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Artificial Intelligence Techniques in IoT Sensor Networks



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ISBN 9780367439255
January 22, 2021 Forthcoming by Chapman and Hall/CRC
336 Pages 98 B/W Illustrations

 
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Book Description

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 intends 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 book introduces the researchers and aspiring academicians the subject of 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 be benefited directly from this book. Moreover, graduate and master students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real time applications.

The book is written in a simple and easy language, discusses the concepts from fundamentals which relieve the requirement of earlier background of the field finds it readable. From this expectation and experience, we believe that every library will be interested to collect copies of this book.

Table of Contents

Preface

Chapter 1

Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images – An Artificial Intelligence Based IoT Implementation for Teleradiology Network

 

1.1 Introduction

1.2 Proposed Methodology

            1.2.1 Fuzzy C Means Clustering

1.3 Results and Discussion

1.4 Conclusion

References

Chapter 2

Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning

 

2.1. Introduction

2.2. Related works

2.3. Proposed Method

            2.3.1. Package Partitioning

            2.3.2. Planning of delivery path using HFMPSO algorithm

            2.3.3. Inserting Pickup Packages

2.4. Experimental Validation

            2.4.1. Performance analysis under varying package count

            2.4.2. Performance analysis under varying vehicle capacities

            2.4.3. Computation Time (CT) analysis

2.5. Conclusion

References

 

Chapter 3

Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things

 

3.1. Introduction

3.2. Related works

3.3. The Proposed Method

          3.3.1. Hadoop Ecosystem

          3.3.2. BOA based FS process

          3.3.3. GBT based Classification

3.4. Experimental Analysis

          3.4.1. FS Results analysis

          3.4.2. Classification Results Analysis

          3.4.3. Energy Consumption Analysis

          3.4.4. Throughput Analysis

3.5. Conclusion

References

Chapter 4

An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system

 

4. 1. Introduction

4. 2. Background Information

          4. 2.1. Clustering objective

          4. 2. 2. Clustering characteristics

4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR)           protocol

          4. 3. 1. Fuzzy based Clustering process

          4. 3. 2. Data aggregation process

          4. 4. Performance Validation

4. 5. Conclusion

References

Chapter 5

Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique

 

5. 1. Introduction

5. 2. Review of Literatures

5. 3. Smart Home- Cloud Backend Services

          5. 3.1 Internet of Things (IoT)

5. 4. Our Proposed Approach

          5. 4.1 Natural Language Processing Services (NLPS)

          5. 4. 2 Pipeline Structure for NLPS

          5. 4. 3 Clustering Model

5. 5. Results and analysis

5. 6. Conclusion

References

Chapter 6

Metaheuristic based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks

 

6. 1. Introduction

6. 2. Proposed Methodology

           6. 2. 1. Deflate based Compression Model

           6. 2. 2. SMO-KELM based Diagnosis Model

6. 3. Experimental results and validation

6. 4. Conclusion

References

Chapter 7

Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT Based Cloud Environment

 

7. 1. Introduction

7. 2. The Proposed Model

          7. 2.1. SMOTE Model

          7. 2.2. FSVM based Classification Model

7. 3. Simulation Results and Discussion

7. 4. Conclusion

References

Chapter 8

Energy Efficient Unequal Clustering Algorithm using Hybridization of Social Spider with Krill Herd in IoT Assisted Wireless Sensor Networks

 

8. 1. Introduction

8. 2. Research Background

8. 3. Literature survey

8. 4. The proposed SS-KH algorithm

          8. 4. 1. SS based TCH selection

          8. 4. 2. KH based FCH algorithm

8. 5. Experimental validation

          8. 5. 1 Implementation setup

          8. 5. 2. Performance analysis

8. 6. Conclusion

References

Chapter 9

IoT Sensor Networks with 5G Enabled Faster RCNN Based Generative Adversarial Network Model for Face Sketch Synthesis

 

9. 1. Introduction

9. 2. The Proposed FRCNN-GAN Model

          9. 2.1. Data Collection

          9. 2.2. Faster R-CNN based Face Recognition

          9. 2.3. GAN based Synthesis Process

9. 3. Performance Validation

9. 4. Conclusion

References

Chapter 10

Artificial Intelligence based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-based Internet of Things

 

10. 1. Introduction

10. 2. Literature review

10. 3. Proposed Methodology

          10. 3.1. Preprocessing

          10. 3.2. Feature extraction

          10. 3.3. Feature selection using ranking method

          10. 3.4. Cyberbully detection

          10. 3.5. Dataset Description

10. 4. Result and discussion

          10. 4.1. Evaluation Metrics

          10. 4.2. Comparative analysis

10. 5. Conclusion

References

Chapter 11

An Energy Efficient Quasi Oppositional Krill Herd Algorithm based Clustering Protocol for Internet of Things Sensor Networks

