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Computational Ecology: Artificial Neural Networks and Their Applications

โœ Scribed by Wenjun Zhang


Publisher
World Scientific Publishing Company
Year
2010
Tongue
English
Leaves
538
Category
Library

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โœฆ Synopsis


Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed. Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods. This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

โœฆ Table of Contents


Cover
Front Matter
Fundamentals of
Network Biology
Copyright
Preface
About the Author
Acknowledgments
Contents
Part 1: Mathematical Fundamentals
1 Fundamentals of Graph Theory
2 Graph Algorithms
3 Fundamentals of Network Theory
4 Other Fundamentals
Part 2: Crucial Nodes/Subnetworks/
Modules, Network Types,
and Structural Comparison
5 Identification of Crucial Nodes
and Subnetworks/Modules
6 Detection of Network Types
7 Comparison of Network Structure
Part 3: Network Dynamics, Evolution,
Simulation, and Control
8 Network Dynamics
9 Network Robustness and
Sensitivity Analysis
10 Network Control
11 Network Evolution
12 Cellular Automata
13 Self-Organization
14 Agent-based Modeling
Part 4: Flow Analysis
15 Flow/Flux Analysis
Part 5: Link and Node Prediction
16 Link Prediction:
Sampling-based Methods
17 Link Prediction: Structure- and
Perturbation-based Methods
18 Link Prediction:
Node-Similarity-based Methods
19 Node Prediction
Part 6: Network Construction
20 Construction of Biological Networks
Part 7: Pharmacological and Toxicological
Networks
21 Network Pharmacology and
Toxicology
Part 8: Ecological Networks
22 Food Webs
Part 9: Microscopic Networks
23 Molecular and Cellular Networks
Part 10: Social Networks
24 Social Network Analysis
Part 11: Software
25 Software for Network Analysis
Part 12: Big Data Analytics
26 Big Data Analytics for
Network Biology
References
Index


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