Kılıç, Hürevren

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Profile URL
https://hdl.handle.net/20.500.14411/6745
Name Variants
H., Kilic
Hürevren, Kılıç
Hurevren, Kilic
Kiliç H.
H.,Kilic
H.,Kılıç
H., Kılıç
K., Hürevren
K.,Hurevren
Kılıç,H.
Kilic,H.
Hürevren Kılıç
K.,Hürevren
K., Hurevren
Kilic H.
Kılıç H.
Kılıç, Hürevren
Kilic, Hurevren
Kilic,Hurevren
Job Title
Profesör Doktor
Email Address
hurevren.kilic@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Website
https://www.atilim.edu.tr/tr/bilgisayar-muhendisligi-ana-bilim-dali/page/1620/akademik-kadro
ORCID ID
0000-0002-9058-0365
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
1
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
2
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
1
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

34

Articles

8

Views / Downloads

179/1615

Supervised MSc Theses

10

Supervised PhD Theses

0

WoS Citation Count

66

Scopus Citation Count

63

Patents

0

Projects

0

WoS Citations per Publication

1.94

Scopus Citations per Publication

1.85

Open Access Source

2

Supervised Theses

10

JournalCount
International Journal of Engineering Education3
Proceedings of the 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007 -- 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST 2007 -- 21 February 2007 through 23 February 2007 -- Cairns -- 702542
2014 IEEE Symposium on Intelligent Agents (IA) -- DEC 09-12, 2014 -- Orlando, FL1
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS1
26th Annual International Symposium on Computer and Information Science -- SEP 26-28, 2011 -- Royal Soc London, London, ENGLAND1
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Subject: automata networks

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 6
    An Automata Networks Based Preprocessing Technique for Artificial Neural Network Modelling of Primary Production Levels in Reservoirs
    (Elsevier, 2007) Kilic, Hurevren; Soyupak, Selcuk; Tuzun, Ilhami; Ince, Ozlem; Basaran, Gokben
    Primary production in lakes and reservoirs develops as a result of complex reactions and interactions. Artificial neural networks (ANN) emerges as an approach in quantification of primary productivity in reservoirs. Almost all of the past ANN applications employed input data matrices whose vectors represent either water quality parameters or environmental characteristics. Most of the time, the components of input matrices are determined using expert opinion that implies possible factors that affect output vector. Major disadvantage of this approach is the possibility of ending-up with an input matrix that may have high correlations between some of its components. In this paper, an automata networks (AN) based preprocessing technique was developed to select suitable and appropriate constituents of input matrix to eliminate redundancy and to enhance calculation efficiency. The proposed technique specifically provides an apriori rough behavioral modeling through identification of minimal AN interaction topology. Predictive ANN models of primary production levels were developed for a reservoir following AN based pre-modeling step. The achieved levels of model precisions and performances were acceptable: the calculated root mean square error values (RMSE) were low; a correlation coefficient (R) as high as 0.83 was achieved with an ANN model of a specific structure. (c) 2006 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 5
    Automata Networks as Preprocessing Technique of Artificial Neural Network in Estimating Primary Production and Dominating Phytoplankton Levels in a Reservoir
    (Elsevier, 2006) Kilic, Hurevren; Soyupak, Selcuk; Gurbuz, Hasan; Kivrak, Ersin
    Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdoven Dam Reservoir. The primary purpose of using preprocessing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA. (c) 2006 Elsevier B.V. All rights reserved.