Sensor data analysis and management : the role of deep learning / Annamalai Suresh, R. Udendhra, M.S Irfan Ahmed.

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.

TABLE 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

"Now days, the entire information and communication technology(ICT) is moved to new product management and applications including the companies like Apple iCloud, Google App Engine, Amazon EC2, IBM Cloud, VMware etc., due to the developing of cloud computing environment. Cloud computing is important emerging field in ICT to make the people life easier by increasing the productivity and processing speed, reducing the cost and time consumption, backup and storing the multiple data, automation in distribution of products and development. As well as, it is more challenging to offer trustworthy, powerful, quality and cost effective cloud services. In distributed cloud environment, large number of dynamic resources are circulated over the world. So, allocation of resource is complex in the middle of cloud user and cloud provider. Because, cloud providers be able to manage the resources with Quality of Service and maximum customer satisfaction. As a result, resource allocation is very essential in emerging of cloud environment. As well as, it leads to poor quality, poor performance and less customer satisfaction which is specified by service level agreement (SLA). So, an efficient heterogeneous resource allocation is essential to avoid these problems. In previous works, the resource allocation problem was solved based on two methods such as i) reactive methods and ii) proactive methods. Reactive method is a usual method in information and communication technology but it is not an effective method. The proactive method is developed to improve the performance of the system by allocating the resources in predefined manner. Though, proactive based methods including time series(TS), Queuing theory(QT) and reinforcement learning(RL) have some limitations. These include numerous data for TS, reconstruction of architecture when changing of resources for QT and large timing for RL. To resolve these proactive method constraints, feedback based approach are introduced in difficult computing systems"-- Provided by publisher.