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ARTIFICIAL INTELLIGENT

Topic: Artificial Immune system

Name :Md Mehtab Khan

Class:B.Tech,CSE5A

Rollno: RM1801B49

Regno:10802698

Abstract

The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational point of view, the immune system has much to offer by way of inspiration to computer scientists and engineers alike. As computational problems become more complex, increasingly, people are seeking out novel approaches to these problems, often turning to nature for inspiration. A great deal of attention is now being paid to the vertebrae immune system as a potential source of inspiration, where it is thought that different insights and alternative solutions can be gleaned, over and above other biologically inspired methods. Given this rise in attention to the immune system, it seems appropriate to explore this area in some detail. This survey explores the salient features of the immune system that are inspiring computer scientists and engineers to build Artificial Immune Systems (AIS). An extensive survey of applications is presented, ranging from network security to optimisation and machine learning. However, this is not complete, as no survey ever is, but it is hoped this will go some way to illustrate the potential of this exciting and novel area of research.

Artificial immune system

In computer science, Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune

system. The algorithms typically exploit the immune system's characteristics of learning and memory to solve a problem.

Definition

The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of ]], Biologically-inspired computing, and Natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.

AIS is distinct from computational immunology and theoretical biology that are concerned with simulating immunology using computational and mathematical models towards better understanding the immune system, although such models initiated the field of AIS and continue to provide a fertile ground for inspiration. Finally, the field of AIS is not concerned with the investigation of the immune system as a substrate computation, such as DNA computing.

Techniques

Clonal Selection Algorithm :

In Artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation. Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator.

Negative Selection Algorithm :

Inspired by the positive and negative selection processes that occur during the maturation of T cells in the thymus called T cell tolerance. Negative selection refers to the identification and deletion (apoptosis) of self-reacting cells, that is T cells that may select for and attack self tissues. This class of algorithms are typically used for classification and pattern recognition problem domains where the problem space is modeled in the complement of available knowledge. For example in the case of an anomaly detection domain the algorithm prepares a set of exemplar pattern detectors trained on normal (non-anomalous) patterns that model and detect unseen or anomalous patterns.

Immune Network Algorithms :

Algorithms inspired by the idiotypic network theory proposed by Niels Kaj Jerne that describes the regulation of the immune system by anti-idiotypic antibodies (antibodies that select for other antibodies). This class of algorithms focus on the network graph structures involved where antibodies (or antibody producing cells) represent the nodes and the training algorithm involves growing or pruning edges between the nodes based on affinity (similarity in the problems representation space). Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks.

Dendritic Cell Algorithms :

The Dendritic Cell Algorithm (DCA) is an example of an immune inspired algorithm developed using a multi-scale approach. This algorithm is based on an abstract model of dendritic cells (DCs). The DCA is abstracted and implemented through a process of examining and modeling various aspects of DC function, from the molecular networks present within the cell to the behaviour exhibited by a population of cells as a whole. Within the DCA information is granulated at different layers, achieved through multi-scale processing.

Bio-inspired computing:

Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers. Biologically inspired computing is a major subset of natural computation

Areas of research

Some areas of study encompassed under the canon of biologically inspired computing, and their biological counterparts:

• genetic algorithms ↔ evolution• biodegradability prediction ↔ biodegradation• cellular automata ↔ life• emergent systems ↔ ants, termites, bees, wasps• neural networks ↔ the brain• artificial life ↔ life• artificial immune systems ↔ immune system• rendering (computer graphics) ↔ patterning and rendering of animal

skins, bird feathers, mollusk shells and bacterial colonies• Lindenmayer systems ↔ plant structures• communication networks and protocols ↔ epidemiology and the spread

of disease• membrane computers ↔ intra-membrane molecular processes in the

living cell• excitable media ↔ forest fires, the Mexican wave, heart conditions, etc• sensor networks

Bio-inspired computing and AI

The way in which bio-inspired computing differs from traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to the what could be described as 'creationist' methods used in traditional AI. In traditional AI, intelligence is often programmed from above: the programmer is the creator, and makes something and imbues it with its intelligence. Bio-inspired computing, on the other hand, takes a more bottom-up, decentralised approach; bio-inspired techniques often involve the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules, and a method of iteratively applying those rules. After several generations

of rule application it is usually the case that some forms of complex behaviour arise. Complexity gets built upon complexity until the end result is something markedly complex, and quite often completely counterintuitive from what the original rules would be expected to produce (see complex systems). For this reason, in neural network models, it is necessary to accurately model an in vivo network, by live collection of "noise" coefficients that can be used to refine statistical inference and extrapolation as system complexity increases [1].

Natural evolution is a good analogy to this method–the rules of evolution (selection, recombination/reproduction, mutation and more recently transposition) are in principle simple rules, yet over thousands of years have produced remarkably complex organisms. A similar technique is used in genetic algorithms.

Computational immunology

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.

The explosive growth of bioinformatics techniques and applications in the post-genomics era has radically transformed immunology research. This has led to a comparable growth in the field of computation immunology, or immunoinformatics.

Computational intelligence

Computational intelligence (CI) is an offshoot of artificial intelligence. As an alternative to classical artificial intelligence it rather relies on heuristic algorithms such as in fuzzy systems, neural networks and evolutionary computation. In addition, computational intelligence also embraces techniques that use Swarm intelligence, Fractals and Chaos Theory, Artificial immune systems, Wavelets, etc.

Computational intelligence combines elements of learning, adaptation, evolution and Fuzzy logic (fuzzy sets) to create programs that are, in some sense, intelligent. Computational intelligence research does not reject statistical methods, but often gives a complementary view (as is the case with fuzzy systems). Artificial neural networks is a branch of computational intelligence that is closely related to machine learning.

Computational intelligence is further closely associated with soft computing, connectionist systems and cybernetics.

Evolutionary computation

In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.

Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution.

Immunocomputing

Immunocomputing explores the principles of information processing that proteins and immune networks utilize in order to solve specific complex problems while protected from viruses, noise, errors and intrusions.

It intends to establish:

• A proper mathematical framework• A new kind of computing• A new kind of hardware

The main difference with other kinds of computing lay on the function of its basic element, the formal protein, defined according with its biological prototype and its mathematical model.

The main biophysical issues considered in immunocomputing are:

• Free folding to a stable state (inspiration for the Formal Protein)• Free binding with other elements dependent on their reciprocal states

(inspiration for the Formal Immune Networks)

Formal immune networks (FINs) have as closest model the idiotypic network of N. Jerne but they consider specific mechanisms of interactions between proteins. FINs are able to learn, recognize and solve problems.

Swarm intelligence

Swarm intelligence (SI) describes the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems

ACKNOWLEDGMENT

Md. Asfaque khan Student of B.Tech (CSE) would like to thank my friends. This term paper has surely enhance my knowledge and the credit for completion of this term paper is shared by my subject teacher who has given me guidelines, and to the makers of google for providing me sufficient and accurate data.

REFERENCES

http://en.wikipedia.org/wiki/Artificial_immune_system

http://en.wikipedia.org/wiki/Biologically_inspired_computing

http://en.wikipedia.org/wiki/Computational_immunology

http://en.wikipedia.org/wiki/Computational_intelligence

http://en.wikipedia.org/wiki/Evolutionary_computation

http://en.wikipedia.org/wiki/Immunocomputing

http://en.wikipedia.org/wiki/Swarm_intelligence