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The Resource Artificial neural networks in biological and environmental analysis, Grady Hanrahan

Artificial neural networks in biological and environmental analysis, Grady Hanrahan

Label
Artificial neural networks in biological and environmental analysis
Title
Artificial neural networks in biological and environmental analysis
Statement of responsibility
Grady Hanrahan
Creator
Subject
Language
eng
Summary
  • "Drawing on the experience and knowledge of a practicing professional, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of artificial neural networks (ANNs) in modern environmental and biological analysis. Based on our knowledge of the functioning human brain, ANNs serve as a modern paradigm for computing. Presenting basic principles of ANNs together with simulated biological and environmental data sets and real applications in the field, this volume helps scientists comprehend the power of the ANN model to explain physical concepts and demonstrate complex natural processes"--Provided by publisher
  • "The cornerstones of research into prospective tools of artificial intelligence originate from knowledge of the functioning brain. Like most transforming scientific endeavors, this field-- once viewed with speculation and doubt--has had profound impacts in helping investigators elucidate complex biological, chemical, and environmental processes. Such efforts have been catalyzed by the upsurge in computational power and availability, with the co-evolution of software, algorithms, and methodologies contributing significantly to this momentum. Whether or not the computational power of such techniques is sufficient for the design and construction of truly intelligent neural systems is of continued debate. In writing Artificial Neural Networks in Biological and Environmental Analysis, my aim was to provide in-depth and timely perspectives on the fundamental, technological, and applied aspects of computational neural networks. By presenting basic principles of neural networks together with real applications in the field, I seek to stimulate communication and partnership among scientists in the fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued acquiescence of the use of neural network tools in scientific inquiry"--Provided by publisher
Member of
http://library.link/vocab/creatorName
Hanrahan, Grady
Dewey number
570.285/63
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
Analytical chemistry series
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Biology
  • Environmental engineering
  • Neural networks (Computer science)
Label
Artificial neural networks in biological and environmental analysis, Grady Hanrahan
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • The Biological Model
  • 1.2.2.
  • The Artificial Neuron Model
  • 1.3.
  • Neural Network Application Areas
  • 1.4.
  • Concluding Remarks
  • References
  • ch. 2
  • Network Architectures
  • Machine generated contents note:
  • 2.1.
  • Neural Network Connectivity and Layer Arrangement
  • 2.2.
  • Feedforward Neural Networks
  • 2.2.1.
  • The Perceptron Revisited
  • 2.2.2.
  • Radial Basis Function Neural Networks
  • 2.3.
  • Recurrent Neural Networks
  • ch. 1
  • 2.3.1.
  • The Hopfield Network
  • 2.3.2.
  • Kohonen's Self-Organizing Map
  • 2.4.
  • Concluding Remarks
  • References
  • ch. 3
  • Model Design and Selection Considerations
  • 3.1.
  • Introduction
  • In Search of the Appropriate Model
  • 3.2.
  • Data Acquisition
  • 1.1.
  • Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems?
  • 1.2.
  • Neural Networks: An Introduction and Brief History
  • 1.2.1.
  • 3.3.4.
  • Logarithmic Scaling
  • 3.3.5.
  • Principal Component Analysis
  • 3.3.6.
  • Wavelet Transform Preprocessing
  • 3.4.
  • Feature Selection
  • 3.5.
  • Data Subset Selection
  • 3.3.
  • 3.5.1.
  • Data Partitioning
  • 3.5.2.
  • Dealing with Limited Data
  • 3.6.
  • Neural Network Training
  • 3.6.1.
  • Learning Rules
  • 3.6.2.
  • Supervised Learning
  • Data Preprocessing and Transformation Processes
  • 3.6.2.1.
  • The Perceptron Learning Rule
  • 3.6.2.2.
  • Gradient Descent and Back-Propagation
  • 3.6.2.3.
  • The Delta Learning Rule
  • 3.6.2.4.
  • Back-Propagation Learning Algorithm
  • 3.6.3.
  • Unsupervised Learning and Self-Organization
  • 3.3.1.
  • 3.6.4.
  • The Self Organizing Map
  • 3.6.5.
  • Bayesian Learning Considerations
  • 3.7.
  • Model Selection
  • 3.8.
  • Model Validation and Sensitivity Analysis
  • 3.9.
