Arslan, İlker

Profile URL
https://hdl.handle.net/20.500.11779/2691
Name Variants
Job Title
Email Address
arslanil@mef.edu.tr
Main Affiliation
02.03. Department of Mechanical Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Files
1728752412-academic-1701013836.avif (9.76 KB)
No research topics data found.

Sustainable Development Goals

SDG data is not available
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
No records found in other affiliations.
Scholarly Output

4

Articles

3

Views / Downloads

95/34

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

0

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.00

Open Access Source

1

Supervised Theses

0

JournalCount
International Journal of Statistics in Medical Research1
Statistics & Probability Letters1
Stochastic Models1
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2021 - 20264

Settings

Author of Publication: search.filters.isAuthorOfPublication.3da2ea70-0485-49bd-804a-61304f485154

Scholarly Output Search Results

Now showing 1 - 4 of 4
  • Article
    Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms
    (Lifescience Global, 2025-09-01) Bulut, Nurgül; Çakar, Tuna; Arslan, İlker; Akıncı, Zeynep Karaoğlu; Oner, Kevser Setenay
    Introduction: This study aims to determine the stages of Alzheimer's disease (AD) using different machine learning algorithms, and compares the performance of these models. Methods: Demographic, genetic, and neurocognitive inventory data from the National Alzheimer's Coordinating Center (NACC) database as well as brain volume/thickness data from magnetic resonance imaging (MRI) scans were used. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were used to identify four different ordinal stages of AD. Results: Although the performance measures of the developed models were similar, the highest classification rate of AD stages was achieved by the Random Forest model (accuracy: 0.86; F1 score: 0.86; AUC: 0.95). The outputs of the model with the best performance were explained by the SHapley Addictive exPlanations (SHAP) method. Conclusions: This indicates that non-invasive markers and machine learning models can be used effectively in early diagnosis and decision support systems to predict stages of AD. © 2025 Elsevier B.V., All rights reserved.
  • Master Term Project
    Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Tuna Çakar
    One of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.
  • Article
    A Distance-Dependent Random Graph Model and Its Analysis
    (Taylor & Francis Inc, 2026-04-14) Arslan, İlker; Işlak, Ümit
    Let W-1,..., Wn be non-negative random variables. We consider an undirected random graph model on the node set {1,. ..,n}, where two nodes i < j are adjacent if W-i < W-j. In our setting, the Wi's are independent but not necessarily identically distributed, resulting in a model that generalizes the classical random permutation graphs. The model exhibits a certain dependence among the edges. Moreover, when nodes have physical interpretations- such as points on the real line R with node i located at position x = i-the model gains spatial structure and becomes, in particular, distance-dependent. We derive theoretical results on degree distributions, the number of isolated vertices, and the number of close neighbors. Simulation-based observations are also provided for the average clustering and the global efficiency.
  • Article
    Increasing and Other Subsequence Problems for Random Interval Sequences
    (Elsevier, 2026-05-01) Arslan, Ilker; Islak, Umit
    Various relations for comparison of intervals of real numbers are introduced, and the expected length of the corresponding longest increasing subsequence is analyzed. When intervals are randomly generated by taking the minimum and maximum of two independent uniform random variables, we prove that the expected length of the longest increasing subsequence grows on root the order of 3 n. We also investigate the asymptotic behavior of the expected length under alternative comparison relations and random interval models. Discussions on other subsequence problems for interval sequences are included.