Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
2 results
Search Results
Book Part A Systematic Review of Optimization Studies Used in Renewable Energy Systems(Springer Science and Business Media Deutschland GmbH, 2026) Söylemez, İ.; Erdoğan, A.This study presents a literature review of recent studies on renewable energy systems. Due to the large number of studies, this study has been limited to some keywords. When only the word “renewable energy systems” is searched, there are more than 14,343 studies in the literature between 2017 and 2024. A systematic search was conducted for the studies in which “optimization” or “mathematical model” was mentioned as a solution methodology. A total of 755 studies were identified in the “Scopus database” and analyzed for these studies. A detailed examination was carried out for the type of studies (research article, review, conference paper, etc.), countries where the studies were carried out, authors who carried out the studies and their statistics with each other, and so on. With this study, an overview of the literature will be provided and it will be a guiding study for researchers on the direction of the studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Conference Object Citation - WoS: 8Citation - Scopus: 12SVM-RCE-R Optimization of Scoring Function for SVM-RCE(Springer International Publishing AG, 2021) Yousef, Malik; Jabeer, Amhar; Bakir-Gungor, BurcuGene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.