Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Celik, Erkan(1)
• Demiralp, Demet(1)
• Dogan, Gurkan(1)
• Gul, Muhammet(1)
• Pekel, Engin(1)
• Serin, Faruk(1)
• Yucesan, Melih(1)

• MakaleAçık Erişim2026/r/munzur/4545
  Predicting middle school students' academic achievement in mathematics with aAnew hybrid approach

  Dogan, Gurkan; Demiralp, Demet

  The academic achievement of middle school students is one of the critical steps in their educational journey that affects their future academic and career prospects. During this transition period, students are faced with a variety of challenges related to their families, schools, and the individual themselves. Research shows that academic performance during these formative years is critical for future educational success and overall life success. Therefore, addressing students' academic achievement, which depends on various factors during this period, is of particular importance for both institutional and individual planning and orientation in the future. In this study, a new hybrid approach based on artificial neural networks that enables automatic analysis of data on family, school, and individual factors affecting middle school students' academic achievement in mathematics is proposed. A publicly available student performance dataset was used for training and testing the proposed hybrid approach and other models. This dataset consists of data such as mathematics grades, family information, residential information, and health status information for 395 students enrolled in two public schools in the Alentejo region of Portugal. The proposed approach achieved an R2 score of 88.6% in experimental studies with this data set, providing approximately 3% higher accuracy than its closest competitor among other methods in the literature.

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  AtıfOpenAlex1

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• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/1927
  Forecasting daily natural gas consumption with regression, time series and machine learning based methods

  Yucesan, Melih; Pekel, Engin; Celik, Erkan; Gul, Muhammet; Serin, Faruk

  An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework’s applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R2), and mean squared error (MSE). According to each method’s results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.

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  AtıfScopus16·WoS19·OpenAlex22

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