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Conference Object Deep Learning and Current Trends in Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2018) Bostan,A.; Ekin,C.; Sengul,G.; Karakaya,M.; Tirkes,G.Academic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain. © 2018 IEEE.Conference Object Deep Learning and Current Trends in Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2018) Bostan,A.; Ekin,C.; Sengul,G.; Karakaya,M.; Tirkes,G.Academic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain. © 2018 IEEE.Conference Object Citation - Scopus: 8Parking space occupancy detection using deep learning methods;(Institute of Electrical and Electronics Engineers Inc., 2018) Akinci,F.C.; Karakaya,M.This paper presents an approach for gathering information about the availabilty of the parking lots using Convoltional Neural Network (CNN) for image processing running on an embedded system. By using an eflicent neural network model, we made it possible to use a very low cost embedded system compared to the ones used in previous works on this topic. This efficient model's performance is compared to one of the models that proved its accuracy in image classification competitions. In these tests, we used datasets that has thousands of different images taken from parking lots in different light and weather conditions. © 2018 IEEE.
