Applications of Artificial Neural Networks for Nonlinear Data

Applications of Artificial Neural Networks for Nonlinear Data
PDF
  • eBook:
    Applications of Artificial Neural Networks for Nonlinear Data
  • Author:
    Hiral Ashil Patel, A. V. Senthil Kumar
  • Edition:
    -
  • Categories:
  • Data:
    September 25, 2020
  • ISBN:
    1799840425
  • ISBN-13:
    9781799840428
  • Language:
    English
  • Pages:
    315 pages
  • Format:
    PDF

Book Description
Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

Content

Section 1 - Concepts and Categorization
Chapter 1. Literature Survey for Applications of Artificial Neural Networks
Chapter 2. Comprehensive Modelling of ANN
Chapter 3. Fundamental Categories of Artificial Neural Networks
Chapter 4. Applications of ANN for Agriculture Using Remote Sensed Data
Chapter 5. Classification Approach for Sentiment Analysis Using Machine Learning

Section 2 - Applications and Analytics
Chapter 6. Predicting the Determinants of Mobile Payment Acceptance: A Hybrid SEMNeural Network Approach
Chapter 7. An Integrated SEM-Neural Network Approach for Predicting Mobile Banking Determinants of Adoption in Turkey
Chapter 8. Fault Severity Sensing for Intelligent Remote Diagnosis in Electrical Induction Machines: An Application for Wind Turbine Monitoring
Chapter 9. Wavelet Neural Networks and Equalization of Nonlinear Satellite Communication Channel
Chapter 10. Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN
Chapter 11. Latest Technology and Future Trends

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