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Artificial Immune Systems

Overview of attention for book
Cover of 'Artificial Immune Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 A Gradient-Based Artificial Immune System Applied to Optimal Power Flow Problems
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    Chapter 2 Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems
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    Chapter 3 NAIS: A Calibrated Immune Inspired Algorithm to Solve Binary Constraint Satisfaction Problems
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    Chapter 4 A Solution Concept for Artificial Immune Networks: A Coevolutionary Perspective
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    Chapter 5 Artificial Immune Systems for Classification of Petroleum Well Drilling Operations
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    Chapter 6 The SUPRAIC Algorithm: A Suppression Immune Based Mechanism to Find a Representative Training Set in Data Classification Tasks
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    Chapter 7 The Influence of Diversity in an Immune–Based Algorithm to Train MLP Networks
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    Chapter 8 Applying Biclustering to Text Mining: An Immune-Inspired Approach
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    Chapter 9 Defence Against 802.11 DoS Attacks Using Artificial Immune System
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    Chapter 10 A Novel Immune Inspired Approach to Fault Detection
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    Chapter 11 Towards a Novel Immune Inspired Approach to Temporal Anomaly Detection
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    Chapter 12 Bankruptcy Prediction Using Artificial Immune Systems
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    Chapter 13 Phase Transition and the Computational Complexity of Generating r-Contiguous Detectors
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    Chapter 14 Real-Valued Negative Selection Algorithm with a Quasi-Monte Carlo Genetic Detector Generation
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    Chapter 15 A Novel Fast Negative Selection Algorithm Enhanced by State Graphs
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    Chapter 16 Clonal Selection Algorithms for 6-DOF PID Control of Autonomous Underwater Vehicles
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    Chapter 17 An Immuno Robotic System for Humanitarian Search and Rescue (Application Stream)
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    Chapter 18 The Application of a Dendritic Cell Algorithm to a Robotic Classifier
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    Chapter 19 On Immune Inspired Homeostasis for Electronic Systems
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    Chapter 20 Modeling Migration, Compartmentalization and Exit of Naive T Cells in Lymph Nodes Without Chemotaxis
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    Chapter 21 Revisiting the Central and Peripheral Immune System
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    Chapter 22 Topological Constraints in the Evolution of Idiotypic Networks
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    Chapter 23 A Computational Model for the Cognitive Immune System Theory Based on Learning Classifier Systems
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    Chapter 24 Motif Detection Inspired by Immune Memory
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    Chapter 25 An Immune-Inspired Approach to Speckled Computing
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    Chapter 26 Biological Inspiration for Artificial Immune Systems
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    Chapter 27 Regulatory T Cells: Inspiration for Artificial Immune Systems
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    Chapter 28 Automated Blog Design System with a Population-Based Artificial Immune Algorithm
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    Chapter 29 Immune and Evolutionary Approaches to Software Mutation Testing
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    Chapter 30 An Artificial Immune System Based Approach for English Grammar Checking
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    Chapter 31 A Novel Clonal Selection Algorithm Based Fragile Watermarking Method
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    Chapter 32 BeeAIS: Artificial Immune System Security for Nature Inspired, MANET Routing Protocol, BeeAdHoc
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    Chapter 33 A Cultural Immune System for Economic Load Dispatch with Non-smooth Cost Functions
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    Chapter 34 Artificial Immune System to Find a Set of k-Spanning Trees with Low Costs and Distinct Topologies
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    Chapter 35 How to Obtain Appropriate Executive Decisions Using Artificial Immunologic Systems
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    Chapter 36 An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions
Overall attention for this book and its chapters
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Mentioned by

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1 X user

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8 Mendeley
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Title
Artificial Immune Systems
Published by
Lecture notes in computer science, January 2007
DOI 10.1007/978-3-540-73922-7
ISBNs
978-3-54-073921-0, 978-3-54-073922-7
Editors

Leandro Nunes de Castro, Fernando José Von Zuben, Helder Knidel

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Professor > Associate Professor 2 25%
Student > Bachelor 1 13%
Student > Doctoral Student 1 13%
Lecturer 1 13%
Other 0 0%
Readers by discipline Count As %
Computer Science 7 88%
Engineering 1 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 July 2017.
All research outputs
#20,420,242
of 22,971,207 outputs
Outputs from Lecture notes in computer science
#7,009
of 8,141 outputs
Outputs of similar age
#153,176
of 157,544 outputs
Outputs of similar age from Lecture notes in computer science
#54
of 59 outputs
Altmetric has tracked 22,971,207 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,141 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 157,544 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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