Browsing by Author "Bilgic, Burcu"

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Citation - WoS: 3
Citation - Scopus: 20
Comparison of Breast Cancer and Skin Cancer Diagnoses Using Deep Learning Method
(Ieee, 2021) Bilgic, Burcu
Artificial intelligence applications are of great importance in the solution of cancer, which is one of the biggest health problems of our age. In this study, a study was conducted on deep learning methods that make life important in the early diagnosis of breast cancer and skin cancer, which are among the most common types of cancer worldwide. Breast cancer and skin cancer data were classified as benign and malignant by deep learning methods. While working with the deep learning method, the classification was made using the Convolutional Neural Network (CNN) algorithm. In this classification, the data are divided into benign cancer sets and malignant cancer sets. Finally, the data provided by the logistic regression method were analyzed and success charts were created and both types were compared. As a result, accuracy and loss graphs of both cancer types were formed. The aim of the study is to compare breast cancer and skin cancer with the deep learning method. And some breast cancer and skin cancer diagnoses are confused. In further studies, the basis of differentiating the diagnosis of these two types of cancer from each other was made in this study.
Citation - WoS: 4
Citation - Scopus: 5
Experimental Investigation of Communication Performance of Drones Used for Autonomous Car Track Tests
(Mdpi, 2021) Yildiz, Melih; Bilgic, Burcu; Kale, Utku; Rohacs, Daniel
Autonomous Vehicles (AVs) represent an emerging and disruptive technology that provides a great opportunity for future transport not only to have a positive social and environmental impact but also traffic safety. AV use in daily life has been extensively studied in the literature in various dimensions, however; it is time for AVs to go further which is another technological aspect of communication. Vehicle-to-Vehicle (V2V) technology is an emerging issue that is expected to be a mutual part of AVs and transportation safety in the near future. V2V is widely discussed by its deployment possibilities not only by means of communication, even to be used as an energy transfer medium. ZalaZONE Proving Ground is a 265-hectare high-tech test track for conventional, electric as well as connected, assisted, and automated vehicles. This paper investigates the use of drones for tracking the cars on the test track. The drones are planned to work as an uplink for the data collected by the onboard sensors of the car. The car is expected to communicate with the drone which is flying in coordination. For the communication 868 MHz is selected to be used between the car and the drone. The test is performed to simulate different flight altitudes of drones. The signal strength of the communication is analyzed, and a model is developed which can be used for the future planning of the test track applications.