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Öğe 3D kidney and tumor segmentation with Multi Depth V-Net model(Kırıkkale Üniversitesi, 2020) Türk, Fuat; Lüy, Murat; Barışçı, NecaattinKidney cancer is an important type of cancer that spreads rapidly today. Although many treatment methods for kidney cancer have been developed in recent years, current studies are still ongoing. These studies enable treatment information that offers new hope to the lives of kidney cancer patients. When the studies are examined, it seems to be an important alternative in medical segmentation. Although the disease can progress insidiously, sometimes patients may not even have a serious complaint until the last stage. Therefore, segmentation is important for early diagnosis and diagnosis. In this study, it has been prepared in mind in order to help physicians. Here, successful results were obtained by making improvements on the Multi Depth V-Net model. The membrane coefficient of 0.949 and 0.944 for Multi Depth V-Net model and V-Net model kidney segmentation, and 0.841 and 0.830 for tumor segmentation, respectively. In line with the data obtained, we can say that V-Net models for kidney and tumor segmentation can be applied and give accurate results.Öğe Comparison of The Perturb and Observe, Increased Conductivity and Particle Swarm Optimization Algorithms for Maximum Power Point Tracking in Photovoltaic Systems(Kırıkkale Üniversitesi, 2021) Lüy, Murat; Türk, Fuat; Metin, Nuri AlperWith the increasing world population, the interest in renewable energy sources has increased in order to reduce the decrease in petroleum and its derivatives products used for energy production, and to minimize the damage caused by gases such as carbon monoxide and methane gas, which are produced as waste from these products. Examples of renewable energy sources are wind, fuel cell and solar panels. In the article study, in solar panel systems, Maximum Power Point Tracking (MPPT) with Direct Current (DC) converter and The Perturb and Observe (P and O), Increasing Conductivity and Particle Swarm Optimization (PSO) algorithms were designed in MATLAB/Simulink environment and simulation studies at variable radiation values has been carried out. As a result of the simulation studies, it has been observed that the PSO MPPT algorithm oscillates less at the variable radiation values at the maximum power point and reaches the maximum power point faster than the P and O and Increasing Conductivity algorithms.Öğe Embedded Systems and Application Areas in Engineering(Kırıkkale Üniversitesi, 2021) Türk, Fuat; Lüy, MuratEmbedded systems are one of the sub-branches of electrical and electronics engineering, but are also used in many service areas today. Embedded systems, which are almost the common point of Electronics and software fields, are preferred primarily by many people in these fields. For the programming of embedded systems, the support of different electronic cards and computers is needed. With the development of both hardware components and software programming languages, embedded systems have begun to be used in almost all engineering branches. In this research study, a comprehensive examination has been made those deals with the reasons why embedded systems are preferred in many engineering branches such as robotic systems, artificial intelligence and agricultural fields.Öğe Feature Selection in the Diabetes Dataset with the Marine Predator Algorithm and Classification using Machine Learning Methods(2024) Türk, Fuat; Metin, Nuri Alper; Lüy, MuratDiabetes, which is classified as one of the leading causes of mortality, is a chronic and intricate metabolic disorder defined by disruptions in the metabolism of carbohydrates, fats, and proteins. Type 1 diabetes is categorized alongside Type 2 diabetes, as well as other distinct kinds of diabetes, including gestational diabetes. Complications, both acute and chronic, manifest in individuals with diabetes due to diminished insulin secretion and disruptions in the metabolism of carbohydrates, fats, and proteins. Following the completion of the data preparation step, the diabetes dataset that was collected from Kaggle is then sent to the feature extraction module for analysis. After the optimization process has been completed, the feature selection block will determine which characteristics stand out the most. The selected traits discussed before are sorted into several categories using the categorization module. The findings are compared to those that would have been obtained if the marine predator optimization algorithm (MPOA) technique had not been carried out, specifically regarding metrics like the F1 score, Recall, Accuracy, and Precision. The findings indicate that the LR classification approach achieves an accuracy rate of 77.63% without property selection. However, when the characteristics are selected using the MPOA, the accuracy rate increases to 79.39%.