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Series: Advances in Data Engineering and Machine Learning

Series Editor(s): Niranjanamurthy M, PhD, Juanying XIE, PhD, and Ramiz Aliguliyev, PhD

Scope: Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.

It is important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases. Data Engineering Contains DevOps, Data Science, and Machine Learning Engineering. DevOps (development and operations) is an enterprise software development phrase used to mean a type of agile relationship between development and IT operations. The goal of DevOps is to change and improve the relationship by advocating better communication and collaboration between these two business units. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Machine learning engineering is the process of using software engineering principles, and analytical and data science knowledge, and combining both of those in order to take an ML model that’s created and making it available for use by the product or the consumers. “Advances in Data Engineering and Machine Learning Engineering” will reach a wide audience including data scientists, engineers, industry, researchers and students working in the field of Data Engineering and Machine Learning Engineering.

Potential Book Titles:

1. Advances in Data Engineering and Machine Learning Engineering: Concepts and Applications
2. Supervised and Unsupervised data engineering for Medical data.
3. Intelligent applications using DevOps: Analysis and Implementation
4. Machine Learning Engineering for Classification and Regression Techniques
5. Advances in Clustering Techniques and Backend engineering: Concept, Paradigm

The series will publish monographs, edited books, handbooks and reference books.

About the Series Editor(s):
Dr. Niranjanamurthy M, Assistant Professor, Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Yelahanka, Bengalore, India. He did his Ph.D. in Computer Science at JJTU, Rajasthan (2016), MPhil-Computer Science at VMU, Salem (2009), Masters in Computer Applications at Visvesvaraiah Technological University, Belgaum, Karnataka (2007). BCA from Kuvempu University 2004 with University 5th Rank. He has 10* years of teaching experience and 2 years of industry experience as a Software Engineer. Published 4 books in Scholars Press Germany and One book under process in CRC press. He has published 54 research papers in various National / International Conferences and Reputed International Journals. Filed 10 Patents in that 2 granted. Currently he is guiding four Ph.D. research scholars in the area of Data Science, Edge Computing, ML, and Networking. He is working as a reviewer in 22 International Journals. Two times got Best Research journal reviewer award. Got Young Researcher award- Computer Science Engineering - Global Outreach Education Awards 2018. Worked as National/ International Ph.D. examiner. He was conducted various National Level workshops and Delivered Lecture. Conducted National and International Conferences. Member of Various Societies IAENG, INSC etc. areas of interest are Data Science, ML, Edge Computing, E-Commerce and M-Commerce related to Industry Internal tool enhancement, Software Testing, Software Engineering, Web Services, Web-Technologies, Cloud Computing, Big data analytics, Networking.

Dr. Juanying Xie is a Prof. and a PhD supervisor at School of Computer Science in Shaanxi Normal University, Xi’an, PR China. She got her PhD & Master degrees from Xidian University in 2012 & 2004, respectively, and majored in Signal and Information Processing & the Computer Application Technology, respectively. She got her BS degree from Shaanxi Normal University in 1993, majored in Computer Science. Since then she was remained as a faculty in Shaanxi Normal University, being as a lecturer, associate professor and professor successively, and has been teaching “data structures” to undergraduate students, and “machine learning” and “data mining” to master students. She did cooperative research with prof. Xiahui Liu in Brunel University as an academic visitor from 2010 to 2011. Her research interests include machine learning, data mining and biomedical data analysis. She has published more than 60 academic papers and 2 books, and got 4 patents etc. In 2019 she was awarded the second level prize of natural science and third level prize of innovation work by Shaanxi province. She is directing a project of National Science foundation of China, and has finished several other projects. Furthermore, she was awarded the hot paper prize by Scientia Sinica (Informationis) in 2018, and two highly cited paper prizes from Frontrunner 5000 in China in 2018 and 2012, respectively.
She is an associate editor of “Health Information Science and Systems”, and a member of editorial board of “The natural science edition of the Journal of Shaanxi Normal University”, and “Computer Engineering & Software”, etc. In addition, she is s senior member of CCF (China Computer Federation), and a member of CAAI (Chinese Association for Artificial Intelligence), and the member of several academic committees, such as Machine learning, Artificial intelligence and pattern recognition, Knowledge engineering and distributed intelligence, etc. At the same time she is the deputy director of Shaanxi education committee of Agricultural labor party in China.