 

11. 1. Introduction

11. 2. The Proposed Clustering algorithm

11. 3. Performance Validation

11. 4. Conclusion

References

Chapter 12

An effective Social Internet of Things (SIoT) Model for Malicious node detection in wireless sensor networks

 

12. 1. Introduction

12. 2. Review of Recent Kinds of literature

12. 3. Network Model: SIoT

12. 3.1 Malicious Attacker Model in SIoT

12. 4. Proposed MN in SIoT System

12. 4.1 Trust based Grouping in SIoT network

12. 4.2 Exponential Kernel Model for MN detection

12. 4.3.1 Example of Proposed Detection System

12. 4.4 Detection Model

12. 5. Results and analysis

12. 6. Conclusion

References

Chapter 13

IoT Based Automated Skin Lesion Detection and Classification using Grey Wolf Optimization with Deep Neural Network

 

13. 1. Introduction

13. 2. The Proposed GWO-DNN Model

          13. 2.1. Feature Extraction

          13. 2.2. DNN based classification

13. 3. Experimental Validation

13. 4. Conclusion

References

Index

 

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Editor(s)

Biography

Dr. Mohamed Elhoseny is currently an Assistant Professor at the Faculty of Computers and Information, Mansoura University where he is also the Director of Distributed Sensing and Intelligent Systems Lab. Besides, he has been appointed as an ACM Distinguished Speaker from 2019 to 2022. Collectively, Dr. Elhoseny authored/co-authored over 85 ISI Journal articles in high-ranked and prestigious journals such as IEEE Transactions on Industrial Informatics (IEEE), IEEE Transactions on Reliability (IEEE), Future Generation Computer Systems (Elsevier), and Neural Computing and Applications (Springer). Besides, Dr. Elhoseny authored/edited Conference Proceedings, Book Chapters, and 10 books published by Springer and Taylor & Francis. His research interests include Smart Cities, Network Security, Artificial Intelligence, Internet of Things, and Intelligent Systems. Dr. Elhoseny serves as the Editor-in-Chief of International Journal of Smart Sensor Technologies and Applications, IGI Global. Moreover, he is an Associate Editor of many journals such as IEEE Access (Impact Factor 3.5), IEEE Future Directions, PLOS One journal (Impact Factor 2.7), Remote Sensing (Impact Factor 3.5), and International Journal of E-services and Mobile Applications, IGI Global (Scopus Indexed). Also, he is an Editorial Board member in reputed journals such as Applied Intelligence, Springer (Impact Factor 1.9). Moreover, he served as the co-chair, the publication chair, the program chair, and a track chair for several international conferences published by IEEE and Springer.

K. Shankar is currently a Postdoctoral Fellow with Department of Computer Applications, Alagappa University, Karaikudi, India. He has authored/coauthored over 54 ISI Journal articles (with total Impact Factor 150+) and more than 100 Scopus Indexed Articles.  He has guest-edited several special issues at many journals published by SAGE, TechScience, Inderscience and MDPI. He has served as Guest Editor and Associate Editor in SCI, Scopus indexed journals like Elsevier, Springer, IGI, Wiley & MDPI. He has served as chair (program, publications, Technical committee and track) on several International conferences. He has delivered several invited and keynote talks, and reviewed the technology leading articles for journals like Scientific Reports – Nature, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Reliability, the IEEE Access and the IEEE Internet of Things. He has authored/edited Conference Proceedings, Book Chapters, and 2 books published by Springer. He has been a part of various seminars, paper presentations, research paper reviews, and convener and a session chair of the several conferences. He displayed vast success in continuously acquiring new knowledge and applying innovative pedagogies and has always aimed to be an effective educator and have a global outlook.  His current research interests include Healthcare applications, Secret Image Sharing Scheme, Digital Image Security, Cryptography, Internet of Things, and Optimization algorithms.

Mohamed Abdel-Basset received the B.Sc., M.Sc., and Ph.D. degrees in information systems and technology from the Faculty of Computers and Informatics, Zagazig University, Egypt. His current research interests are optimization, operations research, data mining, computational intelligence, applied statistics, decision support systems, robust optimization, engineering optimization, multiobjective optimization, swarm intelligence, evolutionary algorithms, and artificial neural networks. He is working on the application of multiobjective and robust meta-heuristic optimization techniques. He is also an/a Editor/reviewer in different international journals and conferences. He has published more than 150 articles in international journals and conference proceedings. He holds the program chair in many conferences in the fields of decision making analysis, big data, optimization, complexity, and the Internet of Things, as well as editorial collaboration in some journals of high impact.