Deniz Kayıkcı
Biographic Data
| ID | 6911377 |
|---|---|
| NAME | Deniz Kayıkcı |
| GIVEN NAMES | Deniz |
| FAMILY NAME | Kayıkcı |
| SIGNATURE | KAYIKCI D |
| AFFILIATIONS | Universitat Autonòma de Barcelona |
| ORCID | 0000-0002-1311-4272 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Archaeological Classification of Small Datasets Using Meta- and Transfer Learning Methods
In recent years, the increasing application of machine learning (ML) techniques to solve archaeological problems has garnered substantial interest. The relevant issue is that limited-sample, heterogeneous, and non-standard structures of datasets in archaeology affect the quality of deep learning (DL) model efficiency in training and testing. This study explores the effectiveness of artificial neural networks (ANNs) in addressing the unique challe…
Understanding the Style Differences of Ancient Ridergods Rock Reliefs with Feature Extraction
This study applies a computational approach to clustering grayscale images of rock reliefs from the Caria, Pisidia and Kibyratis region to detect the number of potential master carvers and/or schools responsible for these ancient artifacts. The model uses a dataset consisting of only 100 images and employs a feature extraction approach combining edge detection, texture analysis, shape analysis, and feature descriptors. We evaluate clustering perf…
No prominent works on this page.
Archaeological Classification of Small Datasets Using Meta- and Transfer Learning Methods
In recent years, the increasing application of machine learning (ML) techniques to solve archaeological problems has garnered substantial interest. The relevant issue is that limited-sample, heterogeneous, and non-standard structures of datasets in archaeology affect the quality of deep learning (DL) model efficiency in training and testing. This study explores the effectiveness of artificial neural networks (ANNs) in addressing the unique challe…
Understanding the Style Differences of Ancient Ridergods Rock Reliefs with Feature Extraction
This study applies a computational approach to clustering grayscale images of rock reliefs from the Caria, Pisidia and Kibyratis region to detect the number of potential master carvers and/or schools responsible for these ancient artifacts. The model uses a dataset consisting of only 100 images and employs a feature extraction approach combining edge detection, texture analysis, shape analysis, and feature descriptors. We evaluate clustering perf…
Image Processing and 3D Reconstruction (2 works) · Archaeological Research and Protection (1 work) · Archaeology and ancient environmental studies (1 work) · Archaeology and Rock Art Studies (1 work) · Artificial neural network (1 work) · Cluster analysis (1 work) · Cultural Heritage Materials Analysis (1 work) · Deep learning (1 work) · Documentation (1 work) · Feature (linguistics (1 work)