[AISWorld] Last Call for Chapters: Encyclopedia of Data Science and Machine Learning

[John Wang]

Dear Colleagues,

This is the last call. I would like to cordially invite you to consider
contributing your expertise to an extremely important book entitled
Encyclopedia of Data Science and Machine Learning that focuses on the 4th
Industrial Revolution. As Editor, I am actively searching for quality
material that would be of interest to our audience. This is a wonderful
opportunity since contributing authors are generally viewed by others as
experts in the sub-field(s) of their chapter content. You can revise,
update, and expand your previous contribution(s) because the new
encyclopedia covers almost everything.  In addition, you are more than
welcome to forward this message to your coauthors, colleagues, research
community, Ph.D. students, as well as any scholar(s) who might be
interested in participating in this project because we need many
contributors from different subareas/perspectives covering its broad topics
(https://www.igi-global.com/publish/call-for-papers/call-details/5266.
However, no more than three total article submissions (with a maximum of
two in the same category) per author will be considered. Please read the
following details regarding this forthcoming volume. Thanks.

Sincerely,
John Wang, Professor
Montclair State University, USA

Important Dates

*Sept. 10, 2021: Extended Proposal Submission Deadline Sept. 15, 2021:
Notification of Acceptance*
Sept. 30, 2021: Full Chapter Submission
Dec. 6, 2021: Review Results Returned
Feb. 14, 2022: Final Acceptance Notification
Feb. 28, 2022: Final Chapter Submission

*Introduction*
Big Data and Machine Learning (BD&ML) are driving the Fourth Industrial
Revolution, also referred to as Industry 4.0. With the age of Big Data upon
us, we risk drowning in a flood of digital data. Big Data has now become a
critical part of the business world and daily life, as the synthesis and
synergy of Machine Learning and Big Data has enormous potential. BD&ML will
not only maximize the citizens’ wealth, but also promote all society’s
health. The Encyclopedia of Data Science and Machine Learning examines
current, state-of-the-art research in the areas of data science, machine
learning, data mining, optimization, artificial intelligence, statistics,
and the interactions, linkages, and applications of knowledge-based
business with information systems. It provides an international forum for
practitioners, educators, and researchers to advance the knowledge and
practice of all facets of BD&ML, emphasizing emerging theories, principles,
models, processes, and applications to inspire and circulate cutting-edge
findings into research, business, and communities.

*Objective*
Data Scientist was labeled by Harvard Business Review as the sexiest job of
the 21st century (http://www.hbs.edu/faculty/Pages/item.aspx?num=43110) and
has been chosen as the best job in America, three years in a row according
to Glassdoor. In such a new Age of Big Data and ever-evolving environment;
managers, executives, researchers, teachers, and professionals of the
discipline need access to the most current information about the concepts,
issues, trends, and technologies in a brand-new interdisciplinary area. The
Encyclopedia of Data Science and Machine Learning will provide a collection
of short chapters (3,000-5,000 words each), authored by leading experts,
offering an in-depth description of concepts, issues, challenges,
innovations, and opportunities in the field of Big Data and Machine
Learning and their impact on all aspects of modern organizations and
society in general.

*Target Audience*
This comprehensive volume is a critical resource for Big Data and Machine
Learning practitioners and scientists, including industry professionals,
technical managers, and corporate executives. In addition, educators,
scholars, graduate students, and government officers will also find this
book to be useful. Among the perspectives examined include historical
underpinnings, strategic planning and policy, hot job prospects, and future
directions, all of which are focused on the unique benefits and
capabilities found in Industry 4.0.

*Recommended Topics*
https://www.igi-global.com/publish/call-for-papers/call-details/5266

*Submission Procedure*
Researchers and practitioners are invited to submit on or before Sept. 10,
2021, a chapter proposal of 1,000 to 2,000 words clearly explaining the
mission and concerns of his or her proposed chapter via Propose Chapter >
at the bottom of
https://www.igi-global.com/publish/call-for-papers/call-details/5266.
Authors will be notified by Sept. 15, 2021, about the status of their
proposals and sent chapter guidelines. Full chapters are expected to be
submitted by Sept. 30, 2021, and all interested authors must consult the
guidelines for manuscript submissions at
https://www.igi-global.com/publish/contributor-resources/before-you-write/
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.
Note: There are no submission or acceptance fees for manuscripts submitted
to this book publication, Encyclopedia of Data Science and Machine
Learning. All manuscripts are accepted based on a double-blind peer review
editorial process.
All proposals should be submitted through the eEditorial Discovery® online
submission manager.

*Publisher*
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 https://www.igi-global.com. This publication is
anticipated to be released in 2022.

*Inquiries*
For inquiries, please contact the editor: prof.johnwang at gmail.com
John Wang, Professor
Montclair State University, USA