolutional neural networks in <br/>supervised learning scenarios to efficiently exploit data with annotations. One may <br/>investigate various strategies, including data augmentation, regularization, and <br/>learning rate scheduling, to enhance the effectiveness of the models. <br/>The result of this investigation emphasizes the importance of precise preparation and <br/>implementation of training and testing procedures in deep convolutional neural <br/>networks in supervised learning in order to maximize their capacity for learning. <br/>Further investigation is necessary to tackle the limitations and challenges that occur <br/>with maximizing the use of data with annotations.<br/>The study at issue offers major findings regarding the impact and implications of the <br/>models utilized on the CIFAR-10 dataset. The findings contribute to an understanding of the strengths and weaknesses of these approaches, guiding future research to enhance the performance of the image classification models.<br/>Keywords: Alexnet, CIFAR-10 Dataset, Convolutional Neural Networks, Googlenet, <br/>Image Classification, Resnet, Vggnet |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Neural networks (Computer science) |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Data sets |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Özgünalp, Umar |
| İlişkili Terim | supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu | Toplam Ödünçverme |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Tez Koleksiyonu | 27.10.2023 | Bağış | YL 3165 A46 2023 | T3546 | 27.10.2023 | 27.10.2023 | Thesis | Electrical and Electronics Engineering Department | ||||
| Dewey Onlu Sınıflama Sistemi | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 27.10.2023 | Bağış | YL 3165 A46 2023 | CDT3546 | 27.10.2023 | 27.10.2023 | Suppl. CD | Electrical and Electronics Engineering Department |