| Titre : |
Artificial Intelligence Techniques in IoT Sensor Networks |
| Type de document : |
texte imprimé |
| Auteurs : |
Mohamed Elhoseny, K Shankar, Mohamed Abdel-Basset, Auteur |
| Année de publication : |
2022 |
| Importance : |
221p |
| Présentation : |
B/W ILLUSTRATION |
| Format : |
18X26 CM |
| ISBN/ISSN/EAN : |
978-0-367-68145-6 |
| Note générale : |
Artificial Intelligence Techniques in IoT Sensor Networks
Edited ByMohamed Elhoseny, K Shankar, Mohamed Abdel-Basset |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Artificial Intelligence Techniques in IoT Sensor Networks- applications of AI techniques- Performance Validation- Natural Language Processing |
| Index. décimale : |
006 |
| Résumé : |
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. |
| Note de contenu : |
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
An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system |
| En ligne : |
https://images.routledge.com/common/jackets/crclarge/978100300/9781003007265.jpg |
Artificial Intelligence Techniques in IoT Sensor Networks [texte imprimé] / Mohamed Elhoseny, K Shankar, Mohamed Abdel-Basset, Auteur . - 2022 . - 221p : B/W ILLUSTRATION ; 18X26 CM. ISBN : 978-0-367-68145-6 Artificial Intelligence Techniques in IoT Sensor Networks
Edited ByMohamed Elhoseny, K Shankar, Mohamed Abdel-Basset Langues : Anglais ( eng)
| Mots-clés : |
Artificial Intelligence Techniques in IoT Sensor Networks- applications of AI techniques- Performance Validation- Natural Language Processing |
| Index. décimale : |
006 |
| Résumé : |
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. |
| Note de contenu : |
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
An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system |
| En ligne : |
https://images.routledge.com/common/jackets/crclarge/978100300/9781003007265.jpg |
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