 | Springer_AI_IoT_2020: BOOK TITLE: "AI based IoT Systems" |
| Submission link | https://easychair.org/conferences/?conf=springer-ai-iot-2020 |
| Poster | download |
| Abstract registration deadline | July 31, 2020 |
| Abstract Acceptance / Rejection Notification Date: | August 10, 2020 |
| Submission deadline | October 15, 2020 |
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BOOK TITLE: AI based IoT Systems
PUBLICATIONS: Springer
Editor(s)
Dr. Souvik Pal, Associate Professor, Department of Computer Science & Engineering, Global Institute of Management and Technology, Krishnagar, India
Prof. Debashis De, Professor, Department of Computer Science and Engineering, Director of Computational Science, Maulana Abul Kalam Azad University of Technology, Kolkata, India
Prof. Rajkumar Buyya, Professor, Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia
About the Book
The edited Book “AI based IoT Systems” is intended to discuss the evolution of future generation Technologies through Internet of Things in the scope of Artificial Intelligence. The main focus of this volume is to bring all the related technologies in a single platform, so that Undergraduate and Postgraduate students, Researchers, Academicians, and Industry people can easily understand the AI algorithms, Machine Learning Algorithms, and Learning Analytics in IoT-enabled Technologies.
This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book will take the readers on a journey that begins with understanding the insight paradigm of AI-enabled IoT technologies and how it can be applied in various aspects. This proposed book will help researchers and practitioners to understand the design architecture and AI algorithms through IoT and the state-of-the-art in IoT countermeasures. It provides a comprehensive discussion on Functional Framework and knowledge Hierarchy for IoT, object identification, Intelligent sensors,Learning and Analytics in Intelligent IoT-enabled Systems, CRISP-DM Frame work, RFID technology, wearable sensors, IoT semantics, Knowledge extraction, Applications of Linear Regression, classification, Vector Machines and Artificial Neural Networks for IoT Devices, Bayesian Learning, Decision Trees, Deep learning frameworks, computational Learning Theory, multi-agent systems for IoT-based ecosystem, Machine Learning Algorithms, Nature inspired algorithms, and Computational Intelligence for cloud-based Internet of Things, and Trustworthy Machine Learning for IoT-enabled systems. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects. This edited book aims to provide the concepts of related technologies and novel findings of the researchers through its Chapter Organization.
Submission Guidelines
All Manuscripts must be original and not simultaneously submitted to another journal or conference. All the submission should be made only through Easychair Only.
- Overall similarity (of context) with existing papers should be less than 10%.
- Minimum Number of pages 25 Pages according to Springer format [will be Provided after acceptance]
- Initial Writing Format: 11 point font in Times New Roman with 1.15 Spacing and Default Margins.
- NO Figure and NO Table should be copied from any other paper / Internet. This activity may lead to direct rejection.
- Language clarity must be there in the manuscript and grammatical mistakes must be avoided.
Indexing
All Manuscripts will be submitted for SCOPUS-Indexing.
Important Dates
Abstract registration deadline (250-350 Words): July 02, 2020. July 31, 2020
Abstract Acceptance / Rejection Notification Date: On or before August 10, 2020
Submission deadline: October 15, 2020
List of Topics (But not limited to)
- IoT ecosystem Functional Framework and knowledge Hierarchy
- Foundation of Learning and Analytics in Intelligent IoT-enabled Systems
- Learning system in IoT: training data, concept representation, function approximation.
- Intelligent Object Identification in IoT Devices: Intelligent sensors, Micro Electro Mechanical Systems (MEMS), Object discovery, electronic product codes (EPC) and ubiquitous codes (uCode).
- IoT-enabled M2M Technology and Software-Defined Networking (SDN), RFID Technology
- CRISP-DM Frame work, Statistics and Exploratory Data Analytics for IoT-based environment
- Statistical and computational learning theorem for IoT applications
- Algorithms and architectures for high-performance computation for IoT-enabled framework
- Applications of Linear Regression, classification, and Feature Selection
- Support Vector Machines and Artificial Neural Networks for IoT Devices
- Applications of Bayesian Learning, Decision Trees, clustering IoT-enabled systems
- Deep learning frameworks (architectures, generative models, deep reinforcement learning)
- Applications of Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods)
- Application of computational Learning Theory and Expectation Maximization for IoT-enabled systems
- Game theory, no-regret learning, multi-agent systems for IoT-based ecosystem
- Data Management and analysis in Intelligent IoT devices
- Pricing model and billing systems in Intelligent IoT-based environment
- Integration of machine learning algorithms with mobile computing for cloud-based Internet of Things
- Machine Learning Algorithms, Nature inspired algorithms, and Computational Intelligence for cloud-based Internet of Things
- Quantum Machine Learning, Computational Learning Theory for IoT-based environment
- Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness) for IoT-enabled systems
- Case Studies: Machine Learning Application in IoT
- Smart Irrigation, Crop e-monitoring
- computational biology,
- crowd sourcing,
- Crowd sensing
- Communication and routing protocols
- healthcare, Clinical Decision Support System, neuroscience,
- IoT-enabled Power automation
- climate science
Publication
The edited Book “AI based IoT Systems” will be published in Springer IoT Series