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    Artificial Immune Systems: Fundamentals and Applications
    Artificial Immune Systems: Fundamentals and Applications
    Artificial Immune Systems: Fundamentals and Applications
    Ebook166 pages2 hours

    Artificial Immune Systems: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Artificial Immune Systems


    In the field of artificial intelligence, artificial immune systems (AIS) are a classification of rule-based, computationally intelligent machine learning systems that take their cues from the fundamentals and procedures of the immune system of vertebrates. When it comes to finding solutions to problems, algorithms are frequently based after the learning and memory capabilities of the immune system.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Artificial immune system


    Chapter 2: Immunology


    Chapter 3: Adaptive immune system


    Chapter 4: Computational immunology


    Chapter 5: Clonal selection algorithm


    Chapter 6: Immune network theory


    Chapter 7: Evolutionary computation


    Chapter 8: Bio-inspired computing


    Chapter 9: Glossary of artificial intelligence


    Chapter 10: Rule-based machine learning


    (II) Answering the public top questions about artificial immune systems.


    (III) Real world examples for the usage of artificial immune systems in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial immune systems' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial immune systems.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJun 22, 2023
    Artificial Immune Systems: Fundamentals and Applications

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      Book preview

      Artificial Immune Systems - Fouad Sabry

      Chapter 1: Artificial immune system

      In artificial intelligence, artificial immune systems (AIS) are a family of computationally intelligent, rule-based machine learning systems inspired by the concepts and processes of the vertebrate immune system. The algorithms are often based after the immune system's features of learning and memory for use in problem-solving.

      The study of artificial immune systems, also known as AIS, involves translating the structure and function of the immune system into computational systems and researching how such systems can be applied to the solution of computational problems in the fields of mathematics, engineering, and information technology. Biologically Inspired Computing (BIC) and Natural Computation (NC) are two subfields of the larger science of Artificial Intelligence (AI). AIS is a subfield of BIC and NC that is interested in machine learning.

      The term artificial immune systems (AIS) refers to problem-solving systems that are adaptive in nature, taking their cues from theoretical immunology as well as observable immunological activities, principles, and models.

      AIS is distinct from computational immunology and theoretical biology, both of which focus on simulating immunology through the use of computational and mathematical models in order to gain a deeper understanding of the immune system. Despite the fact that such models were the impetus for the development of the field of AIS and continue to serve as a source of fertile ground for inspiration, AIS is a distinct field in its own right. In conclusion, the area of Artificial Immune Systems (AIS) does not focus on research into the use of the immune system as a substrate for computation, in contrast to other areas such as DNA computing.

      AIS first appeared in the middle of the 1980s with the publication of publications on immunological networks written by Farmer, Packard, and Perelson (1986) and Bersini and Varela (1990). Nevertheless, it wasn't until the middle of the 1990s that AIS started to be recognized as its own discipline. 1994 was the year that saw the publication of the first publications on AIS by Forrest et al. (on negative selection) and Kephart et al. Dasgupta also undertook substantial research on Negative Selection Algorithms around this time. In 1995, Hunt and Cooke were the ones who first began working on Immune Network models; Timmis and Neal continued this work and added some enhancements to it. 2002 was the year that brought widespread attention to the research conducted by De Castro and Von Zuben, as well as Nicosia and Cutello. In 1999, Dasgupta was the editor of the first book to be published on artificial immune systems.

      At the moment, new concepts related to AIS are also being investigated. Some examples of these concepts are the risk theory and algorithms modeled after the innate immune system. Although there are others who argue that these new notions do not yet give any really 'new' abstract that is an improvement over already existing AIS algorithms, there are others who disagree. This, however, is a topic that is being highly disputed, and the discussion itself is now serving as one of the primary driving factors for the development of AIS. The investigation of degeneracy in AIS models is another new discovery that has taken place, Initially, the objective of AIS was to discover effective abstractions of the processes that are present in the immune system; but, more recently, the organization has shown interest in modeling the biological processes and in applying immune algorithms to challenges pertaining to bioinformatics.

      A textbook on immunological computing was written by Dasgupta and Nino in 2008, and it includes a synthesis of recent work related to immunity-based approaches and explains a broad range of applications. The textbook was released in 2008.

      Specific immunological theories that describe the operation and behavior of the adaptive immune system in mammalian organisms are the source of inspiration for the approaches that are often used.

      Clonal selection algorithm refers to a class of algorithms that were inspired by the clonal selection theory of acquired immunity. This theory attempts to explain how B and T lymphocytes improve their response to antigens over time through a process known as affinity maturation. Clonal selection algorithms are a type of algorithm. These algorithms center their attention on the Darwinian aspects of the theory, in which selection is driven by the affinity between antigens and antibodies, reproduction is driven by cell division, and variation is driven by somatic hypermutation. The most popular applications for clonal selection algorithms are in the areas of optimization and pattern recognition. Some of these algorithms are analogous to parallel hill climbing, while others are analogous to the genetic algorithm without the recombination operator.

