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Yazılım Mühendisliği Bölümü

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Browsing Yazılım Mühendisliği Bölümü by browse.metadata.publisher "Elsevier"

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    Citation - WoS: 224
    Citation - Scopus: 287
    A Comprehensive Survey on Recent Metaheuristics for Feature Selection
    (Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan Ezgi
    Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.
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    Citation - WoS: 20
    Citation - Scopus: 29
    Creating Consensus Group Using Online Learning Based Reputation in Blockchain Networks
    (Elsevier, 2019) Ozsoy, Adnan; Oztaner, Serdar Murat; Sever, Hayri; Bugday, Ahmet
    One of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group. (C) 2019 Elsevier B.V. All rights reserved.