Abstract
The interdisciplinary field of nature-inspired computing is a combination of combining nature computing science of biology, chemistry, physics, engineering, and mathematics which allows the development of new computational hardware, algorithms, or wetware for diagnosing, problem-solving, behaviors of organisms, and synthesis of patterns. Artificial immune systems (AIS) are a sub-field of biologically-inspired computing through machine learning and artificial intelligence (AI). AIS is new algorithm developed from the principles of the human immune system. The AIS is conceptualizing the structure and function of the immune system to computational systems and investigating the applications of the immune system toward solving computational problems. AIS is a dynamic research area used for fault detection, diagnosis, optimization problems, and various approaches to AIS have wide applications. In this chapter, we made an attempt to describe the role of AIS in data analysis and providing solutions for complex diagnostic problems.
Authors K. R. Dasegowda, Akshar Radhakrishnan, and Majji Rambabu contributed equally to this work.
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References
Aldhaheri S, Alghazzawi D, Cheng L, Alzahrani B, Al-Barakati A (2020) Deepdca: novel network-based detection of IoT attacks using artificial immune system. Appl Sci 10(6):1909
Al-Enezi JR, Abbod MF, Alsharhan S (2011) Artificial immune systems-models, algorithms and applications
Alizadeh E, Meskin N, Khorasani K (2016) A negative selection immune system inspired methodology for fault diagnosis of wind turbines. IEEE Trans Cybern 47(11):3799–3813
Almufti SM (2019) Historical survey on metaheuristics algorithms. International Journal of Scientific World. 7(1):1
Ariff NM, Khalid NE, Hashim R, Noor NM (2016) Selfish gene algorithm versus genetic algorithm: a review. In: IOP conference series: materials science and engineering, vol 160, no 1. IOP Publishing, p 012098
Bayar N, Darmoul S, Hajri-Gabouj S, Pierreval H (2015) Fault detection, diagnosis and recovery using artificial immune systems: a review. Eng Appl Artif Intell 1(46):43–57
Brabazon A, O’Neill M, McGarraghy S (2015) Artificial immune systems. In: Natural computing algorithms. Springer, Berlin, Heidelberg, pp 301–332
De Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, vol 2000, pp 36–39
Chen H, Zhang Q, Luo J, Xu Y, Zhang X (2020) An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl Soft Comput 1(86):105884
Chiroma H, Herawan T, Fister I Jr, Fister I, Abdulkareem S, Shuib L, Hamza MF, Saadi Y, Abubakar A (2017) Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm. Appl Soft Comput 1(61):149–173
Çipe F, Arısoy ES, Correa AG (2022) Immunological Responses to Infection. In: Pediatric ENT infections. Springer, Cham, pp 3–17
Daudi J (2015) An overview of application of artificial immune system in swarm robotic systems. Adv Robot Autom 4(1)
De Lacerda MG, de Araujo Pessoa LF, de Lima Neto FB, Ludermir TB, Kuchen H (2021) A systematic literature review on general parameter control for evolutionary and swarm-based algorithms. Swarm Evol Comput 1(60):100777
Falcón-Cardona JG, Coello CA (2020) Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput Surveys (CSUR) 53(2):1–35
Fan X, Sayers W, Zhang S, Han Z, Ren L, Chizari H (2020) Review and classification of bio-inspired algorithms and their applications. J Bionic Eng 17(3):611–631
Fernandez-Leon JA, Acosta GG, Rozenfeld A (2014) How simple autonomous decisions evolve into robust behaviours?: a review from neurorobotics, cognitive, self-organized and artificial immune systems fields. Biosystems 1(124):7–20
Gendreau M, Potvin JY (eds) (2010) Handbook of metaheuristics. Springer, New York
Greensmith J, Aickelin U, Tedesco G (2010) Information fusion for anomaly detection with the dendritic cell algorithm. Inf Fusion 11(1):21–34
Hooper LV, Littman DR, Macpherson AJ (2012) Interactions between the microbiota and the immune system. Science 336(6086):1268–1273
Ishida Y (1990) Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model. In: 1990 IJCNN international joint conference on neural networks. IEEE, pp 777–782
