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
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ZERO HUNGER2
ZERO HUNGER
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
3
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QUALITY EDUCATION4
QUALITY EDUCATION
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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
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
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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
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CLIMATE ACTION13
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LIFE BELOW WATER14
LIFE BELOW WATER
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LIFE ON LAND15
LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
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PARTNERSHIPS FOR THE GOALS17
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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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Now showing 1 - 10 of 72
  • Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Discriminant-based bistability analysis of a TMG-induced lac operon model supported with boundedness and local stability results
    (Tubitak Scientific & Technological Research Council Turkey, 2016) Levent Cavas; Neslihan Avcu; Hakan Alyuruk; Güleser Kalaycı Demir; Cüneyt GÜZELİŞ; Ferhan PEKERGİN; Pekergin, Ferhan; Demir, Güleser Kalayci; Guzelis, Cuneyt; Kalayci Demir, Guleser; Cavas, Levent; Avcu, Neslihan; Alyuruk, Hakan
    This paper presents the results of a theoretical and numerical study on the analysis of bistable behavior of the most studied gene regulatory network the lac operon in terms of the model parameters. The boundedness of the state variables for the considered model are demonstrated the parameter values providing the existence of the multiple equilibria and thus the bistable behavior are determined and a local stability analysis of the equilibria is performed. The parameter region yielding the existence of multiple equilibria is determined in an algebraic way based on discriminants. The model given in the state equation form is defined by the ordinary differential equations with the rational right-hand sides constituted within Hill and Michaelis Menten approaches based on enzyme kinetics. The presented method can also be used in the parametric studies of other gene regulatory and metabolic networks given by state equations with rational right hand sides.
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    Conference Object
    Investigation of Chaotic Mixing Performance on Characteristic Properties of Cake Batter
    (IEEE, 2019) Ruhan Askin Uzel; Tolga Tugay Izmir; Gokhan Demirkiran; Savas Sahin; Cuneyt Guzelis; Uzel, Ruhan Askin; Izmir, Tolga Tugay; Demirkiran, Gokhan; Guzelis, Cuneyt; Tugay Izmir, Tolga; Sahin, Savas
    Chaotification is the process of making an originally non chaotic system being chaotic by applying a suitable control input. The aim of the study was to create a chaotic mixing mechanism using a kitchen type mixer and to test its performance on the quality characteristics of cake batter material. A prototype mixer that works originally with conventional method has been realized with hardware and software changes on a commercial kitchen type mixer. The results obtained at the end of the study showed that the kitchen type mixer was able to successfully switch from the classical mixing mode to the chaotic mixing mode and this mixing mode positively affected the structural and sensory characteristics of the cake batter samples.
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    Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root-locus method
    (INST ENGINEERING TECHNOLOGY-IET, 2019) Neslihan Avcu; Cuneyt Guzelis; Guzelis, Cuneyt; Avcu, Neslihan
    Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non-linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root-locus-based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of which the system shows a certain dynamical behaviour such as bistability oscillation and asymptotically equilibrium dynamics is shown by considering two mostly studied gene regulatory networks namely Gardner's genetic toggle switch and p53 gene network possessing two-phase (mono-stable/oscillation) dynamics.
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    Conference Object
    Citation - Scopus: 6
    Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mert Nakıp; Alperen Helva; Cüneyt Güzeliş; Volkan Rodoplu; Guzelis, Cuneyt; Rodoplu, Volkan; Helva, Alperen; Nakip, Mert
    We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT. © 2021 Elsevier B.V. All rights reserved.
