Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
The Deterministic Dendritic Cell Algorithm
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Information fusion for anomaly detection with the dendritic cell algorithm
Information Fusion
The application of a dendritic cell algorithm to a robotic classifier
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Articulation and clarification of the dendritic cell algorithm
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Application areas of AIS: the past, the present and the future
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
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Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. Despite the successful implementation of different AIS, the validity of the paradigm "self non self" have lifted many questions. The Danger theory was an alternative to this paradigm. If we involve its principles, the AIS are being applied as a classifier. However, image classification offers new prospects and challenges to data mining and knowledge extraction. It is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this paper, we describe our initial framework in which the danger theory was apprehended by the Dendritic cells algorithm is applied to vegetal image classification. The approach classifies pixel in vegetal or soil class. Experimental results are very encouraging and show the feasibility and effectiveness of the proposed approach.