Relevant bibliographies by topics / Artificial Immune Systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial Immune Systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Artificial Immune Systems"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Artificial Immune Systems"

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AMARAL, JORGE LUIS M. DO. "ARTIFICIAL IMMUNE SYSTEMS APPLIED TO FAULT DETECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8236@1.

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UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO<br>Este trabalho investiga métodos de detecção de falhas baseados em sistemas imunológicos artificiais, especificamente aqueles baseados no algoritmo de seleção negativa (NSA) e em outras técnicas de reconhecimento próprio/nãopróprio. Inicialmente, foi proposto um esquema de representação baseado em hiperesferas com centros e raios variáveis e três modelos capazes de gerar detectores, com esta representação, de forma eficiente. O primeiro modelo utiliza algoritmos genéticos onde cada gene do cromossomo contém um índice para um ponto de uma distr
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Maree, Charl. "Diagnostic monitoring of dynamic systems using artificial immune systems." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/1780.

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Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006.<br>The natural immune system is an exceptional pattern recognition system based on memory and learning that is capable of detecting both known and unknown pathogens. Artificial immune systems (AIS) employ some of the functionalities of the natural immune system in detecting change in dynamic process systems. The emerging field of artificial immune systems has enormous potential in the application of fault detection systems in process engineering. This thesis aims to firstly familiarise the reader with the various cu
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Shi, Arthur Q. X. "Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1499.

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Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immu
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Lay, Nicholas Christopher. "Enhancing real-time embedded systems development using artificial immune systems." Thesis, University of York, 2009. http://etheses.whiterose.ac.uk/1419/.

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The Consumer Electronics industry produces a large number of systems which exhibit both real-time and embedded properties (RTES). Frequently problems are encountered during the development of these systems due to a mismatch between the requirements of traditional real-time development techniques and the restrictions imposed on the development process by the retail market. There is therefore a requirement for a method which can be used to enhance the development process and improve the reliability of these systems, yet which does not require the intensive formal analysis often required by estab
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Zarges, Christine [Verfasser]. "Theoretical foundations of artificial immune systems / Christine Zarges." Dortmund : Universitätsbibliothek Technische Universität Dortmund, 2011. http://d-nb.info/1018083537/34.

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Qiu, Xueni, and 邱雪妮. "Artificial immune systems for job shop scheduling problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49617576.

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Effective process scheduling is very important to the modern manufacturing production. This research addresses a classical scheduling problem — the job shop scheduling problem from the standpoint of both static and dynamic environment. In this study, the job shop scheduling problem (JSSP) is investigated in three aspects: (1) static JSSP that operates under a static scheduling environment with known information about the jobs and machines without unexpected events; (2) semi-dynamic JSSP which is developed based on static JSSP but violating the non-operation disruption assumption due to the pr
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Bondal, Akshata A. "Artificial Immune Systems Applied to Job Shop Scheduling." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1204990296.

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Davoudani, Despina. "Specknets : a case study for artificial immune systems." Thesis, Edinburgh Napier University, 2012. http://researchrepository.napier.ac.uk/Output/5220.

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Gasir, Fathi Sidig. "Optimization of fuzzy regression trees using artificial immune systems." Thesis, Manchester Metropolitan University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574508.

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This thesis presents the development of a novel fuzzy regression tree algorithm known as Elgasir, which is based on the CHAID regression tree algorithm and Takagi-Sugeno fuzzy inference. The Elgasir Algorithm is applied to crisp regression trees to produce fuzzy regression trees in order to soften sharp decision boundaries inherited in crisp trees. Elgasir generates a fuzzy rule base by applying fuzzy techniques to crisp regression trees using Trapezoidal membership functions. Then Takagi-Sugeno fuzzy inference is used to aggregate the final output from the fuzzy implications. A novel optimiza
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Al-Enezi, Jamal. "Artificial immune systems based committee machine for classification application." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6826.

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A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse comple
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Books on the topic "Artificial Immune Systems"

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Hart, Emma, Chris McEwan, Jon Timmis, and Andy Hone, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14547-6.

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Andrews, Paul S., Jon Timmis, Nick D. L. Owens, et al., eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03246-2.

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Jacob, Christian, Marcin L. Pilat, Peter J. Bentley, and Jonathan I. Timmis, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536444.

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Liò, Pietro, Giuseppe Nicosia, and Thomas Stibor, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22371-6.

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de Castro, Leandro Nunes, Fernando José Von Zuben, and Helder Knidel, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73922-7.

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Coello Coello, Carlos A., Julie Greensmith, Natalio Krasnogor, Pietro Liò, Giuseppe Nicosia, and Mario Pavone, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33757-4.

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Bentley, Peter J., Doheon Lee, and Sungwon Jung, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85072-4.

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Bersini, Hugues, and Jorge Carneiro, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823940.

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Nicosia, Giuseppe, Vincenzo Cutello, Peter J. Bentley, and Jon Timmis, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b100486.

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Timmis, Jon, Peter J. Bentley, and Emma Hart, eds. Artificial Immune Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b12020.

