Sensor Data Analysis and Management: The Role of Deep Learning (IEEE Press) - Tapa dura

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A. Suresh, PhD is an Associate Professor in the Department of Computer Science and Engineering in SRM Institute of Science & Technology, Tamil Nadu, India. With nearly two decades of experience in teaching, his areas of specializations include Data Mining, Artificial Intelligence, Image Processing, Multimedia and System Software. He has two patents and has published approximately 90 papers in International journals. He is a Senior Member of IEEE, ISTE, MCSI, IACSIT, IAENG, MCSTA and a Global Member of Internet Society (ISOC). He has hosted two special sessions for IEEE sponsored conferences in Osaka, Japan and Thailand.

R. Udendhran is an Assistant Professor Grade III in the Department of Computer Science and Engineering, at the Sri SaiRam Institute of Technology, Chennai, India.

M. S. Irfan Ahmed is Associate Professor in the Department of Computer Science and Information, Faculty of Science and Literature at Taibah University, Saudi Arabia. He is a member of ISTE, MCSI, IACSIT, and IAENG.

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Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis

Sensor Data Analysis and Management: The Role of Deep Learning, delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data.

The editors have curated a distinguished group of perceptive and concise articles that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.

The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of:

• A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data
• An exploration of the benefits of neural networks in real-time environmental sensor data analysis
• Practical discussions of 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

Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.