Tander, Baran

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Profile URL
https://hdl.handle.net/20.500.12469/6960
Name Variants
Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, B
Job Title
Dr. Öğr. Üyesi
Email Address
[email protected]
Main Affiliation
Computer Engineering
Status
Former Staff
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WoS Researcher ID

Sustainable Development Goals

4

QUALITY EDUCATION
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0

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6

CLEAN WATER AND SANITATION
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10

REDUCED INEQUALITIES
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13

CLIMATE ACTION
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14

LIFE BELOW WATER
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2

ZERO HUNGER
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8

DECENT WORK AND ECONOMIC GROWTH
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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17

PARTNERSHIPS FOR THE GOALS
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1

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1

NO POVERTY
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11

SUSTAINABLE CITIES AND COMMUNITIES
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15

LIFE ON LAND
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3

GOOD HEALTH AND WELL-BEING
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3

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7

AFFORDABLE AND CLEAN ENERGY
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1

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5

GENDER EQUALITY
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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Scholarly Output

12

Articles

3

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Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

2

Scopus Citation Count

16

WoS h-index

1

Scopus h-index

3

Patents

0

Projects

0

WoS Citations per Publication

0.17

Scopus Citations per Publication

1.33

Open Access Source

6

Supervised Theses

1

JournalCount
2015 9th International Conference on Electrical and Electronics Engineering (ELECO)1
2018 Medical Technologies National Congress (TIPTEKNO)1
2020 Medical Technologies Congress (TIPTEKNO)1
Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference1
Neural Processing Letters1
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Now showing 1 - 10 of 12
  • Thumbnail Image
    Conference Object
    Mobile Application Development for the Estimation of Recurrence in Post-Operative Kidney Cancer Cases
    (IEEE, 2018) Tander, Baran; Özmen, Atilla; Ozden, Ender
    In this paper a post-operative recurrence estimation tool called Sorbellini's nomogram for the kidney cancer patients showing no metastates is introduced and a novel application for mobile devices based on this model is developed for the physician's follow up procedures. The TNM stage tumor size nuclear (Fuhrman) grade the existance of necrosis and vascular invasion are employed as the input parameters for this software to predict the recurrence probability in mentioned patients. Finaly the performance analyses are carried out to verify the reliability of the application.
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    Article
    Hücresel Sinir Ağları için Gerilim Kaynaklı Hücre Modelleri
    (AVES YAYINCILIK, 2001) Tander, Baran; Ün, Mahmut
    Bu makalede, bağımsız ve bağımlı gerilim kaynağı tabanlı yeni bir Hücresel Sinir Ağı hücre devresi önerilmiştir. Bu modelde akım kaynaklı Chua ve Yang ‘ın klasik hücre devresinin aksine hücreler için denge noktaları dinamik birimdeki Rx ve Cx’ den bağımsızdırlar. Tam bir hücre devresi tasarlanıp kararlı ve kararsız durumlar için benzetimleri yapılmıstır. Önerilen modelin avantaj ve dezavantajları sonuçlar bölümünde tartışılmıştır.
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    Conference Object
    Analytical Approaches for the Amplitude and Frequency Computations in the Astable Cellular Neural Networks With Opposite Sign Templates
    (IEEE, 2007) Tander, Baran; Özmen, Atilla
    In this paper, by using surface fitting methods, analytical approaches for amplitudes and frequencies of the x(1,2)(t) "States" in a simple dynamical neural network called "Cellular Neural Network with Opposite Sign Templates" which was proposed by Zou and Nossek [1], are obtained under oscillation conditions. The mentioned explicit expressions are employed in a cellular neural network based, amplitude and frequency tuneable oscillator design.
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    Conference Object
    A Numerical Method for Frequency Determination in the Astable Cellular Neural Networks With Opposite-Sign Templates
    (IEEE, 2006) Özmen, Atilla; Tander, Baran
    In this study a numerical method is proposed to determine the oscillation frequencies in the astable cellular neural networks with opposite-sign templates [1]. This method depends on the training of a multilayer perceptron that uses various template coefficients and the correspondant frequency values as inputs and outputs. First of all a frequency surface is obtained from templates and then training samples are picked from this surface in order to apply to multilayer perceptron. The effects of the template coefficients to the oscillation frequencies are also investigated. Furthermore an oscillator design is carried out for simulation and the performance as well as the advantages of the proposed method are evaluated.
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    Conference Object
    Citation - Scopus: 1
    Channel Equalization With Cellular Neural Networks
    (IEEE, 2010) Özmen, Atilla; Tander, Baran
    In this paper a dynamic neural network structure called Cellular Neural Network (CNN) is employed for the equalization in digital communication. It is shown that this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled simple CNN containing 9 neurons thus having only 19 weight coefficients. Proposed system is compared with linear transversal filters as well as with a Multilayer Perceptron (MLP) based equalizer.
