An artificial immune system for data analysis

Timmis, Jon; Neal, Mark; Hunt, John

doi:10.1016/s0303-2647(99)00092-1

Cited by 340 publications

(168 citation statements)

References 9 publications

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“…Artificial Immune Systems (AIS) are another example of swarm-based nature inspired algorithms, which follow the processes and actions that happen in the immune systems of vertebrates. Clonal Selection Algorithms (de Castro & Von Zuben, 2002), Negative Selection Algorithms (Forrest, et al, 1994), and Immune Network Algorithms (Timmis et al, 2000) are among the most common techniques in the field of AIS. Another swarm-based optimization algorithm is the Particle Swarm Optimization (PSO), which has been introduced by (Kennedy & Eberhart, 1995).…”

Section: Related Workmentioning

confidence: 99%

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

Shah‐Hosseini¹

2009

Evolutionary Computation

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Section: Related Workmentioning

confidence: 99%

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

Shah‐Hosseini¹

2009

Evolutionary Computation

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“…The NIS is made up of lymphocytes which are white blood cells circulate throughout the body, mainly of two types, namely B-cells and T-cells. These cells play main role in the process of recognizing and destroying any antigens [19] . Fig.…”

Section: Artificial Immune Systems (Ais)mentioning

confidence: 99%

Swarm Negative Selection Algorithm for Electroencephalogram Signals Classification

Ba-Karait

Shamsuddin²,

Sudirman³

2009

J. of Computer Science

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Problem statement:The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. Approach: The proposed detection system of epileptic seizure in EEG signals is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. DWT was used to analyze EEG signals at different frequency bands and statistics over the set of the wavelet coefficients were calculated to introduce the feature vector for SNS classifier. The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. Thus, adapted negative selection is employed to create a set of self-tolerant ALCs. Whereas, PSO is used to evolve these ALCs away from self patterns towards non-self space and to maintain diversity and generality among the ALCs. Results: The experimental results proved that the proposed method reveals very promising performance in classifying EEG signals. A comparison with many previous studies showed that the presented algorithm has better results outperforming those reported by earlier methods. Conclusion: The technique was approved to be robust and effective in detecting and localizing epileptic seizure in EEG recording. Hence, the proposed system can be very helpful to make faster and accurate diagnosis decision.

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“…Artificial immune systems (AIS) are computational tools that emulate processes and mechanism of the biological immune system [77][78][79][80]. AIS use the learning, memory, and optimization capabilities of the immune system to develop computational algorithms for classification, function optimization, pattern recognition, novelty detection, and process control [81][82][83].…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ivanciuc

2008

CTMC

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Abstract:The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure-activity relationships that can discriminate between sets of chemicals that are active/inactive towards a certain biological receptor. An adverse effect of some cationic amphiphilic drugs is phospholipidosis that manifests as an intracellular accumulation of phospholipids and formation of concentric lamellar bodies. Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential. All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such as support vector machines and artificial immune systems. The best predictions are obtained with support vector machines, followed by a perceptron artificial neural network, logistic regression, and k-nearest neighbors.

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An artificial immune system for data analysis

Cited by 340 publications

References 9 publications

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

Swarm Negative Selection Algorithm for Electroencephalogram Signals Classification

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

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