Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024/r/munzur/6331
  Common Fixed Point Results for w-α-Distance

  Atıcı Turan, Gülcan; Polat, Fatma

  In this study, we examined some fixed point theorems in non-full metric spaces. We define the notions of α-lower semi-continuous , w-α-distance, w_0-α– distance, w-α-rational contraction and generalized w-α-rational contraction mapping. We also give related theorem and example. Then, we prove Banach’s fixed-point theorem thanks to the concept w-α- distance in metric spaces equipped with an abritrary binary relation. Also, w-α- rational contraction mapping and generalized w-α-rational contraction mapping are defined and by using these definitions, the theorem related fixed point is expressed and proved.

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• MakaleAçık Erişim2024/r/munzur/6330
  Neural Network Based a Comparative Analysis for Customer Churn Prediction

  Akcayol, M. Ali; Utku, Anıl

  Customer churn refers to a customer's disconnection from a business. The expense associated with customer churn encompasses both the forfeited revenue and the marketing expenditures required to acquire new customers. Mitigating customer churn stands as the foremost objective for every business. Customer churn prediction will contribute to developing strategies enabling businesses to retain these customers by identifying customers with a high risk of loss. In the digital world, the importance of developing customer churn prediction models is increasing daily. In this study, MLP based artificial neural network model was developed for customer churn prediction using customer data from an anonymous telecommunications company. The developed model was compared with kNN, LR, NB, RF, and SVM. The prediction results of the applied models were discussed, and the experimental results showed that all the models compared had over 70% accuracy. Experimental results showed that the developed MLP-based artificial neural network model has the most successful classification performance compared to other models with approximately 95% accuracy.

  AtıfOpenAlex3·TR Dizin2

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• MakaleAçık Erişim2024/r/munzur/6266
  Sentetik Atık Takviyeli Poliolefinlerin Mekanik Özelliklerinin GA-LSTM Hibrit Modeli ile Tahmini

  Utku, Anıl; Kismet, Yilmaz; Can, Umit

  Bu çalışmada, parçacık takviyeli termoplastiklerin enjeksiyon kalıplamasında kullanılan üretim parametrelerinin ürün kalitesi ve üretilen parçanın mekanik özellikleri üzerindeki etkileri optimize edilmiş bir Genetik Algoritma-Uzun Kısa Süreli Bellek (GA-LSTM) hibrit derin öğrenme yöntemi kullanılarak modellenmiştir. Burada termoplastik olarak poliolefinler grubunun en önemli üyesi olan AYPE, YYPE ve PP kullanılırken takviye elemanı olarak ise toz halde sentetik boya atıkları kullanılmıştır. Farklı parametreler kullanılarak enjeksiyon kalıplama yoluyla 819 numune üretilmiş ve her numune üzerinde mekanik çekme, üç nokta eğme ve izod darbe testleri gerçekleştirilmiştir. GA-LSTM modeli, kullanılan parametreler ve deneysel süreç boyunca elde edilen sonuçlarla eğitilmiş ve tahmin edilen değerlerin gerçek değerlere karşılık geldiği belirlenmiştir. Hibrit GA-LSTM modelinin başarısını ölçmek için iyi bilinen yöntemler kullanılmıştır. Elde edilen sonuçlara göre tasarlanan GA-LSTM modeli en iyi sonuçları üretmiştir.

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• MakaleAçık Erişim2024/r/munzur/6264
  Hybrid ConvLSTM Model for Wind Speed Prediction: A Case Study for India's Highest Wind Speed Cities

  Utku, Anıl; Akyol, Sinem

  Wind speed prediction is very important for logistics, energy production and renewable energy sources, aviation and maritime, agriculture, disaster management, environmental monitoring, construction, life planning and economic activities. In this study, it was aimed to predict the wind speeds of Jaisalmer, Kochi, Mangalore, Puri and Rameswaram, which are among the cities with the highest wind speeds in India. ConvLSTM hybrid model was developed by taking advantage of the effective features of CNN and LSTM models for wind speed prediction. With ConvLSTM, it is aimed to better determine the dynamics of wind speed by processing spatial and temporal data simultaneously. ConvLSTM has been extensively tested using an approximately 15-year hourly dataset consisting of RF, SVM, ANFIS, CNN and LSTM and wind speed measurements at 10 meters and 100 meters’ altitudes. Experimental results show that ConvLSTM has R2 values above 0.9 for almost all of the city and wind speed parameters, and outperforms the compared models with the lowest error values in terms of RMSE and MAE error metrics.

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  AtıfOpenAlex1·TR Dizin1

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