Öğe Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods(Kırıkkale Üniversitesi, 2019) Türk, Fuat; Lüy, Murat; Barışçı, NecaattinKidney cancer isdifficult to diagnose and it can be quite complicated for physicians todiagnose. In this study, while providing information about multiple sources tohelp people who are dealing with the challenges of the diagnosis of kidneycancer, in order to serve as a guide the principles of kidney cancer are triedto be explained. In recent years, many new methods of treatment have beendeveloped for kidney cancer, and some are under development by scientists.These studies provide treatment information that offers new hope to the livesof kidney cancer patients. In this study, it is aimed to get acquainted withkidney cancer cells by using machine learning, and deep learning algorithms. Inthis way, an application can be developed to guide patients and physiciansthrough early diagnosis and classification.Öğe Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods(2019) Türk, Fuat; Lüy, Murat; Barışçı, NecaattinKidney cancer is difficult to diagnose and it can be quite complicated for physicians to diagnose. In this study, while providinginformation about multiple sources to help people who are dealing with the challenges of the diagnosis of kidney cancer, in orderto serve as a guide the principles of kidney cancer are tried to be explained. In recent years, many new methods of treatment havebeen developed for kidney cancer, and some are under development by scientists. These studies provide treatment informationthat offers new hope to the lives of kidney cancer patients. In this study, it is aimed to get acquainted with kidney cancer cells byusing machine learning, and deep learning algorithms. In this way, an application can be developed to guide patients andphysicians through early diagnosis and classification.Öğe Multi Depth V-Net Model ile 3 Boyutlu Böbrek ve Tümör Segmentasyonu(2020) Türk, Fuat; Lüy, Murat; Barışçı, NecaattinBöbrek kanseri günümüzde hızla yayılan önemli bir kanser türüdür. Son yıllarda, böbrek kanseri için birçok tedavi yöntemi geliştirilmekle birlikte mevcut çalışmalar halen devam etmektedir. Bu çalışmalar, böbrek kanseri hastalarının hayatlarına yeni bir umut sunan tedavi bilgilerini mümkün kılmaktadır. Çalışmalar incelendiğinde tıbbi segmentasyonda önemli bir alternatif gibi gözükmektedir. Hastalık sinsi ilerleyebilmekle beraber bazen son evreye kadar hastalarda ciddi bir şikâyet bile olmayabilir. Bu yüzden segmentasyon erken tanı ve teşhis için önem arz etmektedir. Bu çalışmada da hekimlere yardımcı olabilmek amacıyla düşünülerek hazırlanmıştır. Burada Multi Depth V-Net modeli üzerinde iyileştirmeler yapılarak başarılı sonuçlar elde edilmiştir. Multi Depth V-Net model ve V-Net model böbrek segmentasyonu için sırasıyla 0,949 ve 0,944 zar katsayısı, tümör segmentasyonu için de 0,841 ve 0,830 zar katsayısına ulaşmıştır. Elde edilen veriler doğrultusunda böbrek ve tümör segmentasyonu için V-Net modellerin uygulanabilir ve doğru sonuçlar verebildiğini söyleyebiliriz.Öğe Non-Destructive Prediction of Bread Staling Using Artificial Intelligence Methods(2023) Argun, Mustafa Şamil; Türk, Fuat; Kurt, AbdullahIn foods with limited shelf life and in new product development studies, it is important for producers and consumers to estimate the degree of staling with easy methods. Staling of bread, which has an essential role in human nutrition, is an important physicochemical phenomenon that affects consumer preference. Costly technologies, such as rheological, thermal, and spectroscopic approaches, are used to determine the degree of staling. This research suggests that an artificial intelligence-based method is more practical and less expensive than these methods. Using machine learning and deep learning algorithms, it was attempted to predict how many days old breads are, which provides information on the freshness status and degree of staling, from photos of whole bread and pieces of bread. Among the machine learning algorithms, the highest accuracy rate for slices of bread was calculated as 62.84% with Random Forest, while the prediction accuracy was lower for all bread images. The training accuracy rate for both slice and whole bread was determined to be 99% when using the convolutional neural network (CNN) architecture. While the test results for whole breads were around 56.6%, those for sliced breads were 92.3%. The results of deep learning algorithms were superior to those of machine learning algorithms. The results indicate that crumb images reflect staling more accurately than whole bread images.