Bankacılık, müşterilerle sık sık iletişime girilmesi gereken bir sektördür. Bankalar müşterilerine, onların durumlarına uygun bir kredi vermek istediğinde müşteriyi telefonla ararlar. Çoğu zaman müşteri, teklif edilen krediyi reddeder, bu da müşteriyle iletişime geçen personelin zamanından büyük bir kayıptır. Bu çalışmada, banka müşterilerinin verilerinin bulunduğu veri seti ele alınarak ve çeşitli makine öğrenmesi sınıflama modelleri kullanılarak müşterinin kredi alıp almayacağı tahmin edilmiştir. Elde edilen çalışma sonuçlarına göre, makine öğrenmesi yöntemleri ile müşterinin kredi alma eğilim tahmini başarılı bir şekilde gerçekleşmiştir. Çalışma sonucunda K-Best uygulanan modellerin arasında doğruluk değeri en yüksek olan sınıflandırıcı modelinin %98,86 ile Rastgele Orman algoritması olduğu, özellik seçimi yapılmadan eğitilen modellerin arasında en yüksek olan modelin %93,66 ile Rastgele Orman algoritması olduğu, cross-validation ve grid search ile eğitilen modellerin arasında ise en yüksek değerin %98,6 ile Rastgele Orman algoritmasında olduğu görülmüştür.
The image obtained from the cameras is 2D, so we cannot know how far the object is on the image. In order to detect objects only at a certain distance in a camera system, we need to convert the 2D image into 3D. Depth estimation is used to estimate distances to objects. It is the perception of the 2D image as 3D. Although different methods are used to implement this, the method to be applied in this experiment is to detect depth perception with a single camera. After obtaining the depth map, the obtained image will be filtered by objects in the near distance, the distant image will be closed, a new image will be run with the object detection model and object detection will be performed. The desired result in this experiment is, for projects with a low budget, instead of using dual camera or LIDAR methods, it is to ensure that a robot can detect obstacles that will come in front of it with only one camera. As a result, 8 FPS was obtained by running two models on the embedded device, and the loss value was obtained as 0.342 in the inference test performed on the new image, where only close objects were taken after the depth estimation.
There are too many cattle in the world and too many breeds of cattle. For someone who is new to cattle breeding, it may be difficult to tell which species their cattle are. In some cases, an experienced person may not understand the breeds of two cattle that are similar in appearance. In this study, the aim is to classify the cattle species with image processing methods and mobile applications written in Flutter and TensorFlow Lite. For classifying breeds, The VGG-16 algorithm was used for feature extraction. XGBoost and Random Forest algorithms were used for classification and the combined versions of the two methods were compared. In addition, SMOTE algorithm and image augmentation algorithms were used to prevent the imbalance of the dataset, the performance results of the combined versions of the two methods were compared. Images of different cattle species from different farms were obtained and the dataset was prepared, then trained image classification models and tested the trained models. As a result of performance tests, it’s obtained that the best model is VGG16+Random Forest+SMOTE+Augmentation with 88.77% accuracy result for this study. In the mobile application, first the cattle is detected with a pre-trained object detection model, and then the breed classification of the cattle on the image is made with image classification model.
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