Person: KARATAŞ BAYDOĞMUŞ, GÖZDE
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KARATAŞ BAYDOĞMUŞ
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Publication Metadata only Disease detection using deep learning slgorithms on the hardware platforms(2023-01-01) KARATAŞ BAYDOĞMUŞ, GÖZDE; KARATAŞ BAYDOĞMUŞ G., Cicekli N. Z.Covid-19 virus, which emerged at the end of 2019, brought human life to a standstill all over the world, causing many people to become permanently ill and die. Since its emergence, the health system has come to the point of collapse with its rapid spread all over the world. Despite the uninterrupted work of healthcare professionals and fighting with their whole selves, this virus spreaded rapidly and infected many people in the world and caused death. Covid-19 virus also caused permanent lung damage in some of the people who survived this disease. In this article, an answer is sought to detect the virus that causes Covid-19 disease by using machine learning methods. The aim of the study is to detect the Covid-19 disease quickly and to start the treatment process immediately. In this work, different models were designed using X-Ray images of patients with and without Covid-19 disease, and among these models, the most accurate and fastest result was proposed. In this sense, sample data were produced from existing data by applying Zoom Range, Shear Range and Horizontal Flip data augmentation methods, since data on Covid-19 is not much. In addition, improvements were made using CNN, VGG16, DenseNet121 and ResNet50 deep learning methods to design proposed model. Since the main aim of the study is to achieve the highest accuracy rate quickly, the performances of deep learning algorithms in different working environments were evaluated. CPU, GPU and TPU are used for this. As a result of experimental studies, it has been observed that all algorithms working with GPU work faster with or without data augmentation. In addition, although deep learning algorithms have been successful in working with big data, it has been seen in this study that there is no need for data augmentation for Covid-19 disease detection such a dataset. By examining such image data on the GPU with any deep learning algorithm proposed in this study, we can detect the disease successfully and quickly.Publication Metadata only Covid-19 Disease Detection with Improved Deep Learning Algorithms on X-Ray Data(2022-06-27) KARATAŞ BAYDOĞMUŞ, GÖZDE; Cicekli N. Z., KARATAŞ BAYDOĞMUŞ G.Publication Metadata only Makine öğrenimi ile saldırı tespitinde veri kümesini alt-örneklemenin etkileri(2022-01-01) KARATAŞ BAYDOĞMUŞ, GÖZDE; DEMİR, ÖNDER; Emanet Ş., KARATAŞ BAYDOĞMUŞ G., DEMİR Ö., MELENLİ S.Publication Metadata only Solution for TSP/mTSP with an improved parallel clustering and elitist ACO(2023-01-01) KARATAŞ BAYDOĞMUŞ, GÖZDE; KARATAŞ BAYDOĞMUŞ G.Many problems that were considered complex and unsolvable have started to solve and new technologies have emerged through to the development of GPU technology. Solutions have established for NP-Complete and NP-Hard problems with the acceleration of studies in the field of artificial intelligence, which are very interesting for both mathematicians and computer scientists. The most striking one among such problems is the Traveling Salesman Problem in recent years. This problem has solved by artificial intelligence’s metaheuristic algorithms such as Genetic algorithm and Ant Colony optimization. However, researchers are always looking for a better solution. In this study, it is aimed to design a low-cost and optimized algorithm for Traveling Salesman Problem by using GPU parallelization, Machine Learning, and Artificial Intelligence approaches. In this manner, the proposed algorithm consists of three stages; Cluster the points in the given dataset with K-means clustering, find the shortest path with Ant Colony in each of the clusters, and con-nect each cluster at the closest point to the other. These three stages were carried out by parallel programming. The most obvious difference of the study from those found in the literature is that it performs all calculations on the GPU by using Elitist Ant Colony Optimization. For the experimental results, examinations were carried out on a wide variety of datasets in TSPLIB and it was seen that the proposed parallel KMeans-Elitist Ant Colony approach increased the performance by 30% compared to its counterparts.