  • Concluding Remarks
  • Handling Missing Values and Outliers
  • References
  • 3.3.2.
  • Linear Scaling
  • 3.3.3.
  • Autoscaling
  • 4.2.1.1.
  • Binary Encoding
  • 4.2.2.
  • Fitness and Objective Function Evaluation
  • 4.2.3.
  • Selection
  • 4.2.4.
  • Crossover
  • 4.2.5.
  • Mutation
  • Ch. 4
  • 4.3.
  • An Introduction to Fuzzy Concepts and Fuzzy Inference Systems
  • 4.3.1.
  • Fuzzy Sets
  • 4.3.2.
  • Fuzzy Inference and Function Approximation
  • 4.3.3.
  • Fuzzy Indices and Evaluation of Environmental Conditions
  • 4.4.
  • The Neural-Fuzzy Approach
  • Intelligent Neural Network Systems and Evolutionary Learning
  • 4.4.1.
  • Genetic Algorithms in Designing Fuzzy Rule-Based Systems
  • 4.5.
  • Hybrid Neural Network-Genetic Algorithm Approach
  • 4.6.
  • Concluding Remarks
  • References
  • ch. 5
  • Applications in Biological and Biomedical Analysis
  • 5.1.
  • 4.1.
  • Introduction
  • 5.2.
  • Applications
  • 5.2.1.
  • Enzymatic Activity
  • 5.2.2.
  • Quantitative Structure-Activity Relationship (QSAR)
  • Hybrid Neural Systems
  • 4.2.
  • An Introduction to Genetic Algorithms
  • 4.2.1.
  • Initiation and Encoding
  • Applications in Environmental Analysis
  • 6.1.
  • Introduction
  • 6.2.
  • Applications
  • 6.2.1.
  • Aquatic Modeling and Watershed Processes
  • 6.2.2.
  • Endocrine Disruptors
  • 6.2.3.
  • 5.2.3.
  • Ecotoxicity and Sediment Quality
  • 6.2.4.
  • Modeling Pollution Emission Processes
  • 6.2.5.
  • Partition Coefficient Prediction
  • 6.2.6.
  • Neural Networks and the Evolution of Environmental Change
  • Kudlak
  • 6.2.6.1.
  • Studies in the Lithosphere
  • Psychological and Physical Treatment of Maladies
  • 6.2.6.2.
  • Studies in the Atmosphere
  • 6.2.6.3.
  • Studies in the Hydrosphere
  • 6.2.6.4.
  • Studies in the Biosphere
  • 6.2.6.5.
  • Environmental Risk Assessment
  • 6.3.
  • Concluding Remarks
  • 5.2.4.
  • References
  • Prediction of Peptide Separation
  • 5.3.
  • Concluding Remarks
  • References
  • ch. 6
Control code
ocn746925664
Dimensions
unknown
Extent
1 online resource (xxii, 188 pages)
File format
unknown
Form of item
online
Isbn
9781439812594
Lccn
2010038461
Level of compression
unknown
Note
Taylor & Francis
Other physical details
illustrations (some color)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)746925664
Label
Artificial neural networks in biological and environmental analysis, Grady Hanrahan
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • The Biological Model
  • 1.2.2.
  • The Artificial Neuron Model
  • 1.3.
  • Neural Network Application Areas
  • 1.4.
  • Concluding Remarks
  • References
  • ch. 2
  • Network Architectures
  • Machine generated contents note:
  • 2.1.
  • Neural Network Connectivity and Layer Arrangement
  • 2.2.
  • Feedforward Neural Networks
  • 2.2.1.
  • The Perceptron Revisited
  • 2.2.2.
  • Radial Basis Function Neural Networks
  • 2.3.
  • Recurrent Neural Networks
  • ch. 1
  • 2.3.1.
  • The Hopfield Network
  • 2.3.2.
  • Kohonen's Self-Organizing Map
  • 2.4.
  • Concluding Remarks
  • References
  • ch. 3
  • Model Design and Selection Considerations
  • 3.1.
  • Introduction
  • In Search of the Appropriate Model
  • 3.2.
  • Data Acquisition
  • 1.1.
  • Artificial Intelligence: Competing Approaches or Hybrid Intelligent Systems?
  • 1.2.
  • Neural Networks: An Introduction and Brief History
  • 1.2.1.
