Güzeliş, Cüneyt

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12656
Name Variants
Job Title
Prof.Dr.
Email Address
Main Affiliation
01.01.09.02. Elektrik- Elektronik Mühendisliği
Status
Current Staff
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ORCID ID
0000-0001-5416-368X
Scopus Author ID
55937768800
Turkish CoHE Profile ID
Google Scholar ID
lX-9vKAAAAAJ
WoS Researcher ID
S-7282-2019
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cuneyt.guzelis.jpg (7.97 KB)

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
3
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
1
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
4
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
3
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
1
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
1
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Documents

16

Citations

734

Go to WoS profile
Scholarly Output

72

Articles

31

Views / Downloads

0/3

Supervised MSc Theses

9

Supervised PhD Theses

0

WoS Citation Count

378

Scopus Citation Count

621

Patents

0

Projects

5

WoS Citations per Publication

5.25

Scopus Citations per Publication

8.63

Open Access Source

20

Supervised Theses

9

JournalCount
IEEE Access5
IET Systems Biology4
10th International Conference on Electrical and Electronics Engineering ELECO 20173
Innovations in Intelligent Systems and Applications Conference (ASYU)2
2020 Innovations in Intelligent Systems and Applications Conference ASYU 20202
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Scholarly Output Search Results

Now showing 1 - 10 of 72
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 4
    Semantic Segmentation for Object Detection and Grasping with Humanoid Robots
    (Institute of Electrical and Electronics Engineers Inc., 2020) Guzelis, Cuneyt; Ucar, Aysegul; Aslan, Simge Nur
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    Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Two-dimensional polynomial type canonical relaxation oscillator model for p53 dynamics
    (Institution of Engineering and Technology journals@theiet.org, 2018) Gökhan Demirkıran; Güleser Kalayci Demir; Cüneyt Güzeliş; Güzelis, Cüneyt; Demirkiran, Gökhan; Demir, Güleser Kalayci
    p53 network which is responsible for DNA damage response of cells exhibits three distinct qualitative behaviours, low state oscillation and high state which are associated with normal cell cycle progression cell cycle arrest and apoptosis respectively. The experimental studies demonstrate that these dynamics of p53 are due to the ATM and Wip1 interaction. This paper proposes a simple two-dimensional canonical relaxation oscillator model based on the identified topological structure of ATM and Wip1 interaction underlying these qualitative behaviours of p53 network. The model includes only polynomial terms that have the interpretability of known ATM and Wip1 interaction. The introduced model is useful for understanding relaxation oscillations in gene regulatory networks. Through mathematical analysis we investigate the roles of ATM and Wip1 in forming of these three essential behaviours and show that ATM and Wip1 constitute the core mechanism of p53 dynamics. In agreement with biological findings we show that Wip1 degradation term is a highly sensitive parameter possibly related to mutations. By perturbing the corresponding parameters our model characterizes some mutations such as ATM deficiency and Wip1 overexpression. Finally we provide intervention strategies considering our observation that Wip1 seems to be an important target to conduct therapies for these mutations. © 2018 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    24-hour Electricity Consumption Forecasting for Day Ahead Market with Long Short Term Memory Deep Learning Model
    (IEEE, 2020) Nalan Ozkurt; Hacer Sekerci Oztura; Cuneyt Guzelis; Guzelis, Cuneyt; Oztura, Hacer Sekerci; Ozkurt, Nalan
    In 2015 with the foundation of Energy Market Management Inc. AS (EPIAS) the production and pricing of electrical energy began to be made according to consumption estimates. In this study twenty-four hours energy consumption forecasting was made by using long short-term memory method and data was downloded from EPIAS's official web page for the Day Ahead Market. The data set used covers 1500 days between June 2016 and July 2020. The results obtained have been compared with EPIAS's own estimates and actual consumption data.
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    Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Revealing determinants of two-phase dynamics of P53 network under gamma irradiation based on a reduced 2D relaxation oscillator model
    (Institution of Engineering and Technology journals@theiet.org, 2018) Gökhan Demirkıran; Güleser Kalayci Demir; Cüneyt Güzeliş; Guzelis, Cuneyt; Demirkiran, Gokhan; Demir, Guleser Kalayci
    This study proposes a two-dimensional (2D) oscillator model of p53 network which is derived via reducing the multidimensional two-phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables and studies the impact of p53-regulators on cell fate decision. First the authors identify a 6D core oscillator module then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations' effects using the phase space approach. © 2018 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 4
    Converting Utility Meters from Analogue to Smart based on Deep Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2020) Humberto J.Cabeza Barreto; Ilker Kurtulan; Suleyman Inci; Mert Nakıp; Cüneyt Güzeliş; Barreto, Humberto J Cabeza; Kurtulan, Ilker; Guzelis, Cuneyt; Inci, Suleyman; Nakip, Mert
    In this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 16
    An Implementation of Vision Based Deep Reinforcement Learning for Humanoid Robot Locomotion
    (Institute of Electrical and Electronics Engineers Inc., 2019) Recep Ozalp; Çaǧri Kaymak; Özal Yildirim; Ayşegül Uçar; Yakup Demir; Cüneyt Güzeliş; Ozaln, Recen; Yildirum, Ozal; Guzelis, Cuneyt; Kaymak, Cagri; Ucar, Ayscgul; Demir, Yakup; P. Koprinkova-Hristova , T. Yildirim , V. Piuri , L. Iliadis , D. Camacho
