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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 papers 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.
List of contents
About the Editors vii
List of Contributors ix
Preface xiii
1 Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment 1
N. Vijayaraj, G. Uganya, M. Balasaraswathi, V. Sivasankaran, Radhika Baskar, and A.S. Syed Fiaz
2 Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data 19
Dr. Vikram Rajpoot, Sudeep Ray Gaur, Aditya Patel, and Dr. Akash Saxena
3 Evaluation of Feature Selection Techniques in Intrusion Detection Systems Using Machine Learning Models in Wireless Ad Hoc Networks 33
T.J. Nagalakshmi, M. Balasaraswathi, V. Sivasankaran, D. Ravikumar, S. Joseph Gladwin, and S. Pravin Kumar
4 Neuro-Fuzzy-Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks 73
K.M. Karthick Raghunath and G.R. Anantha Raman
5 Feature Detection and Extraction Techniques for Real-Time Student Monitoring in Sensor Data Environments 97
Dr. V. Saravanan and Dr (Ms). N. Shanmuga Priya
6 Deep Learning Analysis of Location Sensor Data for Human-Activity Recognition 103
Hariprasath Manoharan, Ganesan Sivarajan, and Subramanian Srikrishna
7 A Quantum-Behaved Particle-Swarm-Optimization-Based KNN Classifier for Improving WSN Lifetime 117
Ajmi Nader, Helali Abdelhamid, and Mghaieth Ridha
8 Feature Detection and Extraction Techniques for Sensor Data 131
Dr. L. Priya, Ms. A. Sathya, and Dr. S. Thanga Revathi
9 Object Detection in Satellite Images Using Modified Pyramid Scene Parsing Networks 147
Akhilesh Vikas Kakade, S Rajkumar (Corresponding Author), K Suganthi, and L Ramanathan
10 Coronary Illness Prediction Using the AdaBoost Algorithm 161
G. Deivendran, S. Vishal Balaji, B. Paramasivan, S. Vimal (Corresponding Author)
11 Geographic Information Systems and Confidence Interval with Deep Learning Techniques for Traffic Management Systems in Smart Cities 173
Prisilla Jayanthi
Index 199
About the author
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.
Summary
Deep Learning provides powerful tools for solving complex modelling problems across a broad range of industries. The benefits of deep learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. As it is evident that deep learning will play a decisive role in solving major problems, this book will provide a better insight into the methods, algorithms and techniques for deep learning practitioners.
Topics that will be covered in this book include the following:
? Internet of things based Human Activity Recognition
? Benefits of Neural Network in Internet of things Application
? Supervised Learning Data Representation
? Convolutional Neural Network for Predicting physical activity based on smartphone sensor data
? Deep Learning Analysis of Location Sensor Data for Human Activity Recognition
? Machine learning for internet of things data analysis
? Deep Learning for IoT Data Analytics
