Yazar "Tuzcu, Atakan" seçeneğine göre listele
Listeleniyor 1 - 4 / 4
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A Machine Learning Based Predictive Analysis Use Case for eSports Games(İzmir Bakırçay Üniversitesi, 2023) Tuzcu, Atakan; Ay, Emel Gizem; Uçar, Ayşegül Umay; Kılınç, DenizLeague of Legends (LoL) is a popular multiplayer online battle arena (MOBA) game that is highly recognized in the professional esports scene due to its competitive environment, strategic gameplay, and large prize pools. This study aims to predict the outcome of LoL matches and observe the impact of feature selection on model performance using machine learning classification algorithms on historical game data obtained through the official API provided by Riot Games. Detailed examinations were conducted at both team and player levels, and missing data in the dataset were addressed. A total of 1045 data were used for training team-based models, and 5232 data were used for training player-based models. Seven different machine learning models were trained and their performances were compared. Models trained on team data achieved the highest accuracy of over 98% with the AdaBoost algorithm. The top 10 features that had the most impact on the prediction outcome were identified among the 47 features in the dataset, and a new dataset was created from team data to retrain the models. After feature selection, the results showed that the accuracy of Logistic Regression increased from 89% to 98% and the accuracy of Gradient Boosting algorithm increased from 96% to 98%.Öğe Betweenness Centrality in Sparse Real World and Wireless Multi-hop Networks(Springer Science and Business Media Deutschland GmbH, 2022) Tuzcu, Atakan; Arslan, HilalGraphs are one of the compact ways to represent information about real-life and intelligent system networks like wireless sensor networks. Betweenness centrality is an important network measure that evaluates the significance of a node based on the shortest paths and is widely used in biological, social, transportation, complex, and communication networks. In this study, we implement an efficient algorithm computing betweenness centrality of nodes for real-world and wireless multi-hop networks. Large sparse graphs are stored using compressed sparse row storage format and modified version of Dijkstra’s algorithm is used to compute shortest paths. We conduct a comprehensive experimental study on real-world networks as well as wireless sensor networks that are state-of-the-art technologies for different applications such as intelligence structures, industrial and home automation as well as health care. We evaluate the effect of network dimension on the time needed to compute betweenness centrality. Experimental results demonstrate that computation time required to compute betweenness centrality varies from 0.9 to 52.5 s when the number of vertices changes from 10,000 to 60,000. We also observe that the proposed algorithm efficiently computes betweenness centrality for networks coming from machine learning, power network, and networks obtained from optimization problems as well as computational fluid dynamics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Betweenness centrality in sparse real world and wireless multi-hop networks(Springer Science and Business Media Deutschland GmbH, 2022) Tuzcu, Atakan; Arslan, HilalGraphs are one of the compact ways to represent information about real-life and intelligent system networks like wireless sensor networks. Betweenness centrality is an important network measure that evaluates the significance of a node based on the shortest paths and is widely used in biological, social, transportation, complex, and communication networks. In this study, we implement an efficient algorithm computing betweenness centrality of nodes for real-world and wireless multi-hop networks. Large sparse graphs are stored using compressed sparse row storage format and modified version of Dijkstra’s algorithm is used to compute shortest paths. We conduct a comprehensive experimental study on real-world networks as well as wireless sensor networks that are state-of-the-art technologies for different applications such as intelligence structures, industrial and home automation as well as health care. We evaluate the effect of network dimension on the time needed to compute betweenness centrality. Experimental results demonstrate that computation time required to compute betweenness centrality varies from 0.9 to 52.5 s when the number of vertices changes from 10,000 to 60,000. We also observe that the proposed algorithm efficiently computes betweenness centrality for networks coming from machine learning, power network, and networks obtained from optimization problems as well as computational fluid dynamics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Predictive e-sports game analysis using machine learning approaches(İzmir Bakırçay Üniversitesi Lisansüstü Eğitim Enstitüsü, 2023) Tuzcu, Atakan; Bursa, OkanOyun analizi günümüzde oldukça rağbet gören bir alandır ve League of Legends e- Spor alanında popüler oyunlardan biridir. Bir MOBA oyunu olan League of Legends aslında ikili sınıflandırma problemi olarak değerlendirilebilir. Bu çalışmada oyunun geliştiricisi tarafından yayınlanan RIOT API kullanarak veri çekilmiş ve çekilen veriler modele beslenmeden önce önişleme tabi tutulmuştur. Veri iki farklı yaklaşımla düzenlenip iki farklı veri kümesi olarak sunulmuştur: oyuncu-tabanlı veri kümesi ve takım-tabanlı veri kümesi. Modeller bu veri kümeleri ile ayrı ayrı eğitilmiş ve sonuçları değerlendirilmiştir. Bu değerlendirme sonucunda performansı en yüksek olan modeller 0,950 ve 0,969 F1-score ile sırasıyla LightGBM ve AdaBoost modelleri olmuştur. Deneysel çalışma olarak boyut azaltma yöntemlerinden öznitelik seçimi uygulanmış ve performansı en yüksek olan AdaBoost modelinin sunduğu en önemli 10 öznitelik seçilmiştir. Seçilen öznitelikler arasında korelasyon analizi yapılarak korelasyonu en az olan 7 öznitelik ile veri kümeleri filtrelenerek, modeller bu deneysel veri kümeleri ile tekrar eğitilip test edilmiştir. Sonuç olarak performanslarda belirli bir düşüş gözlendiğinden elenen özniteliklerin oyun açısından stratejik bir öneme sahip olduğu ortaya konulmuştur.
