Artificial Intelligence Techniques in IoT Sensor Networks

Mohamed Elhoseny, K Shankar, Mohamed Abdel-Basset, Mohamed Elhoseny, K Shankar, Mohamed Abdel-Basset

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Print ISBN: 9780367439255
• eBook ISBN: 9781000318821
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks.

This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master's students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications.

This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

CHAPTER 1
Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images

An Artificial Intelligence-Based IoT Implementation for Teleradiology Network

S. N. Kumar,¹ A. Lenin Fred,² L. R. Jonisha Miriam,² Ajay Kumar H.,² Parasuraman Padmanabhan,³ and Balazs Gulyas³

¹Amal Jyothi College of Engineering, Kanjirappally, Kerala, India

²Mar Ephraem College of Engineering and Technology, Elavuvilai, Tamil Nadu, India

³Nanyang Technological University, Singapore

CONTENTS

1.1Introduction

1.2Proposed Methodology

1.2.1Fuzzy C-Means Clustering

1.2.2Formulation of Nonlinear Tensor Diffusion Filtered Image

1.2.3Improved Adaptive Regularized Kernel FCM

1.3Results and Discussion

1.4Conclusion

Acknowledgments

References

1.1 INTRODUCTION

Segmentation method is exploited for the extraction of desired region of interest (ROI) and in medical image processing, its role is pivotal in the delineation of anatomical organs and anomalies like tumor and cyst. There is no universal algorithm for various modalities, and the selection of segmentation technique relies on the type of imaging modality and ROI. Grouping of segmentation techniques relies on the nature of evolution and, in general, it is classified into semiautomatic and fully automatic. The semiautomatic algorithm requires human intervention: the discrete positioning of points in the level set model [1], selection of foreground, and background seed region in graph cut [2].

The big data analytics and Internet of Things (IoT) trends influence healthcare in the radiology sectors for the classification in efficient diagnosis [3]. A real-time mobile camera terminal captures the skin images that interact with the remote datacenter with a deep learning model, which improvises the...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• About the Editors
• Chapter 1: Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images
• Chapter 2: Artificial Intelligence-Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things-Enabled Logistic Transportation Planning
• Chapter 3: Butterfly Optimization-Based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things
• Chapter 4: An Energy-Efficient Fuzzy Logic-Based Clustering with Data Aggregation Protocol for WSN-Assisted IoT System
• Chapter 5: Analysis of Smart Home Recommendation System from Natural Language Processing Services with Clustering Technique
• Chapter 6: Metaheuristic-Based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks
• Chapter 7: Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT-Based Cloud Environment
• Chapter 8: Energy-Efficient Unequal Clustering Algorithm Using Hybridization of Social Spider with Krill Herd in IoT-Assisted Wireless Sensor Networks
• Chapter 9: IoT Sensor Networks with 5G-Enabled Faster RCNN-Based Generative Adversarial Network Model for Face Sketch Synthesis
• Chapter 10: Artificial Intelligence-Based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-Based Internet of Things
• Chapter 11: An Energy-Efficient Quasi-Oppositional Krill Herd Algorithm-Based Clustering Protocol for Internet of Things Sensor Networks
• Chapter 12: An Effective Social Internet of Things (SIoT) Model for Malicious Node Detection in Wireless Sensor Networks
• Chapter 13: IoT-Based Automated Skin Lesion Detection and Classification Using Gray Wolf Optimization with Deep Neural Network
• INDEX