Table Of ContentDATA SCIENCE AND DATA
ANALYTICS
OPPORTUNITIES AND CHALLENGES
Edited by
Amit Kumar Tyagi
A Chapman & Hall Book
Data Science and Data
Analytics
Data Science and Data
Analytics
Opportunities and Challenges
Edited by
Amit Kumar Tyagi
School of Computer Science and
Engineering Vellore Institute of Technology
First edition published 2022
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Library of Congress Cataloging-in-Publication Data
Names: Tyagi, Amit Kumar, 1988-editor.
Title: Data science and data analytics: opportunities 1/10 and challenges/edited by Amit Kumar Tyagi (School
of Computer Science and Engineering, Vellore Institute of Technology).
Description: First edition. | Boca Raton: C&H\CRC Press, 2021. | Includes bibliographical references and
index.
Identifiers: LCCN 2021007674 (print) | LCCN 2021007675 (ebook) | ISBN 9780367628826 (hbk) | ISBN
9780367628888 (pbk) | ISBN 9781003111290 (ebk)
Subjects: LCSH: Big data.
Classification: LCC QA76.9.B45 D396 2021 (print) | LCC QA76.9.B45 (ebook) | DDC 005.7‐‐dc23
LC record available at https://lccn.loc.gov/2021007674
LC ebook record available at https://lccn.loc.gov/2021007675
ISBN: 978-0-367-62882-6 (hbk)
ISBN: 978-0-367-62888-8 (pbk)
ISBN: 978-1-003-11129-0 (ebk)
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by MPS Limited, Dehradun
Contents
Preface.......................................................................................................................................................ix
Editors.......................................................................................................................................................xi
Contributors............................................................................................................................................xiii
Section I Introduction about Data Science and Data Analytics
1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning
Integrated Based Approach...........................................................................................................3
Sumika Chauhan, Manmohan Singh, and Ashwani Kumar Aggarwal
2. IoT Analytics/Data Science for IoT............................................................................................19
T. Perarasi, R. Gayathri, M. Leeban Moses, and B. Vinoth
3. A Model to Identify Agriculture Production Using Data Science Techniques.....................33
D. Anantha Reddy, Sanjay Kumar, and Rakesh Tripathi
4. Identification and Classification of Paddy Crop Diseases Using Big Data
Machine Learning Techniques....................................................................................................47
Anisha P. Rodrigues, Joyston Menezes, Roshan Fernandes, Aishwarya,
Niranjan N. Chiplunkar, and Vijaya Padmanabha
Section II Algorithms, Methods, and Tools for Data Science and
Data Analytics
5. Crop Models and Decision Support Systems Using Machine Learning................................67
B. Vignesh and G. Suganya
6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random
Forest..............................................................................................................................................83
Arunakumari B. N., Aman Rai, and Shashidhar R.
7. High Dimensionality Dataset Reduction Methodologies in Applied Machine
Learning..........................................................................................................................................97
Farhan Hai Khan and Tannistha Pal
8. Hybrid Cellular Automata Models for Discrete Dynamical Systems...................................145
Sreeya Ghosh and Sumita Basu
9. An Efficient Imputation Strategy Based on Adaptive Filter for Large
Missing Value Datasets...............................................................................................................161
S. Radhika, A. Chandrasekar, and Felix Albu
10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models................173
Aruna Pavate and Rajesh Bansode
v
vi Contents
Section III Applications of Data Science and Data Analytics
11. Wheat Rust Disease Detection Using Deep Learning.............................................................191
Sudhir Kumar Mohapatra, Srinivas Prasad, and Sarat Chandra Nayak
12. A Novel Data Analytics and Machine Learning Model towards Prediction and
Classification of Chronic Obstructive Pulmonary Disease....................................................203
Sridevi U. K., Sophia S., Boselin Prabhu S.R., Zubair Baig, and P. Thamaraiselvi
13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical
LSTM Methods............................................................................................................................217
V. Kakulapati, BasavaRaju Kachapuram, Appiah Prince, and P. Shiva Kalyan
14. A Tier-based Educational Analytics Framework....................................................................233
Javed Nazura and Paul Anand
15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning
Models with Histopathology Images.........................................................................................249
Saikat Islam Khan, Pulak Kanti Bhowmicka, Nazrul Islama,
Mostofa Kamal Nasira, and Jia Uddin
