An Overview of Application of Artificial Immune System in Swarm Robotic Systems
Issue:
Volume 3, Issue 2, April 2015
Pages:
11-18
Received:
23 January 2015
Accepted:
27 February 2015
Published:
12 March 2015
Abstract: The Artificial Immune System (AIS) is a biologically inspired computation system based on vertebrate immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimization and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This paper after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.
Abstract: The Artificial Immune System (AIS) is a biologically inspired computation system based on vertebrate immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimization and anomaly detection. Many investigations have been conducted to understand...
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Lung Region Segmentation using Artificial Neural Network Hopfield Model for Cancer Diagnosis in Thorax CT Images
Rachid Sammouda,
Hassan Ben Mathkour
Issue:
Volume 3, Issue 2, April 2015
Pages:
19-25
Received:
14 February 2015
Accepted:
9 March 2015
Published:
21 March 2015
Abstract: Lung Cancer was found to be one of the leading causes of death of human persons throughout the world. It spreads rapidly after it forms. The survival rate of patient is very low as the disease is identified in a very late stage. In this paper, we represent a fully automated and three-dimensional segmentation method for the early identification of cancerous pixels in thorax Computed Tomography database. The segmentation process is meant to be considered as the bottleneck in the Computer Aided Diagnosis system for lung cancer detection based on the Computed Tomography pixels’ values. We have formulated the segmentation problem as the optimization of a certain energy function. A special Classifier was designed using Hopfield Artificial Neural Network in order to classify or segment the set of pixels in the CT images of the Thorax into a set of user decided number of regions. A step function was designed, implemented and tested to ensure a high convergence speed of the classifier to local optimum that is close to the global optima. The lung contour was adequately located in 95% of the CT scans using a pre-segmentation process based on bit-planes’ features of the CT scans. The segmentation process was initially developed and tested on a large dataset of subjects, with normal and abnormal lung tissues at different stages, each of 150 CT scans giving very satisfactory results.
Abstract: Lung Cancer was found to be one of the leading causes of death of human persons throughout the world. It spreads rapidly after it forms. The survival rate of patient is very low as the disease is identified in a very late stage. In this paper, we represent a fully automated and three-dimensional segmentation method for the early identification of c...
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Analysis and Research on Combination Feature Extraction Method of EEG Singnal
LI Jun-wei,
Jason Gu,
XIE Yun
Issue:
Volume 3, Issue 2, April 2015
Pages:
26-30
Received:
29 March 2015
Accepted:
11 April 2015
Published:
21 April 2015
Abstract: EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.
Abstract: EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single m...
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