Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Technology and System Information
Taspinar, Yavuz
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Clustering Performance on Heart Disease Data: Effects of Distance Metrics and Scaling Akbas, Ibrahim; Taspinar, Yavuz; Koklu, Murat
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5336

Abstract

Cardiovascular diseases (CVD) are one of the leading causes of morbidity and mortality worldwide, requiring advanced analytical approaches to identify early-stage risk groups and classify patient profiles in greater detail. The aim of this study is to reveal latent patient subgroups associated with CVD using unsupervised machine learning methods on clinical data. In this context, a dataset consisting of 11 clinical variables from 303 patients who visited the VA Medical Center in Long Beach, California, was analyzed. During the preprocessing stage, missing observations were eliminated, only numerical variables were used, and both z-score standardization and min–max normalization were applied to the data. Subsequently, hierarchical clustering analyses were performed using single, complete, and average linkage approaches based on Euclidean and cosine distance measures) (the number of possible clusters for different distance–scaling combinations was evaluated using the Elbow and Silhouette measures. The results obtained showed that the 4-cluster solution, particularly under the complete and average linkage methods, represented the data structure in the most clinically explanatory manner. The similarity between the clustering results obtained using the k-means algorithm with Euclidean distance in standardized data and cosine distance in normalized data was calculated as the Rand Index (RI) = 0.8179) (this value demonstrated that the cluster structure was largely preserved despite different distance metrics and scaling strategies.  The findings demonstrate that unsupervised learning-based clustering approaches provide a useful tool for defining meaningful risk classes within heterogeneous patient populations based on clinical datasets and for conducting comparative clinical evaluations between these classes.
Classification and Analysis of Real and Fake Aerial Vehicle Images Using Machine Learning Aksoy, Hasan; Ozcelik, Ziya; Taspinar, Yavuz
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5345

Abstract

Aircraft are widely used in both military and civilian fields today. Detecting aircraft in the airspace is of great strategic and societal importance. In recent years, distinguishing images generated by artificial intelligence from real images has become increasingly difficult. This article presents a study on the classification of real aircraft images and AI-generated aircraft images by machine learning algorithms. Six classifications were obtained from 300 images in the dataset. These classifications are: fake commercial aircraft AI, fake military aircraft AI, fake private aircraft AI, real commercial aircraft, real military aircraft, and real private aircraft. These data were classified using common machine learning models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Accuracy, Precision, Recall, and F1 Score metrics were used to analyze the classification success of these models. ROC was used for a detailed analysis of the classification success of the models. According to the results obtained, the ANN model achieved a classification success rate of 96.6%, the KNN model 90.4%, the SVM model 96.7%, and the LR model 96.5%. The highest classification success rate was obtained from the SVM model. These results show that all models achieved similar classification success rates, with the KNN model achieving a lower classification success rate than the others. In conclusion, it can be said that all models can be used in the classification of aircraft images.
Co-Authors Akbas, Ibrahim Aksoy, Hasan Koklu, Murat Ozcelik, Ziya