    Deep reinforcement learning (DRL) exhibits a promising approach for controlling humanoid robot locomotion. However only values relating sensors such as IMU gyroscope and GPS are not sufficient robots to learn their locomotion skills. In this article we aim to show the success of vision based DRL. We propose a new vision based deep reinforcement learning algorithm for the locomotion of the Robotis-op2 humanoid robot for the first time. In experimental setup we construct the locomotion of humanoid robot in a specific environment in the Webots software. We use Double Dueling Q Networks (D3QN) and Deep Q Networks (DQN) that are a kind of reinforcement learning algorithm. We present the performance of vision based DRL algorithm on a locomotion experiment. The experimental results show that D3QN is better than DQN in that stable locomotion and fast training and the vision based DRL algorithms will be successfully able to use at the other complex environments and applications. © 2020 Elsevier B.V. All rights reserved.
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    Master Thesis
    Nesnelerin interneti için altuzay tabanlı uygulamaya özgü hata metriği öykünmesi ile bütünleşik tahminleme-çizelgeleme
    (2021) Helva, Alperen; Rodoplu, Volkan; Güzeliş, Cüneyt
    The massive access problem refers to the challenge posed in uplink wireless communication from a massive number of Internet of Things (IoT) devices to an IoT gateway, base station or access point. In this thesis, first, we present an Application-Specific Error Function (ASEF), which measures the impact of the forecasting error on network performance for Joint Forecasting-Scheduling (JFS). Second, we propose a Neural Network (NN)-based emulation of ASEF on a subspace of forecasting errors, which we call ``Emulation of ASEF'' (E-ASEF), and develop a novel algorithm, ``Motion On a Subspace under Adaptive Learning rate'' (MOSAL), which moves on this subspace of forecasting errors while minimizing the application-specific error metric at the output of MAC-layer scheduling. Our results show that MOSAL improves the performance of the JFS system while achieving a low execution time. This work paves the way to achieving high network performance at an IoT Gateway that has a massive number of IoT devices in its coverage area.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things
    (Academic Press, 2022) Mert Nakıp; Erdem Çakan; Volkan Rodoplu; Cüneyt Güzeliş; Çakan, Erdem; Rodoplu, Volkan; Güzeliş, Cüneyt; Nakıp, Mert
    The Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance these solutions depend heavily on the performance of forecasters. Hence the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end in this paper first we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second based on this framework we develop the Dynamic Automatic Forecaster Selection (DAFS) method which selects the best-performing forecasting scheme for predictive access in particular for Joint Forecasting-Scheduling (JFS) in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least 80% of the best performance that can be attained for both throughput and energy consumption. Furthermore we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices. © 2022 Elsevier B.V. All rights reserved.
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    Conference Object
    Machine Learning Enabled Sleep Time Estimation (MLE-STE) Architecture for Indoor Positioning in Energy-Efficient Mobile Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alper Saylam; Cüneyt Güzeliş; Volkan Rodoplu; Guzelis, Cuneyt; Rodoplu, Volkan; Saylam, Alper
    Indoor positioning and tracking systems require not only accurate position estimates of mobile IoT devices but also energy efficiency in order to maximize the battery life of the mobile IoT device. The contribution of this paper is the design of a machine learning enabled indoor positioning and tracking system in which artificial intelligence is utilized for the estimation of the duration for which a mobile IoT device needs to sleep in order to conserve energy. Our Machine Learning Enabled Sleep Time Estimation (MLE-STE) architecture is comprised of the following stages: First it forms the forecast of the nearfuture trajectory of the mobile IoT device. Second based on these forecasts it determines the optimal sleep duration subject to the constraint of a maximum tolerable forecasting error. We demonstrate that our MLE-STE architecture outperforms both of the following state-of-the-art algorithms in this area: Positioning Interval based on Displacement (PID) and Dynamic Positioning Interval Based on Reciprocal Forecasting Error (DPI-RFE). This work represents a significant advance in the development of accurate indoor positioning and tracking algorithms that target the energy efficiency of mobile IoT devices. © 2024 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    A 2-dimensional reduced oscillator model with rational nonlinearities for p53 dynamics
    (Institute of Electrical and Electronics Engineers Inc., 2017) Gökhan Demirkıran; Güleser Kalayci Demir; Cüneyt Güzeliş; Guzelis, Cuneyt; Demirkiran, Gokhan; Demir, Guleser Kalayc
    p53 tumour suppressor network plays the key role in DNA damage response of the cell. We present a comprehensive 2-dimensional oscillator model for p53 network dynamics. The model is reduced from the known 17-dimensional two-phase model of p53 network which shows temporary oscillatory behaviour under DNA damage type of Double Strand Breaks. The introduced oscillator model shows the same qualitative dynamics of two-phase model of p53 network such as bistability and oscillations when the parameters are adjusted accordingly. With the help of the identified oscillator in p53 network we introduce a new oscillator perspective: p53 network has an oscillator in the centre which other systems in the cell manipulate this oscillator to contribute to cell fate. The introduction of such low dimensional oscillator model will make it possible to study p53 network and the effects of other biological systems in the context of oscillations. © 2023 Elsevier B.V. All rights reserved.