Ramiz Aliguliyev is a Head of department at the Institute of Information Technology of Azerbaijan National Academy of Sciences (ANAS). He received a master degree in Applied Mathematics from Baku State University, Azerbaijan in 1983, the Candidate of Sciences (Ph.D.) degree in Differential Equations from the Institute of Mathematics and Mechanics of ANAS in 2002 and the Doctor of Sciences degree in Information Technology from the Institute of Control Systems of ANAS in 2010. Ramiz Aliguilyev is a corresponding member of ANAS. His main research interests are in the area of data mining, big data analytics, text mining, evolutionary computation and scientometrics. Dr. Aliguliyev has published more than 200 research papers. He has won five Science Development Foundation under the President of the Republic of Azerbaijan and Science Fund of the State Oil Company of Azerbaijan Republic grants to conduct research in big data analytics and its applications.

Submission to the series:
Please submit all book proposals to:

Dr. Niranjanamurthy M
Ph.D.-Computer Science
Department of Artificial Intelligence and Machine Learning
BMS Institute of Technology and Management
Yelahanka, Bengalore, Karnataka-560064. INDIA
Ph: +91-9886265115
Email: niruhsd@gmail.com

Dr. Juanying XIE
Prof, PhD
School of Computer Science
Shaanxi Normal University
199 South Chang'an Road
Xi'an, 710062
PR China
Email: xiejuany@snnu.edu.cn;
juan_xie@icloud.com;
juanyingxie@gmail.com
Tel: +86 13088965815
Office: 2418 WenJin Building, Chang'an School Zone, Shaanxi Normal University

Dr. Ramiz Aliguliyev
Doctor of Sciences
Head of Department
Institute of Information Technology of ANAS
9A, B.Vahabzade Street
AZ1141 Baku, Azerbaijan
E-Mail : r.aliguliyev@gmail.com,
a.ramiz@science.az

Published and Forthcoming Titles

Wireless Communication Security

Machine Learning and Data Science: Fundamentals and Applications.

Security Issues and Privacy Concerns in Industry 4.0 Applications

 		Data Engineering and Data Science
Concepts and Applications
Edited by Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, and M. Niranjanamurthy
Copyright: 2024   |   Status: Published   |   Hardcover
 
Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one stop shop” for the concepts and applications of data science and engineering for data scientists across many industries.


 		Supervised and Unsupervised Data Engineering for Multimedia Data
Edited by Suman Kumar Swarnkar, J. P. Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore, and Tien Anh Tran
Copyright: 2024   |   Status: Published   |   Hardcover
 
Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency.


 		Medical Imaging
Edited by H. S. Sanjay, and M. Niranjanamurthy
Copyright: 2023   |   Status: Published   |   Hardcover
 
Written and edited by a team of experts in the field, this is the most comprehensive and up-to-date study of and reference for the practical applications of medical imaging for engineers, scientists, students, and medical professionals.



 		Advances in Data Science and Analytics
Edited by M. Niranjanamurthy, Hemant Kumar Gianey, and Amir H. Gandomi
Copyright: 2023   |   Status: Published   |   Hardcover
 
Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student, focusing on machining learning, big data, business intelligence, and analytics.



 		Wireless Communication Security
Mobile and Network Security Protocols
Edited by Manju Khari, Manisha Bharti, and M. Niranjanamurthy
Copyright: 2023   |   Status: Published   |   Hardcover
 
Presenting the concepts and advances of wireless communication security, this volume, written and edited by a global team of experts, also goes into the practical applications for the engineer, student, and other industry professionals.


 		Artificial intelligence and Data Mining in Security Frameworks
Edited by Neeraj Bhargava, Ritu Bhargava, Pramod Singh Rathore, and Rashmi Agrawal
Copyright: 2022   |   Status: Published   |   Hardcover
 
Written and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts.


 		Machine Learning and Data Science
Fundamentals and Applications
Edited by Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, and Nisheeth Joshi
Copyright: 2022   |   Status: Published   |   Hardcover
 
Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.


 		Security Issues and Privacy Concerns in Industry 4.0 Applications
Edited by Shibin David, R. S. Anand, V. Jeyakrishnan, and M. Niranjanamurthy
Copyright: 2021   |   Status: Published   |   Hardcover
 
Written and edited by a team of international experts, this is the most comprehensive and up-to-date coverage of the security and privacy issues surrounding Industry 4.0 applications, a must-have for any library.