Nature-inspired computing for smart application design

Nature-inspired computing for smart application design / Santosh Kumar Das, Thanh-Phong Dao, Thinagaran Perumal, editors.

This book focuses primarily on the nature-inspired approach for designing smart applications. It includes several implementation paradigms such as design and path planning of wireless network, security mechanism and implementation for dynamic as well as static nodes, learning method of cloud computi...

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Bibliographic Details
Other Authors: Das, Santosh Kumar (Editor), Dao, Thanh-Phong (Editor), Perumal, Thinagaran (Editor)
Format: Electronic eBook
Language:English
Published: Singapore : Springer, 2021.
Series:Springer tracts in nature-inspired computing.
Subjects:
Natural computation.
Artificial intelligence.
artificial intelligence.
Artificial intelligence
Natural computation
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • List of Reviewers
  • Contents
  • Editors and Contributors
  • Smart Design and Its Applications: Challenges and Techniques
  • 1 Introduction
  • 2 Some Applications for Smart Design
  • 2.1 City and Environment
  • 2.2 Intelligent Networking
  • 2.3 Security and Management
  • 3 Some Techniques for Smart Design
  • 4 Conclusions
  • References
  • City and Environment
  • Automatic Generation Control Scheme for Power Quality Improvement of Multi-source Power Generating System with Secondary Controller Optimization Using Parameter-Setting-Free Harmony Search
  • 1 Introduction
  • 2 Single-Area Multi-source Power System Modeling
  • 3 Controller Design
  • 4 Proposed Parameter-Setting-Free Harmony Search Algorithm-Tuned PID Controller
  • 5 Simulation Results and Discussions
  • 6 Conclusion
  • References
  • Environmental Sound Classification Using Neural Network and Deep Learning
  • 1 Introduction
  • 1.1 Scope
  • 2 Related Work
  • 3 Problem Formulation
  • 3.1 Segmenting an Audio Signal Into Windows
  • 4 Feature Extraction
  • 4.1 Features Used for Training the Neural Network
  • 5 Designing the Cost Function for Bayesian Regularised Neural Network
  • 5.1 Over-Fitting and Regularization of Neural Networks
  • 5.2 Bayesian Regularization
  • 6 Bayesian Regularised Neural Networks for Urban Sound Noise Classification: A Deep Learning Approach
  • 6.1 Deep Neural Network Design
  • 6.2 Novel Design: Two Input-Output Neural Network Model
  • 6.3 Classification of Multiple Sound Source
  • 6.4 Novel Approach: Multi-label Classification Using Bayesian Regularized Fitnet Model and Deep Lerning Approach
  • 7 Conclusion and Future Work
  • 8 Problems
  • 8.1 Feature Extraction
  • 8.2 Designing of Optimal Neural Network
  • 8.3 Single Label Detection
  • 8.4 Multi Label Detection
  • References
  • Feature Selection Method Using CFO and Rough Sets for Medical Dataset
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Central Force Optimization
  • 2.2 Rough Set Theory
  • 3 Proposed Algorithm
  • 3.1 Preprocessing of Gene Expression Data
  • 3.2 Fitness Function
  • 4 Experimental Result
  • 4.1 Parameter Setting and Datasets
  • 4.2 Results and Discussion
  • 5 Conclusion
  • References
  • Fuzzy-Based Optimal Solution for Minimization of Loss of Company Based on Uncertain Environment
  • 1 Introduction
  • 2 Related Works
  • 3 Proposed Method
  • 4 Simulation and Analysis
  • 5 Conclusions
  • References
  • Intelligent Networking
  • Impacts of Computational Techniques for Wireless Sensor Networks
  • 1 Introduction
  • 1.1 WSN: Basic Introduction
  • 1.2 WSN: Basic Application Area
  • 2 Need of Optimization
  • 3 Description of Applied Algorithm
  • 3.1 Dragonfly Algorithm
  • 3.2 Quasi-opposition Atom Search Optimization (QOASO) Algorithm
  • 3.3 Pathfinder Algorithm (PA)
  • 3.4 Salp Swarm Algorithm
  • 4 Optimization Techniques Applied in WSN
  • 4.1 Modeling of Consumption of Energy
  • 4.2 Path Loss Model
  • 4.3 System Lifetime Model

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