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    Conference Object
    Citation - Scopus: 4
    Smart Stethoscope
    (IEEE, 2020) Türker, Mehmet Nasuhcan; Çagan, Yagiz Can; Yıldırım, Batuhan; Demirel, Mücahit; Özmen, Atilla; Tander, Baran; Çevik, Mesut
    In this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is developed to help the health workers to make accurate diagnoses. Furthermore, the respiratory diseases are classified by using Deep Learning and Long Short-Term Memory (LSTM) techniques whereas the probability of these diseases are obtained.
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    Conference Object
    Citation - WoS: 1
    Design and Implementation of a Cellular Neural Network Based Oscillator Circuit
    (World Scientific and Engineering Acad and Soc, 2009) Tander, Baran; Özmen, Atilla; Özçelep, Yasin
    In this paper, a novel inductorless oscillator circuit with negative feedbacks, based on a simple version of a "Cellular Neural Network" (CNN) called "CNN with an Opposite Sign Template" (CNN-OST) is designed and implemented. The system is capable of generating quasi-sine oscillations with tuneable amplitude and frequency which can't be provided at the same time in the conventional oscillator circuits.
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    Article
    Amplitude and Frequency Modulations With Cellular Neural Networks
    (Springer, 2015) Tander, Baran; Özmen, Atilla
    Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting gsm etc. However the architectures of these individual systems are totally different. In this paper it is shown that a cellular neural network with an opposite-sign template can behave either as an amplitude or a frequency modulator. Firstly a brief information about these networks is given and then the amplitude and frequency surfaces of the generated quasi-sine oscillations are sketched with respect to various values of their cloning templates. Secondly it is proved that any of these types of modulations can be performed by only varying the template components without ever changing their structure. Finally a circuit is designed simulations are presented and performance of the proposed system is evaluated. The main contribution of this work is to show that both amplitude and frequency modulations can be realized under the same architecture with a simple technique specifically by treating the input signals as template components.
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    Other
    Simple and Accurate Cell Macromodels for the Simulations of Cellular Neural Networks
    (AVES YAYINCILIK, 2002) Tander, Baran; Ün, Mahmut
    In this paper, two simple and accurate cell macromodels for PSPICE simulations of Cellular Neural Networks (CNNs) are designed. Firstly, a brief information about CNNs and their benefits are introduced. Then the nonlinear differential equations that characterize the CNNs and the equivalent cell circuit given by Chua and Yang which realizes these equations are presented. With appropriate source transformations, another cell equivalent that employs voltage controlled-voltage sources instead of voltage controlled-current sources is developed. By substituting the dependent sources with their actual circuits for both equivalents, complete systems which are suitable for PSPICE macromodeling are derived. Responses of astable and stable CNNs are analyzed with the proposed macromodels and satisfactory results are observed after the simulations. The benefits and drawbacks of the macromodels are also discussed in the conclusion section.
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    Master Thesis
    Estimation of Energy Production in Biogas Plants
    (Kadir Has Üniversitesi, 2023) YURTEKİN, ŞEVVAL AYŞE; Özmen, Atilla; Tander, Baran
    The importance of renewable energy sources is getting more and more significant day by day. Renewable energies are required since the world's energy consumption is rising in tandem with the human population. The gas produced from wastes such as biogas, agricultural waste, and animal dung is an example of biomass energy, a renewable energy source. Machine learning is a branch of computer science that tries to improve its performance with the data it accumulates over time by simulating a human learning mechanism. This research begins with a discussion of biogas generation and investigations. The discussion then shifts to artificial intelligence, machine learning, and neural networks. In the application portion, an application of feature selection utilizing data, wastes, and biogas production from the Pales biogas plant is developed, as well as an application that forecasts biogas output using regression and an artificial neural network model. In the Python-based model, machine learning and deep learning libraries are utilized, and the data is preprocessed to make it compatible with the model. In this 22-featured model, the elements that contribute to the model, specifically biogas generation, were chosen using feature selection algorithms. The regression model and neural network model were created with the selected features as Dairy cow manure, Wheat Juice, Potato peel, Potato whole, Mixed vegie, Weak vinasse and Poultry manure. There are three distinct digesters included in the data. Three separate analyses were performed on three distinct digesters, and the findings were compared to the cumulative data. In the study, 20% of the data were set aside as test data and 80% were used for training. In terms of model performance, the data feature selection approach was effective for regression models, but negatively effect of variable reduction in neural networks. The R2 score was 52% in the neural network model trained without variable selection, and the mean of regression models trained with feature selection was 49%.