Outlier detection is a data mining method for discovering exceptional, abnormal or suspiciously unusual samples in a data set. Outliers typically represent the data rich but information poor dilemma. Data mining methods are applied to solve this problem in broad range of application fields like credit card fraud detection, network intrusion detection, error extraction, clinical disease research...

This paper presents a model for keyword extraction, extending the basic concepts commonly used in this task, in order to get a formal background that allows determining the importance of the keywords to the documents. The proposed model combines an artificial immune system with a mathematical background based on information theory; this new model has the advantage that does not need any domain ...

Based on Multi-Agent (MA) technology and Artificial Immune (AI) theory, a novel substation fault diagnosis approach is proposed in the paper. The method firstly considers substation fault diagnosis a global complicated problem in uncertainty environment, and decomposes it into several relatively-simple local sub-problems based on ontology concept. Then diagnosis agent is respectively designed f...

Coordination and cooperation of artificial autonomous agents is activity for effective problems solution. Coalition formation is by one of cooperative mechanisms used in multi-agent systems. Various approaches of multi-agent coalition formation ensure coalition stability, optimal allocation of sources and payoff distribution for efficient achievement of individual or collective goals. The paper...

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natura...

In this thesis, the uses of Artificial Immune Systems (AIS) in Machine learning is studded. the thesis focus on some of immune inspired algorithms such as clonal selection algorithm and artificial immune network. The effect of changing the algorithm parameter on its performance is studded. Then a new immune inspired algorithm for unsupervised classification is proposed. The new algorithm is bas...

The hybridization of artificial immune system with cellular automata (CA-AIS) is a novel method. In this hybrid model, the cellular automaton within each cell deploys the artificial immune system algorithm under optimization context in order to increase its fitness by using its neighbor’s efforts. The hybrid model CA-AIS is introduced to fix the standard artificial immune system’s weaknesses. T...

The Natural Immune System (NIS) is a distributed system that solves challenging search and response problems while operating under constraints imposed by physical space and resource availability. Remarkably, NIS search and response times do not scale appreciably with the physical size of the animal in which its search is conducted. Many distributed systems are engineered to solve analogous prob...

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural net...