GENDER PREDICTION FROM INLINE SIGNITURE BIOMETRICS / (Kayıt no. 289794)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03231nam a22002897a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20230414092205.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 230227d2022 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 2732 |
| Cutter no | K27 2022 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Kargbo, Mustapha Mohamed |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | GENDER PREDICTION FROM INLINE SIGNITURE BIOMETRICS / |
| Sorumluluk Bildirimi | MUSTAPHA MOHAMED KARGBO; SUPERVISOR: ASST. PROF. DR. YASEMIN BAY AZYEREN |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 53 sheets; |
| Boyutları | 31 cm. |
| Birlikteki Materyal | Includes CD |
| 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 Management Information Systems Department |
| 504 ## - BİBLİYOGRAFİ NOTU | |
| Bibliyografi Notu | Includes bibliography (sheets 48-53) |
| 520 ## - ÖZET NOTU | |
| Özet notu | ABSTRACT<br/>With the improvement and huge introduction of technological devices and systems, <br/>the use of biometrics has become a very important inclusion in our lives to increase <br/>the level of confidence and security with access privileges to approved or certified <br/>individuals. One of the most popular applications of biometrics is signature biometrics, <br/>especially in terms of identification and/or verification process; however, it is not <br/>limited to this, and it reveals high-level discriminative physiological features. <br/>Although modalities of soft biometrics do provide details about an individual which is <br/>important for forensics when specified identities cannot be provided, they also include <br/>non-unique attributes of individuals like gender, age, ethnicity, presence of scars or<br/>tattoos, etc. The purpose of this study is to predict gender from the online signatures <br/>of an individual applying different feature sets during the classification process. <br/>In this study, Cyprus International University's handwriting and signature database is <br/>used for the investigation and prediction of gender from online signature biometrics. <br/>The database comprises 134 participants’ signatures, providing 8040 signature <br/>samples in total. The prediction is executed in three fundamental steps; feature <br/>selection, feature extraction, and classification. The feature selection method using <br/>WEKA helped to achieve the goal of gaining a better understanding of pattern <br/>recognition in gender prediction from online signature biometrics and this helped to <br/>achieve high accuracy levels using different classification methods such as KNN, <br/>Random Forest, and JRip.<br/>There were 5 experiments conducted in total using different sample sizes and feature <br/>sets and high gender prediction accuracy rates were obtained in all experiments which <br/>proves that signature biometrics contain valuable information especially gender and it <br/>is possible to predict gender from one’s signature. The highest result was obtained in <br/>Experiment 3 with 97 % accuracy.<br/>Keywords: Biometric, Gender Prediction, Online Signature, Machine Learning |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Biometry |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Machine learning |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Azyeren, Yasemin Bay |
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Tez Koleksiyonu | 27.02.2023 | Bağış | YL 2732 K27 2022 | T3061 | 27.02.2023 | 27.02.2023 | Thesis | Management Information Systems Department | |||
| Dewey Onlu Sınıflama Sistemi | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 27.02.2023 | Bağış | YL 2732 K27 2022 | CDT3061 | 27.02.2023 | 27.02.2023 | Suppl. CD | Management Information Systems Department |