Kılıç, Hürevren

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https://hdl.handle.net/20.500.14411/6745
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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
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Profesör Doktor
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hurevren.kilic@atilim.edu.tr
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Computer Engineering
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https://www.atilim.edu.tr/tr/bilgisayar-muhendisligi-ana-bilim-dali/page/1620/akademik-kadro
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0000-0002-9058-0365
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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
International Journal of Engineering Education2
2014 IEEE Symposium on Intelligent Agents (IA) -- DEC 09-12, 2014 -- Orlando, FL1
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Author: Kilic, HurevrenSubject: automata networks

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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.