Browsing Bilgisayar ve Bilişim Bilimleri Fakültesi by Subject "Deep learning"

Now showing items 1-3 of 3

Automatic Detection and Classification of Defective Areas on Metal Parts by Using Adaptive Fusion of Faster R-CNN and Shape From Shading

Selamet, Feyza; Cakar, Serap; Kotan, Muhammed (2022-11-21)

Computer vision and deep learning approaches have an important role in industrial inspection systems. Computer vision technology is essential for fast, defect-free control of products in the production line. The importance ...
Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

Kahraman, Abdulgani; Kantardzic, Mehmet; Kotan, Muhammed (2022-09-28)

Industrial machinery is a significant energy consumer, and its CO2 emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. ...
Working Principles of Convolutional Neural Networks in Keras

Akgun, Devrim (Platanus Publishing, 2023-12-25)