IDENTITY PREDICTION FROM ONLINE HANDWRITING BIOMETRICS / (Kayıt no. 292748)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03376nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20240923111431.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240912d2024 cy dj||| |||| 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 3453 |
| Cutter no | I27 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Ibrahim, Sufıan Olayınka |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | IDENTITY PREDICTION FROM ONLINE HANDWRITING BIOMETRICS / |
| Sorumluluk Bildirimi | SUFIAN OLAYINKA IBRAHIM ; SUPERVISOR, ASST. PROF. DR. YASEMIN BAY AZYEREN |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 53 sheets; |
| Birlikteki Materyal | + 1 CD ROM |
| Boyutları | 30 cm |
| 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 |
| 520 ## - ÖZET NOTU | |
| Özet notu | In the quickly changing world of today, it is more important than ever to correctly determine and confirm people's identities. By leading the development of an advanced system that uses online handwriting biometrics to dramatically alter the identity prediction process, this project is well-positioned to address this growing requirement. The core of this project is a thorough investigation of 42 unique characteristics that are present in the field of online handwriting biometrics. The project attempts to find and prioritize the most discriminative features, guaranteeing the robustness and reliability of the final system, through careful research and rigorous testing. Focusing on these essential characteristics will allow the system to capture the core of a person's handwriting style, making it highly confident in its ability to forecast that person's identity. The use of cutting-edge classification approaches, which will form the foundation of the identification prediction process, is essential to the project's success. By utilizing methods from pattern recognition and machine learning, these approaches will sort through enormous volumes of data and identify minute connections and patterns that could escape human scrutiny. Iteratively improving and optimizing, the system will keep improving its ability to predict handwriting patterns, allowing it to adjust to changing situations and be useful in a variety of settings. The goal of this project is to transform identity authentication by establishing a connection between state-of-the-art technical innovations and conventional verification techniques. Creating a framework that is more inclusive and accessible to everyone is the ultimate aim, going beyond simply optimizing current procedures. With the ability to improve security, fight fraud, and preserve the integrity of vital systems and services, this technology has a wide range of potential uses in the fields of banking, healthcare, and law enforcement. Essentially, this initiative marks a paradigm shift in how we address the fundamental subject of identity verification in the digital era, representing more than just a technological advance. In an increasingly connected world, we have the chance to not only improve and expedite current procedures but also open up new avenues for security, effectiveness, and trust by utilizing online handwriting biometrics. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Management Information Systems |
| 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 | 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 | 12.09.2024 | Bağış | YL 3453 I27 2024 | T3870 | 12.09.2024 | C.1 | 12.09.2024 | Thesis | Management Information Systems | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 12.09.2024 | Bağış | YL 3453 I27 2024 | CDT3870 | 12.09.2024 | C.1 | 12.09.2024 | Suppl. CD | Management Information Systems |