16. Prediction of Acoustic Performance Using Machine Learning Techniques........................267
Ratnavel Rajalakshmi, S. Jeyanthi, Yuvaraj L., Pradeep M., Jeyakrishna S.,
and Abhishek Krishnaswami
Section IV Issue and Challenges in Data Science and
Data Analytics
17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between
Genetic Algorithm and Artificial Bee Colony..........................................................................279
Aleksa Cuk, Timea Bezdan, Nebojsa Bacanin, Miodrag Zivkovic, K. Venkatachalam,
Tarik A. Rashid, and V. Kanchana Devi
18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian
Information Technology Stocks.................................................................................................293
Molla Ramizur Rahman
19. A Novel Data Science Approach for Business and Decision Making for Prediction
of Stock Market Movement Using Twitter Data and News Sentiments..............................305
S. Kumar Chandar, Hitesh Punjabi, Mahesh Kumar Sharda, and Jehan Murugadhas
20. Churn Prediction in Banking the Sector..................................................................................317
Shreyas Hingmire, Jawwad Khan, Ashutosh Pandey, and Aruna Pavate
21. Machine and Deep Learning Techniques for Internet of Things Based
Cloud Systems..............................................................................................................................331
Raswitha Bandi and K. Tejaswini
Contents vii
Section V Future Research Opportunities towards Data Science and
Data Analytics
22. Dialect Identification of the Bengali Language.......................................................................357
Elizabeth Behrman, Arijit Santra, Siladitya Sarkar, Prantik Roy, Ritika Yadav,
Soumi Dutta, and Arijit Ghosal
23. Real-Time Security Using Computer Vision............................................................................375
Bijoy Kumar Mandal and Niloy Sarkar
24. Data Analytics for Detecting DDoS Attacks in Network Traffic..........................................389
Ciza Thomas and Rejimol Robinson R.R.
25. Detection of Patterns in Attributed Graph Using Graph Mining........................................409
Bapuji Rao
26. Analysis and Prediction of the Update of Mobile Android Version....................................433
Aparna Mohan and R. Maheswari
Index..............................................................................................................................................453
Preface
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to
extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of
Artificial Intelligence (AI) and the necessity of the future to make things easier and more productive. In
simple terms, data science is the discovery of data or uncovering hidden patterns (like complex behaviors,
trends, and inferences) from data. Moreover this, big data analytics/data analytics are the analysis mechanism
used in data science by a data scientist. Several tools like Hadoop, R, etc., are being used to analyze this large
amount of data that can be used in predicting the valuable information/making decisions. Note that structured
data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of
data by 2020) is in unstructured form that requires advanced analytics tools. But while analyzing, we face
several concerns like complexity, scalability, privacy leaks, and trust issues. Data science helps us in
extracting meaningful information (or insights) from the unstructured or complex or large amount of data
(available or stored around us virtually on the cloud). Here, we get a big question: “Where do we fit in data
science in the near future with this large amount of data?” In continuation to this, data is everywhere and
expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used
interchangeably, but they can actually involve different skill sets and complexity of data. Also, several
positions like data scientist (coined in 2008), data analysis, data engineer, etc., are the result of data science.
Hence, this book will cover all possible areas, applications with arising serious concerns, and challenges
towards this emerging area/field in detail (with a comparative analysis/taxonomy).
In this book, five tracks have been created which explain possible topics related to data science and data
analytics. Further, this book provides several possible opportunities for analysis algorithms/statistical analysis
in 21st century (towards data science and data analytics). This book presents several trending technologies in
the current smart era like cloud computing, fog computing, edge computing, blockchain, AI for cyber
security, AI for the cloud, AI for software development, blockchain for the cloud, etc. This book also
discusses several myths about these trending technologies in this smart era. Lastly, several opportunities and
possibilities (a way forward towards future) with current trending technologies are discussed in this book. I
hope that this book will help future readers, researchers, and scientists to get in-depth information regarding
terms such as data science and data analytics
Amit Kumar Tyagi
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