Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune Systems

2009 ◽  
Vol 25 (25) ◽  
pp. 159-164 ◽  
Author(s):  
Mikhail Gorobetz ◽  
Ivars Alps ◽  
Anatoly Levchenkov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Artificial Immune Systems ◽  
Optimal Schedule ◽  
Electric Transport ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems

Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune SystemsThis paper describes mathematical formulation and application of artificial immune system for scheduling tasks for public electric transport. Artificial immune system is inspired by human immune system to simulate the process of interaction between antigens and antibodies. The task of scheduling in transport system is represented as one of the most well-known flow shop problem. Artificial immune system as a genetic based method is used to solve such task. Mathematical model and algorithm is proposed to create optimal schedule for public electric transport for minimization of electric energy consumption and time. Numerical example shows several steps of algorithm for artificial immune system for scheduling task solution.

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.

scholarly journals Application of the theory of danger for modeling market barriers based on artificial immune system

2015 ◽  
Vol 5 (1) ◽  
pp. 299-309
Author(s):  
Степанов ◽  
Leonid Stepanov ◽  
Сербулов ◽  
Yuriy Serbulov ◽  
Глухов ◽  
...  
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Protective Mechanism ◽  
Economic Systems ◽  
Artificial Immune ◽  
Human Immune System ◽  
Mathematical Methods ◽  
Foreign Agent ◽  
The Impact

Artificial immune system is a complex of mathematical methods to simulate the basic func-tions of the human immune system, and used to determine the parameters and (or) their values that can minimize the impact of certain factors (external or internal) to the production and economic ent-ity. The main characteristic that distinguishes the immune system of a foreign agent is an antigen that is any molecule which can be recognized by cellular elements of immunity (lymphocytes) using specific sensitive receptors. Otherwise, the antigen is a separate index that distinguishes foreign agent. Despite all this, there are examples where this approach fails. There are cases where the im-mune system does not work on "friend or foe", but uses a protective mechanism of hazard recogni-tion, which is a key method of the theory of danger. This theory does not deny the existence of dif-ferentiation in the "friend or foe", and argues that there are other factors that lead to the initiation of the immune response. For example, the theory of danger determines the nature of data on the beha-vior of competing industrial and economic systems, which must be submitted and processed in the artificial immune systems. Application of the theory of danger increases the efficiency of mathe-matical models, forming an artificial immune system of the market, which in its turn allows recog-nition of a new competitor in the market, assess the risk on its part for the competitors, and deter-mine the values of the characteristics of companies that will dominate over the parameters of a new competitor.

Intrusion Detection System using Artificial Immune Systems: A Case Study

2018 ◽  
Vol 8 (2) ◽  
pp. 19
Author(s):  
. Ojasvini ◽  
. Nitesh ◽  
. Piyush ◽  
Narina Thakur ◽  
Arvind Rehalia
Keyword(s):  
Immune System ◽  
Intrusion Detection ◽  
Large Scale ◽  
Artificial Immune System ◽  
Detection System ◽  
Dynamic Networks ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Malicious Nodes ◽  
Immune Systems

Networks are working at their apical efficiency and are increasing in size by every second; emergence of various threats becomes hindrance in the growth and privacy of the users. The network is vulnerable to security breaches, due to malicious nodes. Intrusion detection systems aim at removing this vulnerability. In this paper, intrusion detection mechanisms for large-scale dynamic networks are investigated. Artificial immune system is a concept that works to protect a network the way immune systems of vertebrates work in nature. This paper also illustrates this artificial immune system, the integration of bio-inspired algorithms, and its functionality with the computer networks.

scholarly journals METHOD OF SPAM DETECTION BASED ON ARTIFICIAL IMMUNE SYSTEMS

2018 ◽  
pp. 38-48
Author(s):  
Maria Petrovna Malykhina ◽  
Vera Arkadyevna Chastikova ◽  
Alexandr Aleksandrovich Biktimirov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Low Frequency ◽  
Spam Detection ◽  
Artificial Immune ◽  
New Approach ◽  
Immune Systems ◽  
Initial Stage ◽  
Living Organisms

The task of developing tools to combat spam is currently focused on creating such techniques for detecting spam, which are endowed with the skills and qualities inherent in a person whose work is not limited to patterns and therefore highly effective. Man has the ability to detect spam signs, which is based on his own knowledge, experience and preferences. There has been substantiated the need to develop a new approach to solving the problem of detecting spam messages, which is based on heuristic methods of optimization, is effective at the initial stage of training and has a low frequency of false operations. This formulation of the problem fully corresponds to modeling mechanisms of the immune systems of living organisms that ensure their survival, these mechanisms being represented, investigated and used by software. There have been identified and described main mechanisms of artificial immune systems intended for solving the problem of spam detection, as well as software and system interacting. The basic concepts of constructing an artificial immune system for the purpose formulated above are determined: class of detectors, presentation of receptors and pathogens. A model of the relationships between them has been worked out. A technique for detecting spam based on the work of an artificial immune system is proposed, an algorithm for its implementation is developed, and the specifics of its members to identify spam messages are described. A software package with advanced research capabilities has been created. Testing and analysis of the results to determine the optimum values of the system operation parameters have been conducted.

THE USAGE OF DISTRIBUTED ARTIFICIAL IMMUNE SYSTEMS FOR SOLVING PROBLEMS OF IDENTIFICATION IN ECOLOGY

2016 ◽  
Vol 8 (3) ◽  
pp. 10-14
Author(s):  
Ушаков ◽  
S. Ushakov ◽  
Астахова ◽  
I. Astakhova ◽  
Хицкова ◽  
...  
Keyword(s):  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Identification Problem ◽  
Distributed Network ◽  
Heterogeneous Environment ◽  
Artificial Immune ◽  
Original Function ◽  
Power Sources ◽  
Immune Systems ◽  
Cross Platform

There is a problem of ecological forecasting identification which is finding power sources for the available experimental data. This task is an inverse problem, the solution of which will be considered by applying the method of symbolic regression. The artificial immune system (AIS) – a model that allows you to solve various problems of recognition, its concept was borrowed from biology. This distributed network can operate in any heterogeneous environment, which is achieved through the use of cross-platform programming language Python. AIS demonstrates the ability to restore the original function in the identification problem, a plot of the solution for test data is presented.

scholarly journals Multithreshold Segmentation Based on Artificial Immune Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Valentin Osuna-Enciso ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Humberto Sossa
Keyword(s):  
Initial Conditions ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Gaussian Function ◽  
Gaussian Mixture ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems ◽  
Gradient Based ◽  
Low Sensitivity

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

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