[hpc-announce] CFC for Book - “AI based IoT Systems”, Springer

[Rajkumar Buyya]

     CALL FOR BOOK CHAPTERS

   *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.

  1. *Overall similarity (of context) with existing papers should be less
     than 10%.*
  2. *Minimum Number of pages 25 Pages according to Springer format [will
     be Provided after acceptance]*
  3. *Initial Writing Format: 11 point font in Times New Roman with 1.15
     Spacing and Default Margins.*
  4. *NO Figure and NO Table should be copied from any other paper /
     Internet. This activity may lead to direct rejection.*
  5. *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*

Abstract Acceptance / Rejection Notification Date: * On or before 
July 10, 2020*

Submission deadline: *September 30, 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

  1. Smart Irrigation, Crop e-monitoring
  2. computational biology,
  3. crowd sourcing,
  4. Crowd sensing
  5. Communication and routing protocols
  6. healthcare, Clinical Decision Support System, neuroscience,
  7. IoT-enabled Power automation
  8. climate science


     Publication

The edited Book */“/**AI based IoT Systems**/”/* will be published in 
*Springer IoT Series*


       *Submission
       Link:** https://easychair.org/conferences/?conf=springer-ai-iot-2020*
 
<https://easychair.org/conferences/?conf=springer-ai-iot-2020>


       *Detailed View:https://easychair.org/cfp/AI_IoT_2020
 
<https://easychair.org/cfp/AI_IoT_2020>*


     Editor Contact(s)


       *Dr. Souvik Pal **[Mail Id:* souvikpal22 at gmail.com
       <mailto:souvikpal22 at gmail.com> ]


       *Prof. Debashis De **[Mail Id:* dr.debashis.de at gmail.com
       <mailto:dr.debashis.de at gmail.com> ]


       *Prof. Rajkumar Buyya