Yazar "Oztemiz, Furkan" seçeneğine göre listele

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    Determination of Efficient Green Wave Corridors in The Transportation Network with The Community Detection Method
    (Gazi Univ, 2024) Oztemiz, Furkan; Karci, Ali
    Signaling systems play an important role in managing urban transport networks. Optimizing signaling systems significantly reduces traffic density in transport networks. One of the popular methods applied to increase the efficiency of the signaling system is the green wave application, which means the coordinated operation of the signaling systems. The green wave system prevents vehicles from being repeatedly caught in red light, reducing travel time, waiting time on the roads and carbon emissions of vehicles. The problem that will arise at this point is on which intersection points the green wave system will be applied. In this study, vehicle counting and signaling data of the city of Malatya were used and the transportation network data was converted into a weighted graph. By applying the walktrap community detection algorithm to the transportation network, the intersection points are grouped according to the vehicle transition similarities on them. It was applied to the green wave system physically for the intersection points in 2 different groups determined. The results show that there are significant increases in the number of vehicles passing per unit time in the regions where green waves are applied. This situation has resulted in a decrease in the number of vehicles waiting at red lights and significant reductions in carbon emissions from stationary vehicles into the atmosphere.
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    Implementation of Applied Prediction with YSA for Data Groups with Association Rule
    (Ieee, 2017) Oztemiz, Furkan; Hamamei, Serder Ethem
    Nowadays, increasing data sizes have grown at incredible levels. Many firms want to interpret their produced data and reach the useful information. In this study, by making customer basket analysis of a company operating in the retail sector, to organize suitable campaigns for the customers has been aimed and sales amounts of campaign items have been predicted before the campaign. The association rules for the items purchased by the customers has been obtained by using the Apriori algorithm. Sales amounts of associated items have been predicted with Artificial Neural Networks (ANN). For prediction process with ANN, MATLAB-NNTOOL toolbox has been used. With these prediction process, sales amounts of the campaign items offered to the customers have been determined and an idea about the success of campaign success has been obtained. In the study, 34 months sales data has been considered.
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    KO: Modularity optimization in community detection
    (Springer London Ltd, 2023) Oztemiz, Furkan; Karci, Ali
    Many algorithms have been developed to detect communities in networks. The success of these developed algorithms varies according to the types of networks. A community detection algorithm cannot always guarantee the best results on all networks. The most important reason for this is the approach algorithms follow when dividing any network into communities (sub-networks). The modularity of the network determines the quality of communities in networks. It is concluded that networks with high modularity values are divided into more successful communities (clusters, sub-networks). This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The basic approach of the proposed algorithm is to transfer nodes at the community boundary to neighboring communities if they meet the specified conditions. The method called KO (Karci-Oztemiz) optimization algorithm maximizes the modularity value of any community detection algorithm in the best case, while it does not change the modularity value in the worst case. For the KO algorithm's test, in this study, Walktrap, Cluster Edge Betweenness, Label Propagation, Fast Greedy, and Leading Eigenvector community detection algorithms have been applied on three popular networks that were unweighted and undirected previously used in the literature. The community structures created by five community detection algorithms were optimized via the KO algorithm and the success of the proposed method was analyzed. When the results are examined, the modularity values of the community detection algorithms applied on the three different networks have increased at varying rates (0%,.,14.73%).
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    Malatya Public Transportation Route Optimization via Ant Colony Algorithm
    (Ieee, 2018) Oztemiz, Furkan; Yeroglu, Celaleddin
    Increasing population density causes traffic densities in city centers. In this study, Ant Colony Algorithm (ACO) was used to find solutions to the traffic problems in crowded cities and Malatya province was chosen as the application region. Need of reducing the traffic intensity in the city centers, has led to the idea that the central stop of public transportation vehicles should be moved. This situation reveals the problem of changing the routes of public transport. In this study, ACO algorithm was used to analyze the new routes in the most ideal way. It is aimed to realize minimum distance and minimum traffic density by solving this problem which is similar to the traveling salesman problem. In order to achieve minimum traffic intensity, the threshold pheromone amount is determined to direct multiple vehicles to alternative routes. The data used in the analysis belongs to the public transportation vehicles of the city of Malatya. A java based program was used to construct the datasets and to solve the problem.
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    Performance Comparison of Physics Based Meta-Heuristic Optimization Algorithms
    (Ieee, 2018) Demirol, Doygun; Oztemiz, Furkan; Karci, Ali
    The optimization process is a process that aims to find the minimum or maximum point according to the objective function. Many different algorithms have been developed for optimization problems. While analytical methods are committed to finding the exact solution specific to their problem, heuristic methods are committed to finding the best solution to the larger set of problems. Mathematical models of the system and the objective function are needed to solve the problems. General purpose heuristic optimization algorithms are evaluated in eight different groups including physics, biology, social, herd, music, chemistry, sports and mathematics. In this study, Be about Water Cycle Algorithm, Electromagnetic Field Optimization, Big Bang Big Crunch, Gravitational Search Algorithm, Optics Inspired Optimization, the performance results of 5 different algorithms were compared for Sphere, Rastrigin, Rosenbrock, Griewank and Ackley test functions. In consequence of the number of stated population, size, run and iteration, after the minimum, maximum, standard deviation, and their mean values were established, their superiority to each other was determined.