Aslan, Serpil; Kaya, Buket; Varol, A.; Yazici, A.; Yazici, M.A.; Varol, C.; Aygunes, G.S.; Cotur, A.

Scientific papers are mostly performed by collaboration between research groups. Researchers work in various subjects and in several research areas. Therefore, identifying a strong research group is a quite complex task. To cope with this complex task, we propose a network-based method that predicts the best appropriate collaborations for researchers in a co-authorship network. In this paper, we have constructed a weighted co-authorship network by using a real database of scientific papers. Almost all of the methods in this area use the unweighted network model. But, weighted networks are particularly important when representing the network-based properties. The constructed network consists of nodes and links which represent respectively authors and their collaborations. In these networks, if two researchers are the common authors in the same paper, they are considered connected. Typically, the link weights represent the number of papers that two authors have made collaboratively. We also use local LCP-based algorithms to calculate the betweenness and closeness of the authors in this paper. The experimental results demonstrate the success of the proposed method.

AtıfScopus3·OpenAlex2

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Korkmaz, Sevcan Aytac; Esmeray, Furkan; Varol, A.; Karabatak, M.; Varol, C.

In this article, the lignite coal datas in the Kalburcayi area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburcayi area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT-ANN, DWT-ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT-ANN method.

AtıfScopus8·OpenAlex12

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