Öğe Optimized machine learning based predictive diagnosis approach for diabetes mellitus(2023) Akkur, Erkan; Türk, FuatAims: Diabetes mellitus is a metabolic disease caused by elevated blood sugar. If this disease is not diagnosed on time, it has the potential to pose a risk to other organs and tissues. Machine learning algorithms have started to preferred day by day in the detection of this disease, as in many other diseases. This study suggests a diabetes prediction approach incorporating optimized machine learning (ML) algorithms. Methods: The framework presented in this study starts with the application of different data pre-processing processes. Random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT) algorithms are used for classification. Grid search is utilized for hyperparameter optimization of algorithms. Different performance evaluation measures are used to find the algorithm that best predicts diabetes. PIMA Indian dataset (PID) is chosen for testing the experiments. In addition, it is investigated to what extent the attributes in the data set affect the result using Shapley additive explanations (SHAP) analysis. Results: As a result of the experiments, the RF algorithm achieved the highest success rate with 89.06%, 84.33%, 84.33%, 84.33% and 0.88% accuracy, precision, sensitivity, F1-score and AUC scores. As a result of the SHAP analysis, it is found that the “Insulin”, “Age” and “Glucose” attributes contributed the most to the prediction model in identifying patients with diabetes. Conclusion: The hyperparameter optimized RF approach proposed in the framework of the study provided a good result in the prediction and diagnosis of diabetes mellitus when compared with similar studies in the literature. As a result, an expert system can be designed to detect diabetes early in real time using the proposed method.Öğe U2-Net Segmentation And Multi-Label Cnn Classification Of Wheat Varieties(Konya Teknik Univ, 2024) Argun, Mustafa Şamil; Türk, Fuat; Civelek, ZaferThere are many varieties of wheat grown around the world. In addition, they have different physiological states such as vitreous and yellow berry. These reasons make it difficult to classify wheat by experts. In this study, a workflow was carried out for both segmentation of wheat according to its vitreous/yellow berry grain status and classification according to variety. Unlike previous studies, automatic segmentation of wheat images was carried out with the U2-NET architecture. Thus, roughness and shadows on the image are minimized. This increased the level of success in classification. The newly proposed CNN architecture is run in two stages. In the first stage, wheat was sorted as vitreous-yellow berry. In the second stage, these separated wheats were grouped by multi-label classification. Experimental results showed that the accuracy for binary classification was 98.71% and the multi-label classification average accuracy was 89.5%. The results showed that the proposed study has the potential to contribute to making the wheat classification process more reliable, effective, and objective by helping the experts.Öğe Yapay sinir ağı yöntemleriyle hipertansiyon teşhis sistemi geliştirilmesi(Kırıkkale Üniversitesi, 2012) Türk, Fuat; Barışçı, NecaattinGünümüzde birçok hastalığın tedavisi mümkün olduğundan, doğru teşhis büyükönem taşımaktadır. Bu sebepten hekimlere doğru ve hızlı teşhis koyabilmelerikonusunda yardımcı olabilmek için, Yapay Zekâ Yöntemlerinin geliştirilmesi sonderece önemlidir. Bu çalışmada 150 hastadan alınan veriler işlenerek bilgisayaraaktarılmıştır. Bu verilere Yapay Sinir Ağı (YSA) modelleri uygulanmıştır. Eldeedilen sonuçlar normal veya hasta olacak şekilde sınıflandırılmıştır. Böyleliklehekimlere hastalık teşhisi yaparken yardımcı olacak bir sistem geliştirilmiştir.
