國立虎尾科技大學 |

Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques./
Author:	Mashayekhi, Morteza.
Description:	1 online resource (163 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781321804713

Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques.
Mashayekhi, Morteza.

Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques. - 1 online resource (163 pages)

Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.

Thesis (Ph.D.)--University of Windsor (Canada), 2015.

Includes bibliographical references

Artificial life (Alife) studies the logic of living systems in an artificial environment in order to gain a deeper insight of the complex processes and governing rules in such systems. EcoSim, an Alife simulation for ecological modeling, is an individual-based predator-prey ecosystem simulation and a generic platform designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes in biology and ecology.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321804713Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques.
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100 1 $a Mashayekhi, Morteza. $3 1186756
245 1 0 $a Individual-Based Modeling and Data Analysis of Ecological Systems using Machine Learning Techniques.
264 0 $c 2015
300 $a 1 online resource (163 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.
500 $a Adviser: Robin Gras.
502 $a Thesis (Ph.D.)--University of Windsor (Canada), 2015.
504 $a Includes bibliographical references
520 $a Artificial life (Alife) studies the logic of living systems in an artificial environment in order to gain a deeper insight of the complex processes and governing rules in such systems. EcoSim, an Alife simulation for ecological modeling, is an individual-based predator-prey ecosystem simulation and a generic platform designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes in biology and ecology.
520 $a Speciation and extinction of species are two essential phenomena in evolutionary biology. Many factors are involved in the emergence and disappearance of species. Due to the complexity of the interactions between different factors, such as interaction of individuals with their environment, and the long time required for the observation, studying such phenomena is not easy in the real world. Using data sets obtained from EcoSim and machine learning techniques, we predicted speciation and extinction of species based on numerous factors. Experimental results showed that factors, such as demographics, genetics, and environment are important in the occurrence of these two events in EcoSim.
520 $a We identified the best species-area relationship (SAR) models, using EcoSim, along with investigating how sampling approaches and sampling scales affect SARs. Further, we proposed a machine learning approach, based on extraction of rules that provide an interpretation of SAR coefficients, to find plausible relationships between the models' coefficients and the spatial information that likely affect SARs. We found the power function family to be a reasonable choice for SAR. Furthermore, the simple power function was the best ranked model in nested sampling amongst models with two coefficients. For some of the SAR model coefficients, we obtained clear correlations with spatial information, thereby providing an interpretation of these coefficients.
520 $a Rule extraction is a method to discover the rules explaining a predictive model of a specific phenomenon. A procedure for rule extraction from Random Forest (RF) is proposed. The proposed methods are evaluated on eighteen UCI machine learning repository and four microarray data sets. Our experimental results show that the proposed methods outperform one of the state-of-the art methods in terms of scalability and comprehensibility while preserving the same level of accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Windsor (Canada). $b Computer Science. $3 1186757
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3706527 $z click for full text (PQDT)