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Journal articles on the topic 'Artificial Immune Systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Skobtsov, Y. A. "ARTIFICIAL IMMUNE SYSTEMS - BASIC MODELS." Mathematical Methods in Technologies and Technics, no. 2 (2021): 103–6. http://dx.doi.org/10.52348/2712-8873_mmtt_2021_2_103.

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2

Dasgupta, Dipankar. "Advances in artificial immune systems." IEEE Computational Intelligence Magazine 1, no. 4 (2006): 40–49. http://dx.doi.org/10.1109/mci.2006.329705.

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3

Dasgupta, Dipankar. "Advances in artificial immune systems." IEEE Computational Intelligence Magazine 1, no. 4 (2006): 40–49. http://dx.doi.org/10.1109/ci-m.2006.248056.

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4

MALIM, MUHAMMAD ROZI, and FARIDAH ABDUL HALIM. "IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 06 (2012): 1250031. http://dx.doi.org/10.1142/s0218213012500315.

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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 t

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5

Hart, Emma, Chris McEwan, Jon Timmis, and Andrew Hone. "Advances in artificial immune systems." Evolutionary Intelligence 4, no. 2 (2011): 67–68. http://dx.doi.org/10.1007/s12065-011-0058-z.

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6

Timmis, J., A. Hone, T. Stibor, and E. Clark. "Theoretical advances in artificial immune systems." Theoretical Computer Science 403, no. 1 (2008): 11–32. http://dx.doi.org/10.1016/j.tcs.2008.02.011.

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7

Timmis, Jon, Paul Andrews, and Emma Hart. "Special issue on artificial immune systems." Swarm Intelligence 4, no. 4 (2010): 245–46. http://dx.doi.org/10.1007/s11721-010-0047-3.

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8

Bentley, Peter J., and Doheon Lee. "Special Issue on Artificial Immune Systems." Journal of Mathematical Modelling and Algorithms 8, no. 2 (2009): 101–2. http://dx.doi.org/10.1007/s10852-009-9106-4.

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9

Aickelin, Uwe. "Special issue on artificial immune systems." Evolutionary Intelligence 1, no. 2 (2008): 83–84. http://dx.doi.org/10.1007/s12065-008-0007-7.

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10

Timmis, Jon. "Artificial immune systems—today and tomorrow." Natural Computing 6, no. 1 (2006): 1–18. http://dx.doi.org/10.1007/s11047-006-9029-1.

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11

Zuccolotto, Marcos, Carlos Eduardo Pereira, Luca Fasanotti, Sergio Cavalieri, and Jay Lee. "Designing an Artificial Immune Systems for Intelligent Maintenance Systems." IFAC-PapersOnLine 48, no. 3 (2015): 1451–56. http://dx.doi.org/10.1016/j.ifacol.2015.06.291.

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12

Burlakov, M. E. "TWO-CLASSIFICATION ARTIFICIAL IMMUNE SYSTEM." Vestnik of Samara University. Natural Science Series 20, no. 7 (2017): 207–20. http://dx.doi.org/10.18287/2541-7525-2014-20-7-207-220.

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In the article the practical aspect of application of principles of biological immune system for solving the problem of analysis and classiﬁcation of email is viewed. In the capacity of analyzed emails ordinary emails (electronic mail) and mails from closed systems (electronic document ﬂow or business management systems) were taken. In the article two-classiﬁcation artiﬁcial immune system was developed with further comparison of eﬀectiveness of their usage with naive Bayesian classiﬁcation algorithm. Practical realization of the developed system with the application in the system of analysis o

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13

Cuevas, Erik, Valentin Osuna-Enciso, Daniel Zaldivar, Marco Pérez-Cisneros, and Humberto Sossa. "Multithreshold Segmentation Based on Artificial Immune Systems." Mathematical Problems in Engineering 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/874761.

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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

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14

Garrett, Simon M. "How Do We Evaluate Artificial Immune Systems?" Evolutionary Computation 13, no. 2 (2005): 145–77. http://dx.doi.org/10.1162/1063656054088512.

