Browsing by Author "Ince, Turker"

Now showing 1 - 2 of 2

Citation - WoS: 2
Citation - Scopus: 4
A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case
(Institute of Electrical and Electronics Engineers Inc., 2017) Asli Tasci; Türker Ince; Cüneyt Güzeliş; Guzelis, Cuneyt; Tasci, Asli; Ince, Turker
In this study three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a cancer type is evaluated by the classification ability of selected genes. A publicly available micro array dataset is employed for gene expression values. Selected gene subsets were able to classify subtypes of the considered cancer type with high accuracies and showed that these feature selection methods were applicable for bio-marker gene selection. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 3
Design of an interactive fashion recommendation platform with intelligent systems, Proiectarea unei platforme interactive de recomandare a articolelor de modă cu sisteme inteligente
(Inst. Nat. Cercetare-Dezvoltare Text. Pielarie, 2024) Arzu Vuruşkan; Gökhan Demirkıran; Ender Yazgan Bulgun; Türker Ince; Cüneyt Güzeliş; Vuruskan, Arzu; Demirkiran, Gokhan; Ince, Turker; Bulgun, Ender; Guzelis, Cuneyt
With the increase in customer expectations in online fashion sales greater integration of fashion recommender systems (RSs) allows more personalization. Design decisions rely on personal taste as well as many other external influences such as trends and social media making it challenging to adapt intelligent systems for the fashion industry. Different methods for recommending personalized fashion items have been proposed however the literature still lacks an approach for recommending expert-suggested and personalized items. In this research an interactive web-based platform is developed to support personalized fashion styling focusing on users with diverse body shapes. To merge the user’s taste and the expert’s suggestion the proposed methodology in this research combines genetic algorithms and machine learning techniques allowing the system to access expert knowledge (including external influences) and incremental learning capability by adapting to the user preferences that unfold during interaction with the system. © 2024 Elsevier B.V. All rights reserved.