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    Master Thesis
    Destek vektör regresyonu için örnek tabanlı düzenleme
    (2023) Keleşoğlu, Nur; Güzeliş, Cüneyt
    Regression analysis is a statistical method used in machine learning to estimate the relationship between two or more quantitative variables in business, finance, economics, engineering, and other disciplines. In this thesis, we proposed a sample based regularization algorithm that augments the cost function of Support Vector Regression (SVR) to increase generalization ability. We aim to increase the generalization capability of SVR, which is a Support Vector Machine (SVM) based machine learning model generally used for regression problems. Contrary to the current approaches to the generalization ability problem, we have improved the performance of the SVR model by increasing the generalization ability of the model. We reduce the weights towards zero, decrease the number of support vectors of the SVR model and increase the generalization ability of the model by the proposed method. In order to see the effect of our approach on the performance of the model, we compared it with the conventional regression machine learning models. We also compared the results of the proposed model with the results of a recent study. In order to evaluate the performance of our approach, we compared the R2 score metric, Mean Square Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) error metrics. We also present the training and execution times of the models. Moreover, we show the implementation of the SVR model with a sample based regularization in indoor fire and forest fire detection applications. Accordingly, we develop a Hybrid SR-SVR architecture with the novel sample based regularization for SVR. We compared this method with the machine learning models. In addition, we present our results in terms of Accuracy, Precision, Recall, F1 score, True Positive Rate (TPR), and True Negative Rate (TNR). The study presented in this thesis contributes to the Support Vector Regression literature by introducing an efficient regularization method, so called sample based regularization.
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    Book Part
    Citation - WoS: 1
    Citation - Scopus: 3
    Learning to move an object by the humanoid robots by using deep reinforcement learning
    (IOS Press, 2021) Simge Nur Aslan; Burak Taşçi; Ayşegül Uçar; Cüneyt Güzeliş; Tasci, Burak; Guzelis, Cuneyt; Ucar, Aysegul; Aslan, Simge Nur
    This paper proposes an algorithm for learning to move the desired object by humanoid robots. In this algorithm the semantic segmentation algorithm and Deep Reinforcement Learning (DRL) algorithms are combined. The semantic segmentation algorithm is used to detect and recognize the object be moved. DRL algorithms are used at the walking and grasping steps. Deep Q Network (DQN) is used to walk towards the target object by means of the previously defined actions at the gate manager and the different head positions of the robot. Deep Deterministic Policy Gradient (DDPG) network is used for grasping by means of the continuous actions. The previously defined commands are finally assigned for the robot to stand up turn left side and move forward together with the object. In the experimental setup the Robotis-Op3 humanoid robot is used. The obtained results show that the proposed algorithm has successfully worked. © 2021 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 1
    Effect of Chaotic Mixing on the Rheological Characterization of Mayonnaise
    (IEEE, 2017) Seda Genc; Tolga Tugay Izmir; Ruhan Askin Uzel; Gokhan Demirkiran; Savas Sahin; Cuneyt Guzelis; Uzel, Ruhan Askin; Izmir, Tolga Tugay; Demirkiran, Gokhan; Guzelis, Cuneyt; Genc, Seda; Sahin, Savas
    In this study a new mixing method for mayonnaise was developed. A chaotic hand mixer was designed. The speed of the mixer rotor was chaotically changed with the proposed method. Performance of the mixed mayonnaise was evaluated by a rheological characterization method. The results showed that the proposed chaotic mixing for mayonnaise has a better performance in terms of energy efficiency than conventional mixed ones indicating potential use in industry or as home appliance.
