scholarly journals Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ufuk Çelik ◽  
Nilüfer Yurtay ◽  
Emine Rabia Koç ◽  
Nermin Tepe ◽  
Halil Güllüoğlu ◽  
...  
Keyword(s):  
Immune System ◽  
Diagnostic Accuracy ◽  
Artificial Immune Systems ◽  
Evaluation Process ◽  
Classification Algorithms ◽  
Primary Headaches ◽  
Artificial Immune ◽  
Web Based ◽  
Accuracy Comparison ◽  
Artificial Immune Algorithms

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

THE MODEL AND ALGORITHM ARTIFICIAL IMMUNE SYSTEM

2016 ◽  
Vol 8 (3) ◽  
pp. 5-10
Author(s):  
Астахова ◽  
I. Astakhova ◽  
Ушаков ◽  
S. Ushakov
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Program Complex ◽  
Immune Systems ◽  
The Creation

In particular, models had only one type of cages , they applied V-lymphocytes. The distribution and a decentralization were the second feature for using artificial immune systems. This article is devoted to creation the artificial immune system (AIS), the creation model and algorithm of IIS is considered. The model for realization of a problem is consid-ered. Accuracy of calculations is compared to other methods, especially to neural networks. The structure of a program complex is described.

Hyperspectral Image Classification Based on Artificial Immune System

2012 ◽  
Vol 500 ◽  
pp. 806-812 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Immune System ◽  
Image Classification ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Hyperspectral Image Classification ◽  
Spectral Dimensionality ◽  
Hyperspectral Remote Sensing Images ◽  
Insight Into

The high spectral dimensionality in hyperspectral images causes the reduction of accuracy for common statistical classification methods in these images. Hence the generation and implementation of more complicated methods have gained great importance in this field. One of these methods is the Artificial Immune Systems which is inspired by natural immune system. Despite its great potentiality, it is rarely utilized for spatial sciences and image classification. In this paper a supervised classification algorithm with the application of hyperspectral remote sensing images is proposed. In order to gain better insight into its capability, its accuracy is compared with Artificial Neural Network. The results show better image classification accuracy for the Artificial Immune method.

How Do We Evaluate Artificial Immune Systems?

2005 ◽  
Vol 13 (2) ◽  
pp. 145-177 ◽  
Author(s):  
Simon M. Garrett
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Danger Theory

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness’ and ‘effectiveness.’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory.’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

scholarly journals ARTIFICIAL IMMUNE SYSTEMS APPROACH FOR MALWARE DETECTION: NEURAL NETWORKS APPLYING FOR IMMUNE DETECTORS CONSTRUCTION

2014 ◽  
pp. 44-50
Author(s):  
Sergei Bezobrazov ◽  
Vladimir Golovko
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Computer Security ◽  
Systems Approach ◽  
Artificial Immune Systems ◽  
Malicious Code ◽  
Artificial Immune ◽  
Biologically Inspired ◽  
Security Systems ◽  
Computer Viruses

This paper presents an approach for solving unknown computer viruses detection problem based on the Artificial Immune System (AIS) method, where immune detectors represented neural networks. The AIS is the biologically-inspired technique which have powerful information processing capabilities that makes it attractive for applying in computer security systems. Computer security systems based on AIS principles allow detect unknown malicious code. In this work we are describing model build on the AIS approach in which detectors represent the Learning Vector Quantization (LVQ) neural networks. Basic principles of the biological immune system (BIS) and comparative analysis of unknown computer viruses detection for different antivirus software and our model are presented.

Multiobjective Optimization and Artificial Immune Systems

2011 ◽  
pp. 1-21 ◽  
Author(s):  
Fabio Freschi ◽  
Carlos A. Coello Coello ◽  
Maurizio Repetto
Keyword(s):  
Immune System ◽  
Multiobjective Optimization ◽  
State Of The Art ◽  
Optimization Algorithms ◽  
Artificial Immune Systems ◽  
The State ◽  
Review Of The Literature ◽  
Artificial Immune ◽  
Immune Systems

This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be focused in the chapter, Artificial Immune Systems (AIS) have some intrinsic characteristics which make them well suited as multiobjective optimization algorithms. Following this basic idea, different implementations have been proposed in the literature. This chapter aims to provide a thorough review of the literature on multiobjective optimization algorithms based on the emulation of the immune system.

Intelligent Techniques in Teaching Science of Artificial Immune System

2011 ◽  
Vol 48-49 ◽  
pp. 637-640
Author(s):  
Tao Gong ◽  
Jia Jia Zhou ◽  
Lei Qi
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Virtual Classroom ◽  
Fuzzy Mathematics ◽  
Teaching Methodology ◽  
Artificial Immune ◽  
Web Based ◽  
Traditional Approaches ◽  
The Web ◽  
Mathematics And Science

Building on three theoretical paradigms (student model, ICAI model, and multi-dimension education immune agent), some intelligent techniques are proposed and designed to teach Fuzzy Mathematics and Science of Artificial Immune System in a web-based way. The goal of the teaching methodology is a new learning, which is interactive, sharing, open, cooperative, and autonomous. The great difference between traditional approaches for teaching such knowledge and the new approach in this paper is the centre of teaching. The traditional teaching is centered with teachers but the new teaching is centered with students. The teaching system for Fuzzy Mathematics and Science of Artificial Immune System is a virtual classroom based on the web, and the two courses are designed as web-based courses. Moreover, for Science of Artificial Immune System, the web-based course system is a typical artificial immune system in fact, and students can learn more real knowledge from the web-based course immune system.

Financial Classification Using an Artificial Immune System

2011 ◽  
pp. 1525-1540
Author(s):  
Anthony Brabazon ◽  
Alice Delahunty ◽  
Dennis O’Callaghan ◽  
Peter Keenan ◽  
Michael O’Neill
Keyword(s):  
Immune System ◽  
Classification System ◽  
Artificial Immune Systems ◽  
Dynamic Environment ◽  
Artificial Immune ◽  
Biologically Inspired ◽  
Immune Systems ◽  
Corporate Failure ◽  
Out Of Sample ◽  
Biologically Inspired Algorithms

Recent years have seen a dramatic increase in the application of biologically-inspired algorithms to business problems. Applications of neural networks and evolutionary algorithms have become common. However, as yet there have been few applications of artificial immune systems (AIS), algorithms that are inspired by the workings of the natural immune system. The natural immune system can be considered as a distributed, self-organizing, classification system that operates in a dynamic environment. The mechanisms of natural immune systems, including their ability to distinguish between self and non-self, provides a rich metaphorical inspiration for the design of pattern-recognition algorithms. This chapter introduces AIS and provides an example of how an immune algorithm can be used to develop a classification system for predicting corporate failure. The developed system displays good classification accuracy out-of-sample, up to two years prior to failure.

Sign in / Sign up

Export Citation Format

Share Document