https://hdl.handle.net/20.500.12619/844 2026-04-11T16:41:46Z 2026-04-11T16:41:46Z Kotan, Muhammed Seymen, Ömer Faruk https://hdl.handle.net/20.500.12619/102347 2024-07-08T14:04:39Z 2023-12-01T00:00:00Z

Makine Öğreniminde Öznitelik Seçme Yöntemlerinin Kullanımı: Güncel Python Uygulamaları Kotan, Muhammed; Seymen, Ömer Faruk

2023-12-01T00:00:00Z Kotan, Muhammed https://hdl.handle.net/20.500.12619/101334 2023-08-28T06:45:06Z 2022-12-01T00:00:00Z

Duygu Analizi ve Dijital Dönüşüm Üzerine Etkileri Kotan, Muhammed

2022-12-01T00:00:00Z Levent ÇALLI Sena KASIM https://hdl.handle.net/20.500.12619/100675 2023-05-31T08:19:25Z 2022-09-30T00:00:00Z

Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry Levent ÇALLI; Sena KASIM Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

2022-09-30T00:00:00Z Kotan, Muhammed Öz, Cemil Kahraman, Abdulgani https://hdl.handle.net/20.500.12619/99814 2023-02-01T08:51:28Z 2021-09-27T00:00:00Z

A Linearization–Based Hybrid Approach for 3D Reconstruction of Objects in a Single Image Kotan, Muhammed; Öz, Cemil; Kahraman, Abdulgani The shape-from-shading (SFS) technique uses the pattern of shading in images in order to obtain 3D view information. By virtue of their ease of implementation, linearization-based SFS algorithms are frequently used in the literature. In this study, Fourier coefficients of central differences obtained from grey-level images are employed, and two basic linearization-based algorithms are combined. By using the functionally generated surfaces and 3D reconstruction datasets, the hybrid algorithm is compared with linearization-based approaches. Five different evaluation metrics are applied to recovered depth maps and the corresponding grey-level images. The results on defective sample surfaces are also included to show the effect of the algorithm on surface reconstruction. The proposed method can prevent erroneous estimates of object boundaries and produce satisfactory 3D reconstruction results in a low number of iterations.

2021-09-27T00:00:00Z