Iwasaki A, Medzhitov R (2015) Control of adaptive immunity by the innate immune system. Nat Immunol 16(4):343–353
Jegadeeshwaran R, Sugumaran V (2015) Brake fault diagnosis using clonal selection classification algorithm (CSCA)—A statistical learning approach. Eng Sci Technol Int J 18(1):14–23
Jim LE, Islam N, Gregory MA (2022) Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes. Comput Secur 1(113):102538
Kar AK (2016) Bio inspired computing—A review of algorithms and scope of applications. Expert Syst Appl 15(59):20–32
Kashani AR, Camp CV, Rostamian M, Azizi K, Gandomi AH (2021) Population-based optimization in structural engineering: a review. Artif Intell Rev 4:1–08
Knight T, Timmis J (2001) AINE: an immunological approach to data mining. In: Proceedings 2001 IEEE international conference on data mining. IEEE Computer Society, pp 297–297
Kogut MH, Lee A, Santin E (2020) Microbiome and pathogen interaction with the immune system. Poult Sci 99(4):1906–1913
Li W, Wang GG (2021) Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Eng Comput 4:1–29
Li G, Jin Y, Akram MW, Chen X, Ji J (2018) Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions—A review. Renew Sustain Energy Rev 1(81):840–873
Liu J, Tsui KC (2006) Toward nature-inspired computing. Commun ACM 49(10):59–64
Luo Q, Wang H, Zheng Y, He J (2020) Research on path planning of mobile robot based on improved ant colony algorithm. Neural Comput Appl 32(6):1555–1566
Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Des Eng 6(3):284–295
Mohapatra S, Khilar PM (2020) Immune inspired fault diagnosis in wireless sensor network. In: Nature inspired computing for wireless sensor networks. Springer, Singapore, pp 103–116
Molina D, Poyatos J, Ser JD, García S, Hussain A, Herrera F (2020) Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations. Cogn Comput 12(5):897–939
Müller V, De Boer RJ, Bonhoeffer S, Szathmáry E (2018) An evolutionary perspective on the systems of adaptive immunity. Biol Rev 93(1):505–528
Niu B, Wang H (2012) Bacterial colony optimization. Discrete Dyn Nat Soc
Nunoo-Mensah H, Boateng KO, Gadze JD (2018) The adoption of socio-and bio-inspired algorithms for trust models in wireless sensor networks: a survey. Int J Commun Syst 31(7):e3444
Pérez J, Cabrera JA, Castillo JJ, Velasco JM (2018) Bio-inspired spiking neural network for nonlinear systems control. Neural Netw 1(104):15–25
Rostami M, Berahmand K, Nasiri E, Forouzandeh S (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 1(100):104210
Sam-Yellowe TY, Sam-Yellowe TY (2021) Immunology: overview and laboratory manual. Springer
Siddique N, Adeli H (2015) Nature inspired computing: an overview and some future directions. Cogn Comput 7(6):706–714
Somayaji A, Hofmeyr S, Forrest S (1998) Principles of a computer immune system. In: Proceedings of the 1997 workshop on new security paradigms, pp 75–82
Theocharopoulou G, Giannakis K, Papalitsas C, Fanarioti S, Andronikos T (2019) Elements of game theory in a bio-inspired model of computation. In: 2019 10th International conference on information, intelligence, systems and applications (IISA). IEEE, pp 1–4
Timmis J, Hone A, Stibor T, Clark E (2008) Theoretical advances in artificial immune systems. Theoret Comput Sci 403(1):11–32
Wang H, Wang W, Xiao S, Cui Z, Xu M, Zhou X (2020) Improving artificial bee colony algorithm using a new neighborhood selection mechanism. Inf Sci 1(527):227–240
Yadav A, Vishwakarma DK (2020) A comparative study on bio-inspired algorithms for sentiment analysis. Clust Comput 23(4):2969–2989
Zedadra O, Guerrieri A, Jouandeau N, Spezzano G, Seridi H, Fortino G (2018) Swarm intelligence-based algorithms within IoT-based systems: a review. J Parallel Distrib Comput 1(122):173–187
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Dasegowda, K.R. et al. (2023). Nature-Inspired Computing: Scope and Applications of Artificial Immune Systems Toward Analysis and Diagnosis of Complex Problems. In: Raza, K. (eds) Nature-Inspired Intelligent Computing Techniques in Bioinformatics. Studies in Computational Intelligence, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-19-6379-7_8
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