AGE ESTIMATING USING HUMAN GAIT EXTRACTED FROM PREPROCESSED VIDEO / (Kayıt no. 292869)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03010nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20241009144550.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy de||| |||| 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 | D 428 |
| Cutter no | M87 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Musalhi, Nasser Humaid Shinoon Al |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | AGE ESTIMATING USING HUMAN GAIT EXTRACTED FROM PREPROCESSED VIDEO / |
| Sorumluluk Bildirimi | NASSER HUMAID SHINOON AL MUSALHI ; SUPERVISOR, PROF. DR. ERBUĞ ÇELEBİ |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 139 sheets ; |
| Boyutları | 30 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 (PhD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering |
| 520 ## - ÖZET NOTU | |
| Özet notu | This research aims to address challenges in age prediction using gait analysis by<br/>analyzing human gait derived from preprocessed video data. The study identifies<br/>problems associated with the quality and preprocessing of video data, which can affect<br/>the accuracy of age estimation algorithms. The lack of comprehensive research on<br/>preprocessing methods and suitable datasets further complicates the development of<br/>reliable age estimation systems. The research aims to extract gait features, estimate<br/>human age based on gait, implement biometric gait recognition using machine<br/>learning, and evaluate the performance of the models.<br/>The results demonstrate the effectiveness of different deep learning-based models in<br/>estimating age based on gait data. The proposed Hybrid Model consistently achieves<br/>the highest accuracy, followed by DenseNet and ResNet models. The standard CNN<br/>model is the least accurate but still provides reasonable estimations among all other<br/>tasted CNN models. The Hybrid Model emerges as the most promising choice for age<br/>estimation due to its high accuracy and consistent performance.<br/>The study also addresses the issue of imbalanced class samples through the use of<br/>sparse categorical cross-entropy and class weight balance techniques. Both approaches<br/>improve the model's performance on imbalanced training and testing data, with the<br/>hybrid model maintaining high accuracy and stability. Comparing these models with<br/>state-of-the-art models, the proposed hybrid models with sparse categorical cross<br/>entropy or class weight balance outperform existing approaches in terms of accuracy.<br/>This research contributes novel approaches for preprocessing human gait images,<br/>developing hybrid models for age estimation, and utilizing sparse categorical cross<br/>entropy and class weight balance techniques to handle imbalanced class samples. The<br/>results demonstrate the effectiveness and potential of these approaches in improving<br/>the accuracy and reliability of age estimation algorithms based on human gait analysis. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer Engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Çelebi, Erbuğ |
| 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 | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 27.09.2024 | Bağış | D 428 M87 2024 | T3887 | 27.09.2024 | C.1 | 27.09.2024 | Thesis | Computer Engineering | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 27.09.2024 | Bağış | D 428 M87 2024 | CDT3887 | 27.09.2024 | C.1 | 27.09.2024 | Suppl. CD | Computer Engineering |