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Gülay Taşçı

ResearcherPublications, citations & collaboration network

Gülay Taşçı is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 50 works, 517 citations, an h-index of 12 and an i10-index of 14.

50
Works
517
Citations
12
h-index
14
i10-index

How has Gülay Taşçı's publication output changed over time?

ScholarIQpublication output · 2018–2025

Output grew100% over the shown period — from 1 works in 2018 to 2 in 2025.

1
1
2
4
1
2
201820212022202320242025

What are the most-cited papers on Gülay Taşçı?

ScholarIQmost cited works
Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals
Gülay Taşçı, Hui Wen Loh, Prabal Datta Barua, Mehmet Bayğın, Burak Taşçı, Şengül Doğan, Türker Tuncer, Elizabeth E. Palmer, Ru‐San Tan, U. Rajendra Acharya
S10169007. 202289 Citations
Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses
Ismail BAYDİLİ, Burak Taşçı, Gülay Taşçı
Diagnostics. 202555 CitationsOPEN ACCESS
Deprem travmasının erken dönem psikolojik etkileri ve olası risk faktörleri
Gülay Taşçı, Filiz Özsoy
Çukurova medical journal (Online)/Çukurova medical journal. 202141 CitationsOPEN ACCESS
A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals
Burak Taşçı, Gülay Taşçı, Şengül Doğan, Türker Tuncer
S766161. 202241 CitationsOPEN ACCESS
Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection
Ismail BAYDİLİ, Burak Taşçı, Gülay Taşçı
Behavioral Sciences. 202537 CitationsOPEN ACCESS

Related on ScholarIQ

Sivas State Hospital
Institution
Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals
Paper
Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses
Paper
Deprem travmasının erken dönem psikolojik etkileri ve olası risk faktörleri
Paper
A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals
Paper
Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection
Paper
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