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.

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.

IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 21 (06) ◽  
pp. 1250031 ◽  
Author(s):  
MUHAMMAD ROZI MALIM ◽  
FARIDAH ABDUL HALIM
Keyword(s):  
Immune System ◽  
Network Theory ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Selection Mechanism ◽  
Artificial Immune ◽  
Time Line ◽  
Immune Systems

Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.

scholarly journals An artificial immune system algorithm for solving the uncapacitated single allocation p-Hub median problem

2021 ◽  
Vol 11 (3) ◽  
pp. 2293
Author(s):  
Fatima Zahraa Grine ◽  
Oulaid Kamach ◽  
Abdelhakim Khatab ◽  
Naoufal Sefiani
Keyword(s):  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Data Sets ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Hub Location ◽  
Median Problem ◽  
Solution Quality ◽  
Artificial Immune System Algorithm ◽  
Single Allocation

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

2014 ◽  
Vol 472 ◽  
pp. 544-549 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette ◽  
Simone Silva Frutuoso de Souza ◽  
Adriano dos Santos e Souza ◽  
Mara Lúcia Martins Lopes
Keyword(s):  
Negative Selection ◽  
Structural Integrity ◽  
Artificial Immune Systems ◽  
Structural Failure ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Negative Selection Algorithm ◽  
Identification And Characterization

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

scholarly journals An Immune Based Patient Anomaly Detection using RFID Technology

2013 ◽  
Vol 2 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Sri Listia Rosa ◽  
Siti Mariyam Shamsuddin ◽  
Evizal Evizal
Keyword(s):  
Immune System ◽  
Monitoring System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Intelligent System ◽  
Patient Data ◽  
Rfid Technology ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI: 10.18495/comengapp.21.121142

scholarly journals DETECÇÃO DE FALHAS ESTRUTURAIS DE UM PRÉDIO DE DOIS ANDARES UTILIZANDO SISTEMA IMUNOLÓGICO ARTIFICIA

2019 ◽  
Vol 11 (4) ◽  
pp. 73-84
Author(s):  
Thiago Carreta Moro ◽  
Fábio Roberto Chavarette ◽  
Luiz Gustavo Pereira Roéfero ◽  
Roberto Outa
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Structural Integrity ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Intelligent Computing ◽  
Negative Selection Algorithm ◽  
Story Building

This work presents the innovative proposal for the development of SHMs with focus on physical cyber systems applied in a two-story building based on Intelligent Computing (CI) techniques, the negative selection algorithm from the Artificial Immune System, to perform the analysis and monitoring of structural integrity in a building.

scholarly journals Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6351
Author(s):  
Łukasz Rokicki
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Evolutionary Algorithm ◽  
Distribution System ◽  
Artificial Immune System ◽  
Low Voltage ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Clonal Selection Algorithm ◽  
Artificial Immune

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

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