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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 bei

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15

Gao, Xiao-Zhi, Mo-Yuen Chow, David Pelta, and Jon Timmis. "Theory and applications of artificial immune systems." Neural Computing and Applications 19, no. 8 (2010): 1101–2. http://dx.doi.org/10.1007/s00521-010-0388-2.

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16

Overill, R. E. "Review: Artificial Immune Systems and their Applications." Journal of Logic and Computation 11, no. 6 (2001): 961–62. http://dx.doi.org/10.1093/logcom/11.6.961-a.

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17

Timmis, Jon, Paul Andrews, and Emma Hart. "On artificial immune systems and swarm intelligence." Swarm Intelligence 4, no. 4 (2010): 247–73. http://dx.doi.org/10.1007/s11721-010-0045-5.

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18

Timmis, J., P. Andrews, N. Owens, and E. Clark. "An interdisciplinary perspective on artificial immune systems." Evolutionary Intelligence 1, no. 1 (2008): 5–26. http://dx.doi.org/10.1007/s12065-007-0004-2.

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19

Wu, Ying, and Colin Fyfe. "Exploratory data analysis with artificial immune systems." Evolutionary Intelligence 1, no. 2 (2008): 159–69. http://dx.doi.org/10.1007/s12065-008-0012-x.

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20

Yang, Jin, Tao Li, Sunjun Liu, Tiefang Wang, Diangang Wang, and Gang Liang. "Computer Forensics System Based on Artificial Immune Systems." JUCS - Journal of Universal Computer Science 13, no. (9) (2007): 1354–65. https://doi.org/10.3217/jucs-013-09-1354.

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The current computer forensics approaches mainly focus on the network actions capture and analysis the evidences after attacks, which always result in the static methods. Inspired by the theory of artificial immune systems (AIS ), a novel model of Computer Forensics System is presented. The concepts and formal definitions of immune cells are given, and dynamically evaluative equations for self, antigen, immune tolerance, mature-lymphocyte lifecycle and immune memory are presented, and the hierarchical and distributed management framework of the proposed model are built. Furthermore, the idea o

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21

Tassi Souza Silva, Frederico, Leandro Ramos Araujo, and Debora Rosana Ribeiro Penido. "Optimal Substation Placement in Distribution Systems using Artificial Immune Systems." IEEE Latin America Transactions 16, no. 2 (2018): 505–13. http://dx.doi.org/10.1109/tla.2018.8327406.

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22

Hofmeyr, Steven A., and Stephanie Forrest. "Architecture for an Artificial Immune System." Evolutionary Computation 8, no. 4 (2000): 443–73. http://dx.doi.org/10.1162/106365600568257.

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An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities a

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23

Gorobetz, Mikhail, Ivars Alps, and Anatoly Levchenkov. "Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune Systems." Scientific Journal of Riga Technical University. Power and Electrical Engineering 25, no. 25 (2009): 159–64. http://dx.doi.org/10.2478/v10144-009-0034-y.

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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 propo

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24

Mikherskii, R. M., and M. R. Mikherskii. "Analysis of the Use of Artificial Immune Systems." IOP Conference Series: Materials Science and Engineering 1069, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1757-899x/1069/1/012025.

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25

Carneiro, Sávio Mota, Thiago A. R. da Silva, Ricardo de A. L. Rabêlo, Francisca R. V. Silveira, and Gustavo A. L. de Campos. "Using artificial immune systems for intelligent agent testing." International Journal of Hybrid Intelligent Systems 12, no. 2 (2015): 65–76. http://dx.doi.org/10.3233/his-150206.

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26

Vagis, Alexandra A. "Prospects of Information Processing in Artificial Immune Systems." Journal of Automation and Information Sciences 41, no. 7 (2009): 64–70. http://dx.doi.org/10.1615/jautomatinfscien.v41.i7.40.

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27

El Dosuky, M. A. "Obesity Heuristic, New Way on Artificial Immune Systems." Advanced Computing: An International Journal 3, no. 6 (2012): 1–7. http://dx.doi.org/10.5121/acij.2012.3601.

