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NIANN: Integration of ANN with Nature-Inspired Optimization Algorithms

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Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 233))

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Abstract

Artificial neural networks (ANNs) are stimulated according to the biological brain's connection of axons and dendrons. These neural networks perform a major part in the advancement of artificial intelligence and learning algorithms. Though initially used for image classification, in modern times applications of ANNs have been useful over numerous fields such as medical data mining, bioinformatics, natural language processing, time series forecasting, and in various optimization problems as well. Nature-inspired algorithms are a set of novel problem-solving approaches that are derived from various incidents occurring in nature around us. Each of the methods such as the BAT, genetic algorithm, or colony optimization methods were created by kee** a specific hard problem in mind. In recent times general purpose use of these nature-inspired algorithms has become widely popular in solving mainly optimization problems derived from the fields of NLP, machine learning, deep learning, classification, and feature selection as well. Nature-inspired algorithms mainly work by mimicking phenomena occurring in nature among various species on a macro scale. A set of nature-inspired algorithms such as the genetic algorithm family mimics the processes that occur in a microorganism such as a cell division, mutation, etc. Since these algorithms are inspired by nature and were developed kee** in mind achieving an optimal solution of a given hard problem, their application in general-purpose problems also yields satisfactory results. If an algorithm fails to achieve a satisfactory solution to a problem, it is easy to modify them according to the need of the given problem to overcome any obstacle. In this chapter, an approach is introduced that aims to combine the nature-inspired optimization algorithm with the learning model of artificial neural networks to provide a more accurate and streamlined output generation of the neural network. Nature-inspired algorithms can be used as a learning method in the ANN model. In contrast to that, an ANN can also be used as an objective function to a nature-inspired algorithm to improve its capability to generate an optimal solution. This chapter aims to explore both approaches in detail.

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Authors and Affiliations

  1. Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Haringhata, WB, 741249, India

    Soumen Kumar Pati & Manan Kumar Gupta

  2. Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, WB, 735102, India

    Ayan Banerjee

  3. Department of Computational and Applied Mathematics, Friedrich-Alexander-Universität, 91054, Erlangen-Nürnberg, Schloßplatz, Germany

    Rinita Shai

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  1. Soumen Kumar Pati
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  2. Ayan Banerjee
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  3. Manan Kumar Gupta
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  4. Rinita Shai
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Correspondence to Soumen Kumar Pati .

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Editors and Affiliations

  1. P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCB) University, Baripada, Odisha, India

    Janmenjoy Nayak

  2. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Systems Science and Informatics Unit, Indian Statistical Institute(ISI), Bangalore, India

    Saroj K. Meher

  5. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

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Pati, S.K., Banerjee, A., Gupta, M.K., Shai, R. (2023). NIANN: Integration of ANN with Nature-Inspired Optimization Algorithms. In: Nayak, J., Das, A.K., Naik, B., Meher, S.K., Brahnam, S. (eds) Nature-Inspired Optimization Methodologies in Biomedical and Healthcare. Intelligent Systems Reference Library, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-031-17544-2_6

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