  • 3.3.4.
  • Logarithmic Scaling
  • 3.3.5.
  • Principal Component Analysis
  • 3.3.6.
  • Wavelet Transform Preprocessing
  • 3.4.
  • Feature Selection
  • 3.5.
  • Data Subset Selection
  • 3.3.
  • 3.5.1.
  • Data Partitioning
  • 3.5.2.
  • Dealing with Limited Data
  • 3.6.
  • Neural Network Training
  • 3.6.1.
  • Learning Rules
  • 3.6.2.
  • Supervised Learning
  • Data Preprocessing and Transformation Processes
  • 3.6.2.1.
  • The Perceptron Learning Rule
  • 3.6.2.2.
  • Gradient Descent and Back-Propagation
  • 3.6.2.3.
  • The Delta Learning Rule
  • 3.6.2.4.
  • Back-Propagation Learning Algorithm
  • 3.6.3.
  • Unsupervised Learning and Self-Organization
  • 3.3.1.
  • 3.6.4.
  • The Self Organizing Map
  • 3.6.5.
  • Bayesian Learning Considerations
  • 3.7.
  • Model Selection
  • 3.8.
  • Model Validation and Sensitivity Analysis
  • 3.9.
  • Concluding Remarks
  • Handling Missing Values and Outliers
  • References
  • 3.3.2.
  • Linear Scaling
  • 3.3.3.
  • Autoscaling
  • 4.2.1.1.
  • Binary Encoding
  • 4.2.2.
  • Fitness and Objective Function Evaluation
  • 4.2.3.
  • Selection
  • 4.2.4.
  • Crossover
  • 4.2.5.
  • Mutation
  • Ch. 4
  • 4.3.
  • An Introduction to Fuzzy Concepts and Fuzzy Inference Systems
  • 4.3.1.
  • Fuzzy Sets
  • 4.3.2.
  • Fuzzy Inference and Function Approximation
  • 4.3.3.
  • Fuzzy Indices and Evaluation of Environmental Conditions
  • 4.4.
  • The Neural-Fuzzy Approach
  • Intelligent Neural Network Systems and Evolutionary Learning
  • 4.4.1.
  • Genetic Algorithms in Designing Fuzzy Rule-Based Systems
  • 4.5.
  • Hybrid Neural Network-Genetic Algorithm Approach
  • 4.6.
  • Concluding Remarks
  • References
  • ch. 5
  • Applications in Biological and Biomedical Analysis
  • 5.1.
  • 4.1.
  • Introduction
  • 5.2.
  • Applications
  • 5.2.1.
  • Enzymatic Activity
  • 5.2.2.
  • Quantitative Structure-Activity Relationship (QSAR)
  • Hybrid Neural Systems
  • 4.2.
  • An Introduction to Genetic Algorithms
  • 4.2.1.
  • Initiation and Encoding
  • Applications in Environmental Analysis
  • 6.1.
  • Introduction
  • 6.2.
  • Applications
  • 6.2.1.
  • Aquatic Modeling and Watershed Processes
  • 6.2.2.
  • Endocrine Disruptors
  • 6.2.3.
  • 5.2.3.
  • Ecotoxicity and Sediment Quality
  • 6.2.4.
  • Modeling Pollution Emission Processes
  • 6.2.5.
  • Partition Coefficient Prediction
  • 6.2.6.
  • Neural Networks and the Evolution of Environmental Change
  • Kudlak
  • 6.2.6.1.
  • Studies in the Lithosphere
  • Psychological and Physical Treatment of Maladies
  • 6.2.6.2.
  • Studies in the Atmosphere
  • 6.2.6.3.
  • Studies in the Hydrosphere
  • 6.2.6.4.
  • Studies in the Biosphere
  • 6.2.6.5.
  • Environmental Risk Assessment
  • 6.3.
  • Concluding Remarks
  • 5.2.4.
  • References
  • Prediction of Peptide Separation
  • 5.3.
  • Concluding Remarks
  • References
  • ch. 6
Control code
ocn746925664
Dimensions
unknown
Extent
1 online resource (xxii, 188 pages)
File format
unknown
Form of item
online
Isbn
9781439812594
Lccn
2010038461
Level of compression
unknown
Note
Taylor & Francis
Other physical details
illustrations (some color)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)746925664

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
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