Browsing by Author "Gunel, Korhan"

Now showing 1 - 4 of 4

Citation - WoS: 1
Citation - Scopus: 1
A Feature Selection Application Using Particle Swarm Optimization for Learning Concept Detection
(SPRINGER-VERLAG BERLIN, 2017) Korhan Gunel; Kazim Erdogdu; Refet Polat; Yasin Ozarslan; Polat, Refet; Erdogdu, Kazim; Ozarslan, Yasin; Gunel, Korhan; A Rocha; AM Correia; H Adeli; LP Reis; S Costanzo
Recent developments of computational intelligence on educational technology yield concept map mining as a new research area. Concept map mining covers the extraction of learning concepts specifying relations among them and generating a concept map from educational contents. In this study we focused on determining the features that characterize a learning concept extracted from an educational text as raw data. The first three features are detected by using a hybrid system of Multi Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) and the performance of the applied method is gauged in the viewpoint of a typical classification problem.
Citation - WoS: 8
Citation - Scopus: 10
An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection
(WILEY, 2019) Korhan Gunel; Kazim Erdogdu; Refet Polat; Yasin Ozarslan; Polat, Refet; Erdogdu, Kazim; Ozarslan, Yasin; Gunel, Korhan
Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content specifying relations among them and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed-forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state-of-the-art algorithms have been made from the viewpoint of a typical classification problem.
Citation - WoS: 3
Citation - Scopus: 3
Analyzing Learning Concepts in Intelligent Tutoring Systems
(ZARKA PRIVATE UNIV, 2016) Korhan Gunel; Refet Polat; Mehmet Kurt; Kurt, Mehmet; Polat, Refet; Gunel, Korhan
The information that is increasing and changing rapidly at the present day and the usage of computers in educational and instructional processes has become inevitable. With the rapid progress in technology research gives more importance to integrate intelligent issues with educational support systems such as distance learning and learning management systems. Such studies are considered as applications of the artificial intelligence on educational processes. Regarding this viewpoint some supervised learning models which is able to recognize the learning concepts from a given educational content presented to a tutoring system has been designed in this study. For this aim firstly three different corpora constructed from educational contents related to the subject titles such as calculus abstract algebra and computer science have been composed. For each candidate learning concepts the feature vectors have been generated using a relation factor in addition to tf-idf values. The relation factor is defined as the ratio of the total number of the most frequent substrings in the corpus that appear with a candidate concept in the same sentence within an educational content to most frequent substring in the corpus. The achievement of this system is measured according to the F-measure.
Citation - WoS: 14
Citation - Scopus: 14
On the Solution of the Black-Scholes Equation Using Feed-Forward Neural Networks
(SPRINGER, 2021) Saadet Eskiizmirliler; Korhan Gunel; Refet Polat; Eskiizmirliler, Saadet; Polat, Refet; Gunel, Korhan
This paper deals with a comparative numerical analysis of the Black-Scholes equation for the value of a European call option. Artificial neural networks are used for the numerical solution to this problem. According to this method we approximate the unknown function of the option value using a trial function which depends on a neural network solution and satisfies the given boundary conditions of the Black-Scholes equation. We consider some optimization methods not examined in the standard literature such as particle swarm optimization and the gradient-type monotone iteration process to obtain the unknown parameters of the neural network. Numerical results show that this proposed version of neural network method obtains all data from the terminal value and boundary conditions with sufficient accuracy.