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28

Viswalingam, Dr Kathir, and G. Ayy appan. "A Study and Survey of Artificial Immune Systems." International Journal of Innovative Research in Computer and Communication Engineering 02, no. 12 (2014): 7197–201. http://dx.doi.org/10.15680/ijircce.2014.0212010.

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29

Kidd. "Artificial Immune Systems: An Overview for Faulting Actuators." Actuators 8, no. 3 (2019): 53. http://dx.doi.org/10.3390/act8030053.

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This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based com

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30

Dasgupta, D. "Guest editorial special issue on artificial immune systems." IEEE Transactions on Evolutionary Computation 6, no. 3 (2002): 225–26. http://dx.doi.org/10.1109/tevc.2002.1011537.

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31

Albergante, Luca. "Automatic management of forums via artificial immune systems." International Journal of Autonomous and Adaptive Communications Systems 4, no. 2 (2011): 145. http://dx.doi.org/10.1504/ijaacs.2011.039721.

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32

Нуриахметов, А. И., and Д. Р. Богданова. "Emotion recognition in images and artificial immune systems." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 9, no. 3(34) (2021): 10–11. http://dx.doi.org/10.26102/2310-6018/2021.34.3.010.

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В статье приведены результаты применения одного из методов искусственных иммунных систем к задаче распознавания эмоций человека по его мимике на изображениях. Искусственные иммунные системы – специальная концепция, в основе которой лежат различные принципы естественной иммунной системы млекопитающих. За счет своего разнообразия, подобным системам удалось достигнуть высоких результатов во множестве различных задач. Поэтому весьма интересным является вопрос об их эффективности в такой задаче, как распознавание эмоций. Так, в данном исследовании, используя один из методов искусственных иммунных с

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33

Azuaje, F. "Artificial Immune Systems: A New Computational Intelligence Approach." Neural Networks 16, no. 8 (2003): 1229. http://dx.doi.org/10.1016/s0893-6080(03)00058-3.

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34

Sieburg, Hans B., J. Allen McCutchan, Oliver K. Clay, Lisa Cabalerro, and James J. Ostlund. "Simulation of HIV infection in artificial immune systems." Physica D: Nonlinear Phenomena 45, no. 1-3 (1990): 208–27. http://dx.doi.org/10.1016/0167-2789(90)90184-q.

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35

Burczynski, Tadeusz. "Information Sciences Special Issue on Artificial Immune Systems." Information Sciences 179, no. 10 (2009): 1377–78. http://dx.doi.org/10.1016/j.ins.2008.12.013.

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36

Chang, Chien-Cheng, Hwai-En Tseng, and Ling-Peng Meng. "Artificial immune systems for assembly sequence planning exploration." Engineering Applications of Artificial Intelligence 22, no. 8 (2009): 1218–32. http://dx.doi.org/10.1016/j.engappai.2009.04.007.

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37

Arslan, Sema, and Hakan Işik. "The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography." Journal of Advances in Computer Networks 2, no. 1 (2014): 31–34. http://dx.doi.org/10.7763/jacn.2014.v2.77.

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38

Cooper, Edwin L. "Evolution of immune systems from self/not self to danger to artificial immune systems (AIS)." Physics of Life Reviews 7, no. 1 (2010): 55–78. http://dx.doi.org/10.1016/j.plrev.2009.12.001.

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39

Karakose, Mehmet. "Reinforcement Learning Based Artificial Immune Classifier." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/581846.

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One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, les

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40

Mikherskii, R. M. "APPLICATION OF AN ARTIFICIAL IMMUNE SYSTEM FOR VISUAL PATTERN RECOGNITION." Computer Optics 42, no. 1 (2018): 113–17. http://dx.doi.org/10.18287/2412-6179-2018-42-1-113-117.