      The algorithm for negative selection is modeled after the positive and negative selection processes that take place in the thymus during the process of T cell tolerance, which occurs during the maturation of T cells. The term negative selection refers to the process of identifying self-reacting cells and then eliminating (through apoptosis) those cells. Own-reacting cells are T cells that have the potential to select for and destroy self tissues. The classification and pattern recognition problem domains are common applications for this family of methods. These issue domains need the problem space to be described using the complement of information that is accessible. In the case of an anomaly detection domain, for instance, the algorithm will first prepare a set of exemplar pattern detectors that have been trained on typical (or non-anomalous) patterns. These detectors will then be used to model and identify patterns that have not been seen before, also known as anomalous patterns.

      Immune network algorithms are computer programs that are modeled after the idiotypic network theory that was developed by Niels Kaj Jerne. This theory explains how anti-idiotypic antibodies control the immune system. Immune network algorithms were developed as a result of this theory (antibodies that select for other antibodies). This family of algorithms focuses on the network graph structures that are involved, where antibodies (or cells that produce antibodies) represent the nodes, and the training procedure includes either increasing or pruning the connections that connect the nodes depending on affinity (similarity in the problems representation space). Immune network methods have found applications in the fields of grouping, data visualization, control, and optimization. These algorithms have features with those of artificial neural networks.

      Dendritic cell algorithms The dendritic cell algorithm (DCA) is an example of an algorithm that was built utilizing a multi-scale approach and was inspired by the immune system. This program utilizes a conceptual model of dendritic cells as its foundation (DCs). The DCA is abstracted and implemented through a process that involves examining and modeling various aspects of DC function. These aspects range from the molecular networks that are present within the cell to the behavior that is exhibited by a population of cells as a whole. This examination and modeling process is what allows the DCA to be developed. Through the use of multi-scale processing, the information contained inside the DCA is granulated into many separate levels.

      {End Chapter 1}

      Chapter 2: Immunology

      A subspecialty of medicine is immunology.

      Numerous medical specialties, including rheumatology, virology, allergology (dermatology), bacteriology, cancer, and even transplantation medicine, use immunology.

      Ilya Ilyich Mechnikov, a Russian biologist, coined the phrase to describe a situation in which the body defends itself against an outside force.

      The physical, chemical, and physiological features of the immune system's components in vitro make immunology important in reproductive medicine, According to psychiatry, psychiatric diseases cause reduced immune function, however there is no evidence of any particular immunological deficits.

      Before immunity, bone marrow, and primary lymphatic tissues including the spleen, tonsils, lymphatic veins, lymph nodes, adenoids, and liver were designated. However, many immune system components are cellular in nature and not connected to any particular organs, but rather are embedded or moving throughout the body's tissues.

      Traditional immunology has connections to the domains of medicine and epidemiology. It investigates the interactions between immunity, infections, and bodily systems. The Athens Plague in 430 BCE is the first time immunity is mentioned in writing. According to Thucydides, those who had previously recovered from the illness could care for the ill without getting sick themselves. Although numerous other ancient societies made mention of this occurrence, it wasn't until the 19th and 20th centuries that the idea became a scientific theory.

      The core discipline of immunology is the study of the molecular and cellular elements that make up the immune system, as well as their function and interaction. A more basic innate immune system and an acquired or adaptive immune system in vertebrates make up the immune system. Additional distinctions include humoral (or antibody) and cell-mediated components for the latter.

      The immune system has the ability to recognize both self and non-self. A chemical that triggers the immunological response is known as an antigen. Lymphocytes are the cells that recognize the antigen. They release antibodies as soon as they are recognized. Proteins known as antibodies work to destroy disease-causing bacteria. Instead of actively killing infections, antibodies mark antigens as targets for other immune cells, like phagocytes or NK cells, to destroy.

      The interaction between antibodies and antigens is referred to as the (antibody) response. Antigens are defined as anything that triggers the production of antibodies, whereas antibodies are specialized proteins released by a particular class of immune cells known as B lymphocytes (antibody generators). Understanding the characteristics of these two biological entities and the cellular reaction to them is the foundation of immunology.

      It is increasingly becoming more and more obvious that immunological responses play a role in the emergence of numerous prevalent ailments that aren't typically thought of as immunologic, such as cancer, diabetes, and neurodegenerative diseases like Alzheimer's. The immune system also has a direct role in infectious disorders such tuberculosis, malaria, hepatitis, pneumonia, diarrhea, and helminth infestations. Therefore, study in the field of immunology is crucial for breakthroughs in biotechnology, biomedical research, and modern medicine.

      Immunological research is continuing to become increasingly specialized, focusing on non-classical models of immunity and immune system-unrelated functions of cells, organs, and systems (Yemeserach 2010).

      Because of the affinity of the binding between the antibody and the antigen, the antibody is a superb instrument for substance detection using a variety of diagnostic procedures. Antibodies that are specific for a target antigen can be detected by conjugating them with a color-forming enzyme, an isotopic (radio) or fluorescence label, or both. Antibodies that cross-react with antigens that are not exact matches might cause false positives and other mistakes in these tests, however, because of the similarity between some antigens.

      Immunotherapy is the practice of treating a disease or ailment with elements of the immune system or antigens.

      Most frequently, immunotherapy is used to treat allergies, Crohn's disease and other autoimmune diseases, Rheumatoid arthritis and Hashimoto's thyroiditis, and certain cancers.

      Patients who are

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