Sensor Data Analysis and Management: The Role of Deep Learning

Sensor Data Analysis and Management: The Role of Deep Learning provides an overview of the applications of deep learning techniques to the analysis of sensor data. It collects cutting-edge resources on recent techniques for fault detection and classification, Internet of Things sensors, and high-performance computer gathering and processing. The book includes real-time examples, a step-by-step approach, and discussions on the Internet of Things, neural networks, supervised learning, and boosting with XGBoost. It is perfect for industry practitioners and academics involved in deep learning and sensor data analysis.

\n Format: Hardback
\n Length: 224 pages
\n Publication date: 02 December 2021
\n Publisher: John Wiley and Sons Ltd
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Sensor Data Analysis and Management: The Role of Deep Learning delves into comprehensive insights into the methods, algorithms, and techniques employed in deep learning for analyzing sensor data. This comprehensive book serves as a valuable resource, compiling cutting-edge knowledge on diverse topics such as recent advancements in fault detection and classification in sensor data, the application of deep learning to Internet of Things (IoT) sensors, and a case study on high-performance computer gathering and processing of sensor data.

The editors have assembled a distinguished panel of insightful and concise papers, showcasing the immense potential of deep learning as a powerful tool for tackling complex modeling challenges across a wide array of industries. These industries encompass predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.

The book offers real-time examples of analyzing sensor data through deep learning algorithms, accompanied by a step-by-step guide for installing and training deep learning using the Python keras library. Additionally, readers will gain valuable insights from the inclusion of:

A thorough introduction to the Internet of Things, focusing on human activity recognition through wearable sensor data.

An exploration of the advantages of neural networks in real-time environmental sensor data analysis.

Practical discussions on supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition.

An analysis of boosting with XGBoost for sensor data analysis, making it an essential resource for industry practitioners and academics engaged in this field.

\n Weight: 542g\n
Dimension: 176 x 250 x 21 (mm)\n
ISBN-13: 9781119682424\n \n