Academician
SERCAN KÜLCÜ
DOKTOR ÖĞRETİM ÜYESİ
GİRESUN ÜNİVERSİTESİ MÜHENDİSLİK FAKÜLTESİ BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
- Ana Dal Mühendislik Temel Alanı
- Yan Dal Bilgisayar Bilimleri ve Mühendisliği
Article counts come from two sources, each shown separately: YÖKSİS (academic declarations) and OpenAlex (open scholarly catalog). Patents and artistic activities are listed under the Works tabs below.
Index quartiles
Scopus (SJR)
5 Total
- Q1 4
- Q2 0
- Q3 1
- Q4 0
Other counts
- YÖKSİS rows 5 Q1 0 Q2 0 Q3 0 Q4 0
WoS (JCR)
6 Total
- Q1 1
- Q2 3
- Q3 0
- Q4 2
Other counts
- YÖKSİS rows 6 Q1 0 Q2 0 Q3 0 Q4 0
TR Index
4 articles
OpenAlex citation percentiles
Top 1% articles
0
Top 10% articles
1
Avg percentile
70.0%
Articles with percentile
8
OpenAlex citation percentile; covered works only (~52%).
What do these indicators mean?
- Q1–Q4: the quartile of the journal in the given index (Scopus/WoS). Q1 = top 25%, Q4 = bottom 25%.
- Top 1% / top 10%: number of works among the top 1% or 10% most-cited worldwide in the same field and year.
- Avg percentile: mean citation percentile of the works (100 = highest).
- Source: index quartiles from Scopus/WoS and TR Index flags; citation percentiles from OpenAlex.
Proceedings
Conference proceedings recorded in YÖKSİS.
Records
- 2025 Nucleus Segmentation in Heterogeneous Microscopy Images on the Data Science Bowl 2018 Dataset
- 2016 A Scalable Approach for Sentiment Analysis of Turkish Tweets and Linking Tweets to News
- 2016 A survey on semantic Web and big data technologies for social network analysis
- 2022 A New Routing Objective Function for IETF 6TiSCH Protocol
- 2025 Meta-Learner Ensemble Model for Brain Tumor Classification
- 2025 Selection Of The Most Suitable Parameters For Classification Of Hazelnut Fruit Using Shufflenet Deep Learning Algorithm
- 2025 Comparison Of The Performance Of Resnet-50 And Shufflenet Deep Learning Algorithms In Classification Of Hazelnut Fruit
- 2022 Improving Synchronization Time in 6TiSCH Networks with Smart Antennas
- 2025 Transformer vs. CNN: Benchmarking RT-DETR and YOLO Models on the UATD Dataset