Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 17Citation - Scopus: 22Exploring the Factors Influencing Big Data Technology Acceptance(Institute of Electrical and Electronics Engineers Inc., 2023) Rahman, Nayem; Daim, Tuğrul U.; Başoğlu, Ahmet NuriBig Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis of the structural model reveals that the hypothesis tests are significant for 8 out of 12 path relationships. IEEEConference Object Citation - WoS: 8Citation - Scopus: 15A Comparative Study on Neural Network Based Soccer Result Prediction(Institute of Electrical and Electronics Engineers Inc., 2007) Aslan, Burak Galip; İnceoğlu, Mustafa MuratThis study mainly remarks the efficiency of black-box modeling capacity of neural networks in the case of forecasting soccer match results, and opens up several debates on the nature of prediction and selection of input parameters. The selection of input parameters is a serious problem in soccer match prediction systems based on neural networks or statistical methods. Several input vector suggestions are implemented in literature which is mostly based on direct data from weekly charts. Here in this paper, two different input vector parameters have been tested via learning vector quantization networks in order to emphasize the importance of input parameter selection. The input vector parameters introduced in this study are plain and also meaningful when compared to other studies. The results of different approaches presented in this study are compared to each other, and also compared with the results of other neural network approaches and statistical methods in order to give an idea about the successful prediction performance. The paper is concluded with discussions about the nature of soccer match forecasting concept that may draw the interests of researchers willing to work in this area.