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Data Science and Data Analytics
Opportunities and Challenges

Edited By

Amit Kumar Tyagi



  • Available for pre-order. Item will ship after August 20, 2021
ISBN 9780367628826
August 20, 2021 Forthcoming by Chapman and Hall/CRC
470 Pages 232 B/W Illustrations

 
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Book Description

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. It is the future of Artificial Intelligence (AI) and the necessity of 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, Big Data Analytics/Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, R, etc., are being used to analyse this large amount of data that can be used in predicting the valuable information/ making decisions. Note that structured data can be easily analysed 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 analysing, we face several concerns like complexity, scalability, privacy leaking 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 at cloud).

In summary, this book will cover all the possible areas, applications with arising serious concerns, and challenges towards this emerging area/ field in detail (with a comparative analysis/ taxonomy). This books provides information to its readers

  1. This book gives concept of Data Science, Tools and Algorithms existed for many useful applications
  2. This book provides many challenges and Opportunities in Data Science and Data Analytics, which help researchers in identifying research gaps or problems to continue their research work
  3. All possible areas and uses of data science in this smart era
  4. This book is in written many areas like agriculture, healthcare, Graph mining, Education, Security, etc., for providing a clear understanding to readers.

Academician, Data Scientist, and Stockbrokers from Industry/Business will find this book useful in knowing optimal strategies for enhancing their firm's productivity.

Table of Contents

Section 1. Introduction about Data Science and Data Analytics

Chapter 1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated based approach
Sumika Chauhan, Manmohan Singh, Ashwani Kumar Aggarwal

Chapter 2 IoT Analytics/ Data Science for IoT
T. Perarasi, R. Gayathri, M. Leeban Moses, B. Vinoth

Chapter 3. A model to identify agriculture production using Data Science techniques
D. Anantha Reddy, Sanjay Kumar, Rakesh Tripathi

Chapter 4. Identification and Classification of Paddy Crop Diseases using Big Data Machine Learning Techniques
Anisha P Rodrigues, Joyston Menezes, Roshan Fernandes,Aishwarya, Niranjan N Chiplunkar, Vijaya Padmanabha

Section 2. Algorithms, Methods, Tools for Data Science and Data Analytics

Chapter 5. Crop Models and Decision Support Systems using Machine Learning
B.Vignesh , G.Suganya

Chapter 6. An Ameliorated Methodology to Predict Diabetes Mellitus using Random Forest
Arunakumari B. N, Aman Rai, Shashidhar R

Chapter 7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning
Farhan Hai Khan, Tannistha Pal

Chapter 8. Hybrid Cellular Automata Models For Discrete Dynamical Systems
Sreeya Ghosh, Sumita Basu

Chapter 9. An Efficient Imputation Strategy Based On Adaptive Filter For Large Missing Value Data Sets
S Radhika, A Chandrasekar, Felix Albu

Chapter 10. An Analysis of Derivative based Optimizers on Deep Neural Network Models
Aruna Pavate, Rajesh Bansode

Section 3. Applications of Data Science and Data Analytics

Chapter 11. Wheat Rust Disease Detection using Deep Learning
Sudhir Kumar Mohapatra, Srinivas Prasad, Sarat Chandra Nayak

Chapter 12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary Disease
Sridevi U.K., Sophia S., Boselin Prabhu S.R., Zubair Baig, P.Thamaraiselvi

Chapter 13. A Novel Multimodal risk disease prediction of Coronavirus by using Hierarchical LSTM methods
V. Kakulapati, Kanchipuram BasavaRaju, Appiah Prince, P. Shiva Kalyan

Chapter 14. Analytics in Education: An Educational Analysis Framework for Enhanced Learning Outcomes
Nazura Javed, Paul Anand

Chapter 15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images
Md. Saikat Islam Khana , Pulak Kanti Bhowmicka, Nazrul Islama, Mostofa Kamal Nasira, Jia Uddinb

Chapter 16. Prediction of Acoustic Performance using Machine learning Techniques
Ratnavel Rajalakshmi, S. Jeyanthi, Yuvaraj L, Pradeep M, Jeyakrishna S, Abhishek Krishnaswami

Section 4. Issue and Challenges in Data Science and Data Analytics

Chapter 17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony
Aleksa Cuk, Timea Bezdan, Nebojsa Bacanin, Miodrag Zivkovic, K Venkatachalam, Tarik A. Rashid, Kanchana Devi V

Chapter 18. Algorithmic Trading using Trend Following Strategy: Evidence from Indian Information Technology Stocks
Molla Ramizur Rahman

Chapter 19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement using Twitter Data and News Sentiments
S. Kumar Chandar, Hitesh Punjabi, Mahesh Kumar Sharda, Jehan Murugadhas

Chapter 20. Churn Prediction in Banking Sector
Shreyas Hingmire,Jawwad Khan, Ashutosh Pandey, Aruna Pavate

Chapter 21. Machine and Deep Learning Techniques for Internet of Things based Cloud Systems
Raswitha Bandi , K.Tejaswini

Section 5. Future Research Opportunities towards Data Science and Data Analytics

Chapter 22. Dialect Identification of Bengali Language
Elizabeth Behrman, Arijit Santra, Siladitya Sarkar, Prantik Roy, Ritika Yadav, Soumi Dutta, Arijit Ghosal

Chapter 23. Real Time Security Using Computer Vision
Bijoy Kumar Mandal, Niloy Sarkar

Chapter 24. Data Analytics for Detecting DDoS Attacks in Network Traffic
Ciza Thomas, Rejimol Robinson R R

Chapter 25. Detection of Patterns in Attributed Graph Using Graph Mining
Bapuji Rao

Chapter 26. Analysis and Prediction of the Update of Mobile Android Version
Aparna Mohan, Maheswari. R

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Editor(s)

Biography

Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India.

He earned his PhoD. in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009-2010, and 2012-2013. He was an Assistant Professor and Head -  Research, Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India in 2018-2019. His current research focuses on Machine Learning with Big data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as "AARIN" and "P3- Block" to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems (MCPS). He has published more than 8 patents in the area of Deep Learning, Internet of Things, Cyber Physical Systems and Computer Vision. He was recently awarded best paper award for paper titled "A Novel Feature Extractor Based on the Modified Approach of Histogram of oriented Gradient", ICCSA 2020, Italy (Europe). He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, and Universal Scientific Education and Research Network, CSI and ISTE.