Bulut, Nurten

Profile URL
https://hdl.handle.net/20.500.12573/2692
Name Variants
Bulut, Nurten Bulut,Nurten
Job Title
Arş. Gör.
Email Address
nurten.bulut@agu.edu.tr
Main Affiliation
02. 04. Bilgisayar Mühendisliği
02. Mühendislik Fakültesi
01. Abdullah Gül University
Status
Current Staff
Website
ORCID ID
0000-0002-1895-8749
Scopus Author ID
58545209000
Turkish CoHE Profile ID
C50E571804A1F5D2
Google Scholar ID
byrHJ-sAAAAJ
WoS Researcher ID
IZV-3202-2023
Files
5431_Nurten Bulut.jpeg (3.81 KB)
No research topics data found.

Sustainable Development Goals

SDG data is not available
Documents

3

Citations

65

h-index

1

Go to Scopus profile
Documents

1

Citations

48

Go to WoS profile
No records found in other affiliations.
Scholarly Output

5

Articles

3

Views / Downloads

7/29

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

48

Scopus Citation Count

65

Patents

0

Projects

0

WoS Citations per Publication

9.60

Scopus Citations per Publication

13.00

Open Access Source

2

Supervised Theses

0

JournalCount
Gazi Mühendislik Bilimleri Dergisi2
-- 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- Sivas; Sivas Cumhuriyet University -- 1941531
PeerJ1
Proceedings of the International Conference on Statistics -- 6th International Conference on Statistics: Theory and Applications, ICSTA 2024 -- Barcelona -- 3200091
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2023 - 20245

Settings

Author of Publication: search.filters.isAuthorOfPublication.d8bc96b3-4d8e-4e9b-bf89-f835b37044fa

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Conference Object
    The Effect of Different Classifiers on Recursive Cluster Elimination in the Analysis of Transcriptomic Data
    (Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Bulut, Nurten; Bakir-Güngör, Burcu; Qaqish, Bahjat F.; Yousef, Malik
    Gene expression data with limited sample size and a large number of genes are frequently encountered in genetic studies. In such high-dimensional data, identification of genes that distinguish between disease states is a challenging task. Feature selection (FS) is a useful approach in dealing with high dimensionality. Support Vector Machines Recursive Cluster Elimination (SVM-RCE) is a technique for FS in high-dimensional data. The SVM-RCE approach has been utilized for identification of clusters of genes whose expression levels correlate with pathological state. A key step in SVM-RCE is the use of an SVM classifier to assign an area under the curve (AUC) score to each gene cluster based on its ability to predict class labels. In this study, we investigate the use of alternative classifiers in the cluster-scoring step. Specifically, we compare Support Vector Machines, Random Forest, XgBoost, Naive Bayes, and linear logistic regression. In addition to AUC score performance evaluation, the algorithms are compared in terms of the number of selected genes at different levels of clustering and in terms of the running time. © 2023 Elsevier B.V., All rights reserved.
  • Article
    Makine Öğrenmesi ve Derin Öğrenme Yöntemleri ile Hidroponik Tarım
    (2023) Bulut, Nurten; Hacıbeyoglu, Mehmet
    In the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.
  • Article
    Hydroponic Agriculture with Machine Learning and Deep Learning Methods
    (Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, Mehmet
    In the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.
  • Conference Object
    Enhancing Gene Expression Data Analysis Through SVM-Based Recursive Cluster Elimination and Weighted Center Approaches
    (Avestia Publishing, 2024-08) Yousef, Malik; Bulut, Nurten; Gungor, Burcu Bakir; Qaqish, Bahjat F.
    The complexity and high dimensionality of gene expression data pose significant challenges for effective feature selection and accurate classification in bioinformatics. This study introduces two novel algorithms, Support Vector Machine-Recursive Cluster Elimination (SVM-RCE) and its advanced version, SVM-RCE with Center Weights (SVM-RCE-CW), designed to optimize feature selection by leveraging clustering techniques and machine learning models. Both algorithms aim to reduce the feature space, thereby enhancing the interpretability and performance of classification models. We present a comprehensive comparison of these methods against traditional feature selection techniques, demonstrating their efficacy in achieving significant dimensionality reduction while maintaining or improving classification accuracy in several gene expression datasets. © 2024 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 48
    Citation - Scopus: 65
    Review of Feature Selection Approaches Based on Grouping of Features
    (PeerJ Inc, 2023-07-17) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Bulut, Nurten; Qaqish, Bahjat; Yousef, Malik
    With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly -ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work's findings can guide effective design of new FS approaches using feature grouping.