scholarly journals An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Maria Navarro-Caceres ◽  
Pramod Herath ◽  
Gabriel Villarrubia ◽  
Francisco Prieto-Castrillo ◽  
G. Kumar Venyagamoorthy
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Energy Optimization ◽  
Management Problem ◽  
Artificial Immune ◽  
Household Energy ◽  
Save Energy ◽  
Bioinspired Algorithms

Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.

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.

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.

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 Intrusion detection based on the artificial immune system

2020 ◽  
Vol 68 (4) ◽  
pp. 790-803
Author(s):  
Danijela Protić
Keyword(s):  
Immune System ◽  
Dendritic Cell ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
The Way

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.

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