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Book chapters on the topic "Artificial Immune Systems"

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Du, Ke-Lin, and M. N. S. Swamy. "Artificial Immune Systems." In Search and Optimization by Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_10.

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Aickelin, Uwe, Dipankar Dasgupta, and Feng Gu. "Artificial Immune Systems." In Search Methodologies. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_7.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Artificial Immune Systems." In Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_16.

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Greensmith, Julie, Amanda Whitbrook, and Uwe Aickelin. "Artificial Immune Systems." In Handbook of Metaheuristics. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1665-5_14.

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Sotiropoulos, Dionisios N., and George A. Tsihrintzis. "Artificial Immune Systems." In Machine Learning Paradigms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47194-5_7.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Artificial Immune Systems." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_33.

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Barbakh, Wesam Ashour, Ying Wu, and Colin Fyfe. "Artificial Immune Systems." In Non-Standard Parameter Adaptation for Exploratory Data Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04005-4_10.

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Timmis, Jon. "Artificial Immune Systems." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_919.

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Dasgupta, Dipankar. "Artificial Immune Systems." In Encyclopedia of Sciences and Religions. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_1208.

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Corus, Dogan, Pietro S. Oliveto, and Donya Yazdani. "Fast Artificial Immune Systems." In Parallel Problem Solving from Nature – PPSN XV. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99259-4_6.

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Conference papers on the topic "Artificial Immune Systems"

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Liberato, Luiz, Álvaro Nogueira da Cruz, Anderson Amorim, et al. "Pattern Recognition in Biosequences Using Artificial Immune System." In 27th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013293500003929.

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Raza, Ali, and Benito R. Fernandez. "Artificial Immune System for Heterogeneous Mobile Robotic Systems." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4264.

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Artificial immune system draws its inspiration from the biological immune functions mainly those of humans. Recently, newer definitions of biological immune system have appeared and gained significance because of their strong immunological roots e.g. danger theory. This raises the need to look into earlier work on immuno-inspired robotics. Especially, older approach of idiotypic-network must be compared with the newer approach of danger-theory. Authors in this research have successfully applied both the definitions on heterogeneous mobile robotic systems. Idiotypic connections between antibodi
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Mohamed Elsayed, Samir A., Reda A. Ammar, and Sanguthevar Rajasekaran. "Artificial Immune Systems." In the 27th Annual ACM Symposium. ACM Press, 2012. http://dx.doi.org/10.1145/2245276.2245326.

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Jansen, Thomas, and Christine Zarges. "Artificial immune systems for optimisation." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2480809.

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Jansen, Thomas, and Christine Zarges. "Artificial immune systems for optimisation." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605357.

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Jansen, Thomas, and Christine Zarges. "Artificial immune systems for optimisation." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330930.

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"Session details: Artificial immune systems." In GECCO05: Genetic and Evolutionary Computation Conference. ACM, 2005. http://dx.doi.org/10.1145/3249395.

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"Session details: Artificial immune systems." In GECCO05: Genetic and Evolutionary Computation Conference. ACM, 2005. http://dx.doi.org/10.1145/3249396.

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Alonso, F. R., D. Q. Oliveira, A. C. Z. De Souza, and B. I. L. Lopes. "Distribution system reconfiguration using artificial immune systems." In 2014 North American Power Symposium (NAPS). IEEE, 2014. http://dx.doi.org/10.1109/naps.2014.6965397.

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Lytvynenko, V., W. Wójcik, A. Smolarz, B. Suleimenov, and M. Junisbekov. "Combined Models of Artificial Immune Systems." In The XIIIth Conference WD 2016. CRC Press, 2017. http://dx.doi.org/10.1201/9781315098050-11.

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Reports on the topic "Artificial Immune Systems"

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Singh, Anjali. Amino Acids: Building Blocks of Proteins. ConductScience, 2022. http://dx.doi.org/10.55157/cs20220612.

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Amino acids are essential organic compounds serving as protein building blocks. Recognized for their biological roles, they underpin proteins' structure and interactions. Classified by polarity and nutritional necessity, essential amino acids, not synthesized by the body, include histidine, leucine, lysine, and more, while non-essential ones are produced internally. These molecules exhibit diverse functions, from neurotransmitter precursor synthesis to immune support. Industries leverage amino acids in animal feed, artificial sweeteners, flavor enhancers, and drug manufacturing, highlighting t
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Ampatzidis, Yiannis, Mahendra Bhandari, Andres Ferreyra, et al. AI in Agriculture: Opportunities, Challenges, and Recommendations. Chair Alex Thomasson. Council for Agricultural Science and Technology, 2025. https://doi.org/10.62300/iaag042514.

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Artificial Intelligence (AI) is rapidly being integrated into people’s lives, reshaping industries, and enabling previously unimagined innovation, even in agriculture. Generative AI focuses on creating content like text and pictures based on vast quantities of data. ExtensionBot is a generative AI platform that supports agricultural extension by providing farmers with accurate scientific information and specific recommendations. It has been shown to deliver more accurate responses to agricultural questions than broader generative AI models. Other forms of AI have been used to analyze data to p
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