Artificial Intelligence Techniques in IoT Sensor Networks

Dr. Mohamed Elhoseny is an Assistant Professor at the Department of Computer Science, College of Computer & Information Technology, American University in the Emirates (AUE). Dr. Elhoseny is an ACM Distinguished Speaker and IEEE Senior Member. He received his Ph.D. in Computers and Information from Mansoura University/University of North Texas through a joint scientific program. Dr. Elhoseny is the founder and the Editor-in-Chief of IJSSTA journal published by IGI Global. Also, he is an Associate Editor at IEEE Journal of Biomedical and Health Informatics, IEEE Access, Scientific Reports, IEEE Future Directions, Remote Sensing, and International Journal of E-services and Mobile Applications. Moreover, he served as the co-chair, the publication chair, the program chair, and a track chair for several international conferences published by recognized publishers such as IEEE and Springer. Dr. Elhoseny is the Editor-in-Chief of the Studies in Distributed Intelligence Springer Book Series, the Editor-in-Chief of The Sensors Communication for Urban Intelligence CRC Press-Taylor& Francis Book Series, and the Editor-in-Chief of The Distributed Sensing and Intelligent Systems CRC Press-Taylor& Francis Book Series.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.

• PrefaceChapter 1Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images – An Artificial Intelligence Based IoT Implementation for Teleradiology Network1.1 Introduction1.2 Proposed Methodology1.2.1 Fuzzy C Means Clustering1.3 Results and Discussion1.4 ConclusionReferencesChapter 2Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning2.1. Introduction2.2. Related works2.3. Proposed Method2.3.1. Package Partitioning2.3.2. Planning of delivery path using HFMPSO algorithm2.3.3. Inserting Pickup Packages2.4. Experimental Validation2.4.1. Performance analysis under varying package count2.4.2. Performance analysis under varying vehicle capacities2.4.3. Computation Time (CT) analysis2.5. ConclusionReferencesChapter 3Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things3.1. Introduction3.2. Related works3.3. The Proposed Method3.3.1. Hadoop Ecosystem3.3.2. BOA based FS process3.3.3. GBT based Classification3.4. Experimental Analysis3.4.1. FS Results analysis3.4.2. Classification Results Analysis3.4.3. Energy Consumption Analysis3.4.4. Throughput Analysis3.5. ConclusionReferencesChapter 4An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system4. 1. Introduction4. 2. Background Information4. 2.1. Clustering objective4. 2. 2. Clustering characteristics4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR) protocol4. 3. 1. Fuzzy based Clustering process4. 3. 2. Data aggregation process4. 4. Performance Validation4. 5. ConclusionReferencesChapter 5Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique5. 1. Introduction5. 2. Review of Literatures5. 3. Smart Home- Cloud Backend Services5. 3.1 Internet of Things (IoT)5. 4. Our Proposed Approach5. 4.1 Natural Language Processing Services (NLPS)5. 4. 2 Pipeline Structure for NLPS5. 4. 3 Clustering Model5. 5. Results and analysis5. 6. ConclusionReferencesChapter 6Metaheuristic based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks6. 1. Introduction6. 2. Proposed Methodology6. 2. 1. Deflate based Compression Model6. 2. 2. SMO-KELM based Diagnosis Model6. 3. Experimental results and validation6. 4. ConclusionReferencesChapter 7Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT Based Cloud Environment7. 1. Introduction7. 2. The Proposed Model7. 2.1. SMOTE Model7. 2.2. FSVM based Classification Model7. 3. Simulation Results and Discussion7. 4. ConclusionReferencesChapter 8Energy Efficient Unequal Clustering Algorithm using Hybridization of Social Spider with Krill Herd in IoT Assisted Wireless Sensor Networks8. 1. Introduction8. 2. Research Background8. 3. Literature survey8. 4. The proposed SS-KH algorithm8. 4. 1. SS based TCH selection8. 4. 2. KH based FCH algorithm8. 5. Experimental validation8. 5. 1 Implementation setup8. 5. 2. Performance analysis8. 6. ConclusionReferencesChapter 9IoT Sensor Networks with 5G Enabled Faster RCNN Based Generative Adversarial Network Model for Face Sketch Synthesis9. 1. Introduction9. 2. The Proposed FRCNN-GAN Model9. 2.1. Data Collection9. 2.2. Faster R-CNN based Face Recognition9. 2.3. GAN based Synthesis Process9. 3. Performance Validation9. 4. ConclusionReferencesChapter 10Artificial Intelligence based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-based Internet of Things10. 1. Introduction10. 2. Literature review10. 3. Proposed Methodology10. 3.1. Preprocessing10. 3.2. Feature extraction10. 3.3. Feature selection using ranking method10. 3.4. Cyberbully detection10. 3.5. Dataset Description10. 4. Result and discussion10. 4.1. Evaluation Metrics10. 4.2. Comparative analysis10. 5. ConclusionReferencesChapter 11An Energy Efficient Quasi Oppositional Krill Herd Algorithm based Clustering Protocol for Internet of Things Sensor Networks11. 1. Introduction11. 2. The Proposed Clustering algorithm11. 3. Performance Validation11. 4. ConclusionReferencesChapter 12An effective Social Internet of Things (SIoT) Model for Malicious node detection in wireless sensor networks12. 1. Introduction12. 2. Review of Recent Kinds of literature12. 3. Network Model: SIoT12. 3.1 Malicious Attacker Model in SIoT12. 4. Proposed MN in SIoT System12. 4.1 Trust based Grouping in SIoT network12. 4.2 Exponential Kernel Model for MN detection12. 4.3.1 Example of Proposed Detection System12. 4.4 Detection Model12. 5. Results and analysis12. 6. ConclusionReferencesChapter 13IoT Based Automated Skin Lesion Detection and Classification using Grey Wolf Optimization with Deep Neural Network13. 1. Introduction13. 2. The Proposed GWO-DNN Model13. 2.1. Feature Extraction13. 2.2. DNN based classification13. 3. Experimental Validation13. 4. ConclusionReferencesIndex