Altunkaya, Duygu

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12514/7145
Name Variants
Job Title
Öğretim Görevlisi
Email Address
duygualtunkaya@artuklu.edu.tr
Main Affiliation
Department of Computer Programming / Bilgisayar Programcılığı Bölümü
Status
Website
ORCID ID
0000-0002-4277-056X
Scopus Author ID
58739923400
Turkish CoHE Profile ID
320045
Google Scholar ID
Google Scholar ID
WoS Researcher ID
HDL-7174-2022

Sustainable Development Goals

SDG data is not available
Documents

2

Citations

2

h-index

1

Go to Scopus profile
Documents

0

Citations

0

Go to WoS profile
Scholarly Output

1

Articles

1

Views / Downloads

4/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

1

WoS h-index

0

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

1.00

Open Access Source

0

Supervised Theses

0

Google Analytics Visitor Traffic

JournalCount
Sakarya University Journal of Computer and Information Sciences1
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

20251
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Article
    Citation - Scopus: 1
    Encoding IoT Data: a Comprehensive Review of Image Transformation Techniques
    (Sakarya University, 2025) Altunkaya, D.; Okay, F.Y.; Özdemir, S.
    In the era of the Internet of Things (IoT), where smartphones, built-in systems, wireless sensors, and nearly every smart device connect through local networks or the internet, billions of smart things communicate with each other and generate vast amounts of time-series data. As IoT time-series data is high-dimensional and high-frequency, time-series classification or regression has been a challenging issue in IoT. Recently, deep learning algorithms have demonstrated superior performance results in time-series data classification in many smart and intelligent IoT applications. However, it is hard to explore the hidden dynamic patterns and trends in time series. Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies that use image transformation/encoding techniques in the IoT domain. We examine the studies according to their encoding techniques, data types, and application areas. Lastly, we emphasize the challenges and future dimensions of image transformation. © 2025, Sakarya University. All rights reserved.