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Springer System Design for Epidemics Using Machine Learning - Springer

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Description

This comprehensive guide from Springer explores the intersection of machine learning, deep learning, and epidemic modelling to design effective systems for disease tracking and prediction. Written for computer scientists, engineers, and researchers, the book covers practical methodologies for leveraging artificial intelligence in epidemiological systems. Part of the Signals and Communication Technology series, it provides both theoretical foundations and real-world applications of machine learning techniques applied to epidemic data analysis, forecasting models, and public health decision-making systems. The work bridges gaps between classical epidemiology and modern AI approaches, offering valuable insights for professionals developing intelligent systems to combat infectious disease outbreaks.

Key Features

Covers machine learning and deep learning applications specifically designed for epidemic system modelling and prediction.

Part of the Signals and Communication Technology series, ensuring rigorous technical coverage and peer-reviewed content.

Combines theoretical foundations with real-world case studies for researchers and engineers working in public health technology.

Published by Springer, a leading academic publisher known for authoritative technical and scientific works.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
02 February 2024
Listed Since
07 January 2024

Barcode

No barcode data available

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