Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Citation - WoS: 14
Citation - Scopus: 18
D-Mosg: Discrete Multi-Objective Shuffled Gray Wolf Optimizer for Multi-Level Image Thresholding
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2021) Karakoyun, Murat; Gülcü, Şaban; Kodaz, Halife
Segmentation is an important step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is a very popular approach. To apply the thresholding approach, many methods such as Otsu, Kapur, Renyi etc. have been proposed in order to produce the thresholds that will segment the image optimally. These suggested methods usually have their own characteristics and are successful for particular images. It can be thought that better results may be obtained by using objective functions with different characteristics together. In this study, the thresholding which is originally applied as a single-objective problem has been considered as a multi-objective problem by using the Otsu and Kapur methods. Therefore, the discrete multi-objective shuffled gray wolf optimizer (D-MOSG) algorithm has been proposed for multi-level thresholding segmentation. Experiments have clearly shown that the D-MOSG algorithm has achieved superior results than the compared algorithms. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 58
Citation - Scopus: 76
A Discrete Tree-Seed Algorithm for Solving Symmetric Traveling Salesman Problem
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2020) Çınar, Ahmet Cevahir; Korkmaz, Sedat; Kıran, Mustafa Servet
Tree-Seed algorithm (TSA) is a recently developed nature inspired population-based iterative search algorithm. TSA is proposed for solving continuous optimization problems by inspiring the relations between trees and their seeds. The constrained and binary versions of TSA are present in the literature but there is no discrete version of TSA which decision variables represented as integer values. In the present work, the basic TSA is redesigned by integrating the swap, shift, and symmetry transformation operators in order to solve the permutation-coded optimization problems and it is called as DTSA. In the basic TSA, the solution update rules can be used for the decision variables whose are defined in continuous solution space, this rules are replaced with the transformation operators in the proposed DTSA. In order to investigate the performance of DTSA, well-known symmetric traveling salesman problems are considered in the experiments. The obtained results are compared with well-known metaheuristic algorithms and their variants, such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing (SA), State Transition Algorithm (STA), Artificial Bee Colony (ABC), Black Hole (BH), and Particle Swarm Optimization (PSO). Experimental results show that DTSA is another qualified and competitive solver on discrete optimization. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 20
Citation - Scopus: 32
Prediction of Middle School Students' Programming Talent Using Artificial Neural Networks
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2020) Çetinkaya, Ali; Baykan, Ömer Kaan
Nowadays, the softwarization and virtualization of resources and services rapidly continue, and along with reading and writing, programming is going to be one of the basic human ability. Thus, the detection of skilled programmers at an early age has become important for economies to strengthen their workforce and compete globally. The current technological momentum shows that when the middle school students of today reach the 2030s, the demand for advanced programming skills will be rapidly increased, expanding as high as 90% between 2016 and 2030. Thus, the identification of these skilled people at an early age is important. Accordingly, this study focused on predicting middle school students' programming aptitude using artificial neural network (ANN) algorithms. A participant survey was developed and applied to middle school students consisting of fifth, sixth, and seventh graders from Konya Science Center, Turkey. After the completion of the survey, the participants then took the 20-level Classic Maze course (CMC) on Code.org. The participants' final scores in the CMC were calculated based on the level they completed and the lines of codes they wrote. The best results were obtained using the Bayesian regularization algorithm: Training-R = 9.72284e-1; Test-R = 9.12687e-1, and All-R = 9.597e-1. The results show that ANN is an appropriate machine learning method that can forecast participants' skills, such as analytical thinking, problem-solving, and programming aptitude. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 28
Citation - Scopus: 34
Solving a Big-Scaled Hospital Facility Layout Problem With Meta-Heuristics Algorithms
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2020) Tongur, Vahit; Hacıbeyoğlu, Mehmet; Ülker, Erkan
The main objective of the hospital facility layout problem is to place the polyclinics, laboratories and radiology units within the predefined boundaries in such way that minimize the movement cost of patients and healthcare staff. Especially in big-scaled hospitals including several different specialized departments, it is important in terms of hospital efficiency that interacting units are placed closely. Nowadays meta-heuristic algorithms are often used to solve optimization problems such as facility layout. In this study; polyclinic, laboratory and radiology units' layout of a big-scaled university hospital was organized using three meta-heuristic algorithms which are migrating bird optimization (MBO), tabu search (TS) and simulated annealing (SA). The results were compared with the existing clinic layout. Consequently MBO and SA meta-heuristic algorithms have given the same best results improving the existing clinic layout efficiency approximately by 58%. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 52
Citation - Scopus: 61
Training Multi-Layer Perceptron With Artificial Algae Algorithm
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2020) Türkoğlu, Bahaeddin; Kaya, Ersin
Artificial Neural Networks are commonly used to solve problems in many areas, such as classification, pattern recognition, and image processing. The most challenging and critical phase of an Artificial Neural Networks is related with its training process. The main challenge in the training process is finding optimal network parameters (i.e. weight and biase). For this purpose, numerous heuristic algorithms have been used. One of them is Artificial Algae Algorithm, which has a nature-inspired metaheuristic optimization algorithm. This algorithm is capable of successfully solving a wide variety of numerical optimization problems. In this study, Artificial Algae Algorithm is proposed for training Artificial Neural Network. Ten classification datasets with different degrees of difficulty from the UCI database repository were used to compare the proposed method performance with six well known swarm-based optimization and backpropagation algorithms. The results of the study show that Artificial Algae Algorithm is a reliable approach for training Artificial Neural Networks. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

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