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Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing 2024 ed. [Hardback]

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  • Formāts: Hardback, 561 pages, height x width: 235x155 mm, 163 Illustrations, color; 66 Illustrations, black and white; IX, 561 p. 229 illus., 163 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1221
  • Izdošanas datums: 03-Dec-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031697685
  • ISBN-13: 9783031697685
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  • Hardback
  • Cena: 154,01 €*
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Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing 2024 ed.Palielināt
  • Formāts: Hardback, 561 pages, height x width: 235x155 mm, 163 Illustrations, color; 66 Illustrations, black and white; IX, 561 p. 229 illus., 163 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1221
  • Izdošanas datums: 03-Dec-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031697685
  • ISBN-13: 9783031697685
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031697685.html
Keywords:

This book deals with the application of ANNs in real-world problems requiring data analysis and signal processing. Artificial neural networks (ANNs) have emerged in society thanks to the large number of applications that have been used in an awe-inspiring way. These networks offer effective solutions to practical, real-world problems. The wide variety of application fields of the studies in the book is remarkable; these are related to sensorization, agriculture, healthcare, air pollution, video games, and cybersecurity, among others. To organize this variety, the chapters have been grouped into three sections related to: (1) Forecasting and Prediction, (2) Knowledge Discovery and Knowledge Management, and (3) Signal Processing. This book aims to reach readers interested in ANNs and their applications in different fields, so it is interesting not only for computer science but also for other related disciplines.

Forecasting and Prediction.- On the minimum error using Kolmogorov size
shallow neural network and Gradient Descent algorithms for complicated
univariate functions.- A Review on the Classification of Body Movement Time
Series to Support Clinical Decision-making.- FMarkNet Forecasting model based
on Neural networks and the Markowitz  Model.