Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon Kullanarak Emeklilik Fonu Net Varlık Değerlerinin Tahmin Edilmesi

Kayakuş, Mehmet; Terzioğlu, Mustafa

doi:10.17671/gazibtd.742995

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…When a linear relationship is observed between the variables, LR is used to predict the future and examine how the variables affect each other [18]. LR creates a straight line that minimizes discrepancies between predicted and actual values [19]. Then, the value of the dependent variable is predicted using the independent variable.…”

Section: Baseline Modelsmentioning

confidence: 99%

Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model

Utku

2023

Bilişim Teknolojileri Dergisi

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The traffic density problem has become one of the most important problems of urban life. The time and fuel spent due to traffic density is a significant loss for vehicle users and countries. Applications developed to reduce the time spent in traffic cannot make successful predictions about long-term traffic density. Traffic data obtained from cameras, sensors and mobile devices highlights the use of artificial intelligence technologies in order to solve the traffic management problem. In this study, a hybrid prediction model has been proposed by using CNN and RNN models for traffic density prediction. The proposed hybrid model has been tested using LR, RF, SVM, MLP, CNN, RNN and LSTM and Istanbul's traffic data for 2020. Experimental results showed that the proposed hybrid model has more successful results than the compared models. The proposed model has 0.929 R2 in the prediction of the number of vehicles passing through the junction, and 0.934 R2 in the prediction of the average speed of the vehicles passing through the junction.

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Section: Baseline Modelsmentioning

confidence: 99%

Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model

Utku

2023

Bilişim Teknolojileri Dergisi

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“…In this study, Coefficient of Determination (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) evaluation methods were used to evaluate the estimation performance of the methods. Equations of the methods are given below [33]:…”

Section: Performance Metricsmentioning

confidence: 99%

Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

Emeç

TEKİN

2022

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in the world. Understanding and predicting the key features determining housing prices and value is an important consideration for urban planners and housing policymakers. In this study, machine learning and artificial neural network models were used to predict the housing demand of Konya, and their forecasting performances were compared. As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.

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