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The suitability of artificial immune systems for recognizing visual patterns is discussed. A new algorithm and software implementation of an artificial immune system have been proposed based on which real-time pattern recognition can be done using a Web camera. It has been shown experimentally that this system can be successfully used to recognize both human faces and any other objects. An issue of using an artificial immune system in high-performance parallel computing systems is discussed. The advantages of the developed artificial immune system include the ability to teach the system a new

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41

Ren, Chang Ning, Zhi Hao Peng, and Dong Xia Zheng. "An Artificial Immune System Based on Intelligent Agents." Advanced Materials Research 886 (January 2014): 673–76. http://dx.doi.org/10.4028/www.scientific.net/amr.886.673.

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The use of immune systems in intrusion detection has been a hot spot of research for the past decade. Immune systems have many features that are desirable for the imperfect, uncontrolled, and open environments in which most computers currently exist. It is hoped that biologically inspired approaches in this area, including the use of intelligent agents will be able to meet this challenge. In this paper, we extract some useful mechanisms from natural biological systems together with the use of intelligent agents, in order to develop cost effective computational solutions to complex problems in

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42

Gonsalves, Tad, and Kiyoshi Itoh. "MINIMIZING OPERATIONAL COST USING ARTIFICIAL IMMUNE ALGORITHM." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 13, no. 4 (2009): 9–18. http://dx.doi.org/10.3233/jid-2009-13402.

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This paper deals with the minimization of the operational costs of business systems using the Artificial Immune System (AIS). The objective function consists of service costs and waiting costs. Service cost is the cost of hiring service-providing professionals, while waiting cost is the estimate of the loss to business as some customers, not willing to wait for service, decide to go to the competing organizations. Since business systems are inherently concurrent with multiple asynchronous activities, we have used Generalized Stochastic Petri Net as the modelling tool. The Petri net-modeled sys

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43

GASIR, FATHI, KEELEY CROCKETT, and ZUHAIR BANDAR. "INDUCING FUZZY REGRESSION TREE FORESTS USING ARTIFICIAL IMMUNE SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, supp02 (2012): 133–57. http://dx.doi.org/10.1142/s0218488512400181.

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Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different

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44

Gong, Maoguo, Haifeng Du, and Licheng Jiao. "Optimal approximation of linear systems by artificial immune response." Science in China Series F 49, no. 1 (2006): 63–79. http://dx.doi.org/10.1007/s11432-005-0314-x.

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45

Castro, L. N. de, and J. I. Timmis. "Artificial immune systems as a novel soft computing paradigm." Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, no. 8 (2003): 526–44. http://dx.doi.org/10.1007/s00500-002-0237-z.

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46

Gao, Xiao-Zhi, Mo-Yuen Chow, David Pelta, and Jon Timmis. "Special Issue on Artificial Immune Systems: Theory and Applications." Neural Computing and Applications 19, no. 4 (2007): 647. http://dx.doi.org/10.1007/s00521-007-0136-4.

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47

Dasgupta, Dipankar, Senhua Yu, and Fernando Nino. "Recent Advances in Artificial Immune Systems: Models and Applications." Applied Soft Computing 11, no. 2 (2011): 1574–87. http://dx.doi.org/10.1016/j.asoc.2010.08.024.

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48

Schaust, Sven, and Helena Szczerbicka. "ARTIFICIAL IMMUNE SYSTEMS IN THE CONTEXT OF MISBEHAVIOR DETECTION." Cybernetics and Systems 39, no. 2 (2008): 136–54. http://dx.doi.org/10.1080/01969720701853434.

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49

Cuevas, Erik, Valentín Osuna-Enciso, Fernando Wario, Daniel Zaldívar, and Marco Pérez-Cisneros. "Automatic multiple circle detection based on artificial immune systems." Expert Systems with Applications 39, no. 1 (2012): 713–22. http://dx.doi.org/10.1016/j.eswa.2011.07.063.

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50

Protić, Danijela. "Intrusion detection based on the artificial immune system." Vojnotehnicki glasnik 68, no. 4 (2020): 790–803. http://dx.doi.org/10.5937/vojtehg68-27954.

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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

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