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    Evaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodology
    (Elsevier Ltd, 2024) Otay, İ.; Çevik Onar, S.; Öztayşi, B.; Kahraman, C.
    Smart cities are technological settlements using the collected data to utilize resources and services effectively by combining information and communication technologies with various tools connected to the internet of things network. Sustainable energy systems in smart cities are systems can be evaluated by using multiple criteria decision making methods or methodologies based on several vague/imprecise evaluation criteria. In this paper, sustainable energy systems in smart cities are evaluated by interval-valued Pythagorean fuzzy (IVPF) sets with an integrated optimization based multi-expert fuzzy Best Worst Method (BWM) and TOPSIS methodology that can better handle uncertainty and vagueness in experts’ linguistic assessments than existing methodologies. The considered criteria are weighted by multi-expert IVPF Best Worst Method, which has become a popular weighting method in recent years. Later, the energy alternatives for a real case study are prioritized by multi-expert IVPF TOPSIS method. In the analysis, the most important criterion is found as Environmental sustainability (C1) with the defuzzified weight of 0.218 while the other weights are as initial investment (C2) with 0.196, operating expenses (C3) with 0.163, technical feasibility (C4) with 0.154, social acceptability (C5) with 0.140, and scalability (C6) with 0.129. The obtained results indicate that “Investing in advanced technologies” in a smart city with relative degree of closeness (RDC) value of 0.798, has been determined as the best alternative among the considered five alternatives. It is closely followed by “Developing a transportation system” with the RDC value of 0.681. Sensitivity analysis shows that the ranking results are quite robust and reliable. The comparative analysis with crisp BWM and TOPSIS methodology is applied to check the validity of the proposed methodology. © 2024 Elsevier Ltd
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    Forecasting box office performances using machine learning algorithms
    (Springer Verlag, 2020) Çağlıyor, S.; Öztayşi, B.; Sezgin, S.
    Motion picture industry is one of the largest industries worldwide and has a significant importance in the global economy. However, still each year, there is a considerable number of movies fail even to break even and lose a lot of money. Considering the high stakes and high risks in the industry forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study it is aimed to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey before their market entry. As independent variables MPAA rating, budget, star and director power, sequel, adaptation, number of screens, domestic performance, release time lag between domestic and foreign market are investigated. From sources like IMDB, Box Office Mojo, Box Office Türkiye a data set of 1585 movies is constructed and four models -Support Vector Regression (SVM), Artificial Neural Networks (ANN), Decision Tree Regression (DT) and Linear Regression (LR) are evaluated. Since our model is developed to predict the expected box office of a movie before its theatrical release in Turkey, it can help studios distributors and exhibitors in their decisions about market entry, timing of entry or distribution strategies. © 2020, Springer Nature Switzerland AG.