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    Master Thesis
    Nesnelerin interneti için anomali tahminlemesi
    (2022) Çıkmazel, Rıfat Orhan; Rodoplu, Volkan; Güzeliş, Cüneyt
    Anomali tahminlemesi, anomalilerin geçmişte meydana gelmesine bağlı olarak gelecekte meydana gelecek anomalilerin tahmin edilmesi problemini ifade eder. Bu tez çalışmasında, akıllı şehirlerin IoT verilerinde meydana gelen anomalileri tahmin etmek için ``Çok Çözünürlüklü Seviyeler Arası İyileştirme (MR-ILR)'' adlı yeni bir mimari geliştirdik. Anomali tahminlemesi problemine yönelik mevcut yaklaşımların aksine, mimarimiz IoT zaman serisi verilerini birden çok zaman çözünürlüğünde işleyerek tahminler yapar ve bu tahminleri iyileştirmek için ardışık çözünürlüklerdeki tahminleri birleştirir. Mimarimiz üç modülden oluşmaktadır: Birincisi, ``Seviyeler Arası VEYA'' modülü, IoT verilerindeki geçmiş anomalileri temsil eden vektörün mantıksal VEYA'sını alır ve giderek daha kaba çözünürlüklerde bir anomalinin oluşumunu temsil eder. İkincisi, bir Çok Katmanlı Algılayıcı (MLP), herhangi bir belirli çözünürlükte anomalinin oluşumunu tahmin eder. Üçüncüsü, ardışık çözünürlüklerdeki anomali tahminleri, daha doğru anomali tahminleri üretmek için ``İyileştirici'' modüllerinde birleştirilir. Mimarimiz, farklı zamansal çözünürlüklerde anomali tahminleri üretme esnekliğini sağlamaktadır. Mimarimizin performansını değerlendirmek için MR-ILR mimarimizin performansı MLP ve Uzun Kısa Süreli Bellek (LSTM) kıyaslama modelleriyle karşılaştırılmaktadır. Sonuçlar, mimarimizin F1 puanına göre bu iki kıyaslama modelinden önemli ölçüde daha iyi performans sergilediğini göstermektedir. Bu tez çalışmasında tasarladığımız mimari, IoT verilerindeki zorlu bir problem olan anomali tahminlemesinin çözümünde önemli bir ilerlemeyi temsil etmektedir ve çok daha geniş alanlardaki anomali tahminlemesinin hedeflendiği problemlerde uygulanma potansiyeline sahiptir.
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    Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization
    (Elsevier Ltd, 2024) Mert Nakıp; Nur Kelesoglu; Cüneyt Güzeliş; Guzelis, Cueneyt; Kelesoglu, Nur; Nakip, Mert
    We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination facilitating the integration of l1 regularization into the SVR method thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires benchmarking against 15 machine learning models including state-of-the-art techniques such as Recurrent Trend Predictive Neural Network (rTPNN) Long-Short Term Memory (LSTM) Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire enabling early preventive measures. Notably it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR facilitated by flattening-samples based augmented regularization allows for high detection performance even with smaller training sets. © 2024 Elsevier B.V. All rights reserved.
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    Article
    Citation - WoS: 13
    Citation - Scopus: 14
    Spatiotemporal chaotification of delta robot mixer for homogeneous graphene nanocomposite dispersing
    (ELSEVIER, 2020) Savas Sahin; Ali Emre Kavur; Sibel Demiroglu Mustafov; Ozgur Seydibeyoglu; Ozgun Baser; Yalcin Isler; Cuneyt Guzelis; Seydibeyoglu, Ozgur; Kavur, Ali Emre; Baser, Ozgun; Guzelis, Cuneyt; Sahin, Savas; Isler, Yalcin; Demiroglu Mustafov, Sibel; Mustafov, Sibel Demiroglu
    This paper presents the design implementation and polymer nanocomposite mixing application of a robust spatiotemporal chaotic delta robot. Blending fluids efficiently is a vital process for the preparation of graphene nanocomposite mixing. The most commonly used mixing materials are polymeric materials that need to be blended in non-Newtonian fluids. To achieve a superior blending performance over the conventional ones it is used two different chaotification mechanisms for the realization of the spatiotemporal chaotic delta robot mixer system. One of them is for the chaotification of the mixer propeller while the second one is for the chaotification of the three-dimensional position of the endpoint of the delta robot. The model-based robust chaotification scheme based on sliding mode control is applied to chaotify the speed of the delta robot-mixer via dynamical state-feedback chaotification method. The chaotification of 3D position of the mixer is realized in a feedforward way by producing chaotic input signals. The implemented robust chaotic delta robot mixer exploits the efficacy of chaotic mixing in obtaining homogeneity in the mixture with less operation time and hence reduced electrical energy consumption. In these performance evaluations energy consumption and material characterization which are measured by reliable material characterization methods such as X-ray diffraction Fourier-transform-infrared spectroscopy water contact angle dynamical mechanical analysis atomic force microscopy Raman and field emission-scanning electron microscope analyses are used as criteria. The obtained results show that for the delta robot the proposed chaotic-speed together with 3D chaotic-movement operation mode provides a better mixing performance than other mixing operation modes. (C) 2020 Elsevier B.V. All rights reserved.