Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2024Neural 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.
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İncele - MakaleAçık Erişim2024Scopus SJRQ1Spread patterns of COVID-19 in European countries: hybrid deep learning model for prediction and transmission analysis
Utku, Anıl; Akcayol, M. Ali
The COVID-19 pandemic has profoundly impacted healthcare systems and economies worldwide, leading to the implementation of travel restrictions and social measures. Efforts such as vaccination campaigns, testing, and surveillance have played a crucial role in containing the spread of the virus and safeguarding public health. There needs to be more research exploring the transmission dynamics of COVID-19, particularly within European nations. Therefore, the primary objective of this research was to examine the spread patterns of COVID-19 across various European countries. Doing so makes it possible to implement preventive measures, allocate resources, and optimize treatment strategies based on projected case and mortality rates. For this purpose, a hybrid prediction model combining CNN and LSTM models was developed. The performance of this hybrid model was compared against several other models, including CNN, k-NN, LR, LSTM, MLP, RF, SVM, and XGBoost. The empirical findings revealed that the CNN-LSTM hybrid model exhibited superior performance compared to alternative models in effectively predicting the transmission of COVID-19 within European nations. Furthermore, examining the peak of case and death dates provided insights into the dynamics of COVID-19 transmission among European countries. Chord diagrams were drawn to analyze the inter-country transmission patterns of COVID-19 over 5-day and 14-day intervals.
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İncele - MakaleAçık Erişim2023KOBİ’lerin Ekonomiye Sağladıkları Katkının Tahmini İçin Derin Öğrenme Tabanlı Model
Akcayol, M. Ali; Utku, Anıl; Sevinç, Ali
Küçük ve Orta Büyüklükteki İşletme (KOBİ)'ler, sermayesi, işgücü ve varlıkları, ulusal yönetmeliklere göre belirlenen eşik değerlerin altında olan özel sektör işletmeleridir. KOBİ'ler, özellikle gelişmekte olan ülkelerde olmak üzere dünyadaki çoğu ülkenin ekonomisinde önemli rol oynamaktadır. Dünya genelinde işletmelerin yaklaşık %90'ını oluşturan KOBİ'ler, istihdamın %50'sinden fazlasını sağlamaktadır. Ülke ölçeğinde KOBİ’lerin ekonomiye katkılarının tahin edilmesi planlama ve yatırım açısından oldukça önemlidir. Bu çalışmada, KOBİ’lerin ekonomiye sağladıkları katkının tahminine yönelik derin öğrenme tabanlı bir model geliştirilmiştir. Geliştirilen LSTM tabanlı derin öğrenme modelinin sonuçları, RF, SVM, CNN, MLP, RNN ve GRU ile karşılaştırılmıştır. Deneysel sonuçlar, geliştirilen derin öğrenme modelinin 2,169 MSE, 1,473 RMSE, 1,175 MAE ve 0,959 R2 değeri ile karşılaştırılan diğer modellerden daha başarılı tahmin performansına sahip olduğunu göstermiştir.
- MakaleAçık Erişim2023Deep Learning Based Video Event Classification
Gencaslan, Serim; Utku, Anil; Akcayol, M. Ali
In recent years, due to the growth of digital libraries and video databases, automatic detection of activities from videos and obtaining patterns from large datasets have come to the fore. Object detection from images is used as a tool for various applications and is the basis of video classification. Objects in videos are more difficult to identify than in single images, as the information in videos has a time-continuity constraint. Following the developments in the field of computer vision, the use of open source software packages for machine learning and deep learning and the developments in hardware technologies have enabled the development of new approaches. In this study, a deep learning-based classification model has been developed for the classification of sports branches in video. In the model developed using CNN, transfer learning has been applied with VGG-19. Experimental studies on 32827 frames using CNN and VGG-19 models showed that VGG-19 has a more successful classification performance than CNN with an accuracy rate of 83%.
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