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Conference Object Citation - WoS: 1An Undergraduate Curriculum for Deep Learning(Ieee, 2018) Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, MuratDeep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.Conference Object Citation - WoS: 1Parking Space Occupancy Detection Using Deep Learning Methods(Ieee, 2018) Akinci, Fatih Can; Karakaya, MuratThis 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 efiicent 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.Conference Object Citation - Scopus: 2Enhancing Image Resolution With Generative Adversarial Networks(Institute of Electrical and Electronics Engineers Inc., 2022) Yildiz,B.Super-resolution is the process of generating high-resolution images from low-resolution images. There are a variety of practical applications used in real-world problems such as high-definition content creation, surveillance imaging, gaming, and medical imaging. Super-resolution has been the subject of many researches over the past few decades, as improving image resolution offers many advantages. Going beyond the previously presented methods, Generative Adversarial Networks offers a very promising solution. In this work, we will use the Generative Adversarial Networks-based approach to obtain 4x resolution images that are perceptually better than previous solutions. Our extensive experiments, including perceptual comparison, Peak Signal-to-Noise Ratio, and classification success metrics, show that our approach is quite promising for image super-resolution. © 2022 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.Conference Object Citation - Scopus: 2An Undergraduate Curriculum for Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2018) Tirkes,G.; Ekin,C.C.; Engul,G.; Bostan,A.; Karakaya,M.Deep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields. © 2018 IEEE.Article Citation - WoS: 1Citation - Scopus: 2Precision Forecasting for Hybrid Energy Systems Using Five Deep Learning Algorithms for Meteorological Parameter Prediction(Elsevier Sci Ltd, 2025) Ceylan, Ceren; Yumurtaci, ZehraThe intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current literature applies single-algorithm based on each individual energy source and less multi-algorithm based on comparative studies on multiple architectures as applied to integrated hybrid systems. In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). Using five years of Istanbul meteorological data (2018-2022), optimal algorithms were systematically identified for each parameter through rigorous hyperparameter optimization and cross-validation. Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 degrees C, R2: 0.9976). The integrated approach predicted annual hybrid system energy production with 89 % accuracy, demonstrating 0.48 % deviation from observed values. Most significantly, our framework successfully forecasted sixth year (2023) energy production with 1.55 % error, validating its real-world applicability. This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures.Conference Object An Undergraduate Curriculum for Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2018) Tirkes,G.; Ekin,C.C.; Engul,G.; Bostan,A.; Karakaya,M.Deep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields. © 2018 IEEE.Conference Object Multi-Label Movie Genre Detection From Movie Posters Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2024) Yılmaz, A.A.In the field of cinema, the concept of genre has emerged as a concept that basically includes films that have common characteristics in terms of subject matter, have adopted a common method, and have a low risk of error because they have been tried before. Identifying the genres of movies is a challenging task because genres are intangible features that are not physically present in any movie scene, so off-the-shelf image detection models may not be easily integrated into this process. In this study, we aim to address the detection of movies according to their genres using deep learning algorithms. Movie poster data of IMDB and MM-IMDB datasets were utilized in our multi-label movie genre detection studies. In our experiments, we utilized four modern pre-trained models follow as DenseNet, VGG-16, ResNet-50, and MobileNet, and evaluated their performance using performance metric values such as accuracy, precision, recall, and F-score. According to the obtained empirical results, the DenseNet architecture achieved the highest accuracy values compared to other deep learning methods in detecting multi-label movie genre detection with an impressive rates of 91.64% and 92.56%. © 2024 IEEE.Article Deep Learning Based Covid-19 Detection Using Computed Tomography Images(Prof.Dr. İskender AKKURT, 2024) Yılmaz, A.A.; Sevinç, Ö.The infectious coronavirus disease (COVID-19), seen in Wuhan city of China in December 2019, led to a global pandemic, resulting in countless deaths. The healthcare sector has become extensively use of deep learning (DL), a method that is currently quite popular. The aim of this study is to identify the best and most successful deep learning model and optimizer approach combination for COVID-19 diagnosis. For this reason, several DL methods and optimizer techniques are tested on two comprehensive public data set to select the best DL model with optimizer technique. A variety of performance evaluation metrics, including f-score, precision, specificity, and accuracy, were used to assess the models' effectiveness. The experimental results show that the most suitable and effective architecture is DenseNet-201 in the network comparison, which achieved a 98% accuracy rate using the AdaGrad optimizer and 200 iterations. © IJCESEN.
