In this study, a flipped classroom was compared with blended learning and face-to-face learning environments and the aim was to identify the effect of these learning environments on students’ achievements, academic engagement and satisfaction levels. Based on this aim, one control and two experimental groups were formed. The students in Experiment-I learned the theoretical aspect of the course through the materials in the online learning environment which were prepared prior to the class study time in context with the “flipped classroom”. The students in Experiment-II learned the theoretical aspect of the course in the classroom in accordance with the blended learning model and fulfilled active learning activities out of class study time. The students in the control group learned the theoretical aspect of the course in the classroom according to the current curriculum and conducted active learning activities at their homes. It was found that the scores for the students in the experimental groups with regard to academic achievement and engagement were higher than the scores for those in the control group and the differences between the groups were statistically significant. It was revealed that the students were also generally satisfied with the flipped classroom.

Early diagnosis and accurate treatment is crucial in increasing the survival rate of diseases that can result in death, such as breast cancer. Therefore, there is a greater need for artificial intelligence systems that will help doctors make decisions in health care, especially in fatal diseases. Because these systems are not affected by human nature factors such as distraction, stress etc. so that they can distinguish small and important issues that could be overlooked, especially in the scan results of the patient. The aim of this study is to predict whether a mass can be identified in breast and whether the mass found in the breast is benign or malignant with the help of machine learning which is a sub-study area of artificial intelligence. In this study, the images in the mini-MIAS database are used. Firstly, unwanted areas were eliminated. Then Gauss, Average, Median and Wiener filters were applied to reduce noise and smoothing the images and an algorithm based on Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to make suspicious areas more visible. New data sets were created by using HOG (Histogram of Oriented Gradients), LBP (Local Binary Pattern), GLCM (Gray Level Co-occurrence Matrices) for feature extraction and correlation (COR) for feature selection. Selected features were classified in three different categories (normal, benign, malignant) and two different categories (normal, abnormal) using. Different machine learning algorithms (C5.0 (normal and boosted), Naive Bayes, CART and Random Forest) were applied to the data sets and the performances were compared. According to the research findings, to decide whether there was a breast mass, the highest accuracy value was calculated by applying Median and Wiener filters together, equating histogram with CLAHE and using the GLCM feature extraction method on the data set and the accuracy was found 0.657 with Naive Bayes algorithm. When trying to find out whether the mass found in the breast is benign or malignant, Median was applied together with Weiner Filter, equating histogram with CLAHE and HOG feature extraction method was used, and the accuracy was calculated as 0,660 with Random Forest algorithm.

Skin cancer is a common disease among middle-aged and elderly white-skinned people. It is divided into many types in terms of medical criteria. Malign melanoma is one of the most dangerous and fatal cancer types, it can be treated if detected early. The main focus of this paper is to provide a precise, effective, robust and automated way to segment the lesion in order to facilitate the classification of the lesion with high accuracy during the early diagnosis of skin cancer. This process consists of two stages. In the first stage, image processing techniques (Image Enhancement, Linear Filtering, and Image Restoration) are used to obtain images free of artifacts such as hair and ruler marks. The second stage which is the important part of this paper is to modify a U-Net architecture and propose a 46-layered structure of U-Net to obtain a successful lesion segmentation rate. In this study, experiments were performed on two different U-Net architectures (U-Net 32, and U-Net 46).

Most of the criminal acts are performed using criminal tools. One of the most effective ways of preventing crime is to observe and detect offensive weapons by security camera systems. Deep learning techniques can show very high-performance in observing and perceiving objects. In the current study, the performances of the pre-trained AlexNet, VGG16, and VGG19 models based on convolutional neural networks, were tested for the detection and classification of criminal tools such as guns and knives. In the study, the training process was carried out using transfer learning approaches such as Fine-tuning and Training from scratch based on deep architectures. To test the deep architectures used in the proposed study, the gun and knife datasets frequently used in the literature were collected and combined with new datasets obtained originally from search engines and videos, and then their performances were tested. In the experimental results, the VGG16 model based on fine-tuning for the two and three classes achieved the highest accuracy in detecting criminal devices with a rate of 99.73% and 99.67%, respectively. As a result, the study has observed that offensive weapons could be detected with security cameras using learned weights of deep architectures

Bugün web siteleri, milyarlarca insan tarafından kullanılmaktadır. Bir kişinin/topluluğun/kurumun veya markanın; web sitesi aracılığı ile daha çok kişiye ulaşabilmesinde, takipçileri tarafından kabul görmesinde ve böylelikle hedeflediği başarıyı yakalayabilmesinde, site ziyaretçilerinin ihtiyaçlarının tespit edebilmesi son derece önemlidir. Tespit edilen bu ihtiyaçlar, bir web sitesinin tasarım ve içerik yönünden geliştirilmesinde kilit rol oynamaktadır. Bu çalışmanın amacı; site-içi aramalar ve apriori algoritması kullanılarak web sitesi ziyaretçilerinin ihtiyaç tespitine yönelik bir örnek olay incelemesi sunmaktır. Bu kapsamda, veri seti olarak Kırklareli Üniversitesi web sitesinden (www.klu.edu.tr) elde edilen bir aylık web günlük dosyası kullanılmıştır. Analiz süreci, Veri Madenciliği için Çapraz Endüstri Standard Süreç Modeli (CRISP-DM: CRoss-Industry Standard Process for Data Mining) çerçevesinde ele alınmıştır. Üniversite kayıtlarıyla ilgili işlemlerin yoğun olarak gerçekleştirildiği aya yönelik yapılan analizler sonucunda; ziyaretçiler tarafından gerçekleştirilen aramalarda çoğunlukla “Yatay Geçiş”, “Kayıt Yenileme”, “Ders Programı”, “Ders Kayıtları”, “Harç” ve “Kontenjanlar” kelime/kelime gruplarının bulunduğu gözlemlenmiştir. Apriori algoritması ile gerçekleştirilen analizler sonucunda, sırasıyla “Yatay Geçiş” (1177 kez), “Harç” (889 kez) ve “Ders Programı” (600 kez) aramalarının yapılan tüm aramaların başında geldiği tespit edilmiştir. Üniversite web sitesinin daha iyi hizmet vermesine olanak tanıyacak; “Ders Programı ve Yatay Geçiş aramalarını yapan ziyaretçilerin %60’ı Harç kelimesini de aramıştır” şeklinde çeşitli birliktelik kuralları çalışmada paylaşılmıştır.