Bilgiç, Burcu

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Profile URL
https://hdl.handle.net/20.500.14411/7812
Name Variants
Bilgic Ucak, Burcu
Burcu, Bilgiç
Bilgic, Burcu
B., Burcu
B.,Burcu
B., Bilgic
B.,Bilgic
Bilgic,B.
B.,Bilgiç
Burcu, Bilgic
Bilgiç, Burcu
Bilgiç,B.
Job Title
Araştırma Görevlisi
Email Address
bilgic.burcu@atilim.edu.tr
Main Affiliation
Department of Electrical & Electronics Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
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Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

2

ZERO HUNGER
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0

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11

SUSTAINABLE CITIES AND COMMUNITIES
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1

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14

LIFE BELOW WATER
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6

CLEAN WATER AND SANITATION
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1

NO POVERTY
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0

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5

GENDER EQUALITY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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17

PARTNERSHIPS FOR THE GOALS
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15

LIFE ON LAND
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10

REDUCED INEQUALITIES
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7

AFFORDABLE AND CLEAN ENERGY
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8

DECENT WORK AND ECONOMIC GROWTH
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4

QUALITY EDUCATION
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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3

GOOD HEALTH AND WELL-BEING
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1

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13

CLIMATE ACTION
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This researcher does not have a Scopus ID.
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Scholarly Output

4

Articles

2

Views / Downloads

14/141

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

24

Scopus Citation Count

42

WoS h-index

3

Scopus h-index

3

Patents

0

Projects

0

WoS Citations per Publication

6.00

Scopus Citations per Publication

10.50

Open Access Source

2

Supervised Theses

1

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JournalCount
29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK1
Journal of Applied Statistics1
Sustainability1
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Now showing 1 - 4 of 4
  • Thumbnail Image
    Article
    Citation - WoS: 17
    Citation - Scopus: 17
    A New Outlier Detection Method Based on Convex Optimization: Application To Diagnosis of Parkinson's Disease
    (Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-Wilhelm
    Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.
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    Article
    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.
  • Thumbnail Image
    Master Thesis
    900 Mhz'de Yere Yakın Antenlerle Deneysel Radyo Yayılımıçalışması
    (2020) Bilgiç, Burcu; Kara, Ali; Aydın, Elif
    Bu çalışmanın amacı, dış ortamdaki radyo sinyal yayılımını deneysel sonuçlara göre analiz etmektir. Bu tez, Ankara'nın çevresindeki kırsal ve banliyö ortamlardan alınan deneysel verileri içermektedir. Beş farklı sahadaki GHz altı yol kaybı deneyleri gösterilmiştir. Yol kaybı ölçümleri farklı alıcı ve verici anten yüksekliklerinde yapılmıştır. Buna ek olarak, açık ve kapalı görüş sahalarındaki deneysel çalışmalardan elde edilen eğri uydurma yöntemleri sunulmuştur. Bu araştırma, farklı dış ortamlardaki linklerde kablosuz cihazların etkin kullanımını açıklamıştır. Bu tez, farklı anten yükseklikleri ve farklı arazi çeşitlerinde, yol kayıplarını modellemek ve yeni formüller oluşturmak için eğri uydurma yöntemiyle kataloglama sistemi oluşturmayı sağlamıştır. Hem anten yüksekliği etkisi hem arazi yapısının yol kaybına etkisi yeni modellerle sunulmuştur. Ayrıca, literatürde yer alan modellerle oluşturulan yeni model karşılaştırılmıştır.
  • Thumbnail Image
    Conference Object
    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.