Call for Chapters: Machine Learning Enabled IoT for Smart Applications Across Industries

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Machine Learning Enabled IoT for Smart Applications Across Industries. All manuscripts are accepted based on a double-blind peer review editorial process

Dr. Neha Goel, RKGIT, India
Dr. Ravindra Kumar Yadav, RKGIT, India

Call for Chapters

  • Proposals Submission Deadline: February 1, 2023
  • Full Chapters Due: April 16, 2023
  • Submission Date: April 16, 2023


Machine learning (ML), and Internet of Things (IOT) are the top technologies used by businesses to increase efficiency, productivity, and increase their competitiveness in this fast‐paced digital era transformation. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IOT devices, which require ML to satisfy their working objective. IOT technology has proven as an efficient in solving many real world problems and machine learning algorithms combine with IOT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. This book highlights the importance of Machine learning for IoT’s success and diverse Machine learning powered IoT applications. Machine learning developments in IoT is classified from three perspectives: data, application, and industry. Further, this book will identify and discuss emerging IoT trends, including Internet of Behavior (IoB), pandemic management, edge and fog computing, and connected autonomous vehicles with a primary focus on machine learning to develop futuristic and sustainable solutions. Despite IoT’s ability to transform our present-day societies into smarter and more sustainable ones, it has to overcome a set of challenges, e.g., technological, individual, business, and those related to our societies.


This edited book highlights Machine Learning Enabled IOT for Smart applications. This book covers important research areas in domain of smart homes, warning systems, smart shopping, smart gadgets, smart cities, intelligent roads, health care, fire systems, threat-identification systems, tracking, and surveillance with Machine Learning Enabled IOT.The actual objective of ML in IoT is to bring complete automation by enhancing learning that facilitates intelligence through smarter objects . ML gives IoT-enabled systems the potential to mimic human like decisions after training from the data and further improve their understanding of our surroundings. Machine learning gives a brain to IoT-enabled systems to grasp the insight from data produced by millions of IoT objects. IoT-based ML consists of algorithms that will learn from the colossal amount of data. ML tasks can be seen from IoT perspectives to predict from a treasure trove of data (1) data quality and (2) pattern recognition. Machine learning Algorithms can also be used to enhance data quality. Machine learning Algorithms are used to identify outliers and impute data before training Machine learning Algorithms for prediction.The book addresses the problem and challenges in energy, industry, and healthcare and solutions proposed for Machine Learning Enabled IOT and new algorithms in machine learning and also addresses their accuracy for existing real-time problems.

Target Audience
Primary Audience

Students of Bachelor Programmes in Electronics and Electrical in engineering, science, and technology.Students of Master Programmes in Electronics and Electrical in engineering, science, and technology.Designers and engineers working in the fields of image processing, biomedical sciences, medical instrumentation, etc.Researchers working in the fields of image processing, biomedical sciences, medical instrumentation, etc.Any student interested in starting his/her career in the field of IOT and Machine learning.

Secondary Audience

Text and reference books in college and university libraries,Ready reference for researchers interested in being updated in the domain of IOT, Machine learning for image processing applications,textbook in universities and institutions that offer postgraduate programmes in image processing and IOT, ML technologies.

Recommended Topics

  • Machine Learning enabled IOT
  • Machine learning
  • IOT industry
  • Pattern recognition
  • Feature engineering
  • Future Outlier detection algorithm
  • Data imputation
  • IOT Paradigm concerning data sources
  • IOT Paradigm concerning smart grid
  • IOT Paradigm concerning smart cities
  • IOT Paradigm concerning smart traffic
  • IOT Paradigm concerning smart home
  • IOT Paradigm concerning smart health care system
  • IOT Paradigm concerning smart environmental control
  • IOT Paradigm concerning smart social applications
  • IOT Paradigm concerning industry perspective
  • Edge computing
  • Fog computing
  • lightweight deep learning
  • Challenges in various application using IOT

Submission Procedure

Researchers and practitioners are invited to submit on or before February 1, 2023, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by February 15, 2023 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by April 16, 2023, and all interested authors must consult the guidelines for manuscript submissions at prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

All proposals should be submitted through the eEditorial Discovery® online submission manager.


This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the “Information Science Reference” (formerly Idea Group Reference), “Medical Information Science Reference,” “Business Science Reference,” and “Engineering Science Reference” imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit This publication is anticipated to be released in 2023.

Important Dates

  • February 1, 2023: Proposal Submission Deadline
  • February 15, 2023: Notification of Acceptance
  • April 16, 2023: Full Chapter Submission
  • May 30, 2023: Review Results Returned
  • July 11, 2023: Final Acceptance Notification
  • July 25, 2023: Final Chapter Submission

Dr. Neha Goel


Dr. Ravindra Kumar Yadav


Computer Science and Information Technology; Environmental, Agricultural, and Physical Sciences; Medical, Healthcare, and Life Sciences; Science and Engineering

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