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Machine Learning and Data Science

Fundamentals and Applications
Edited by Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, and Nisheeth Joshi
Series: Advances in Data Engineering and Machine Learning
Copyright: 2022   |   Status: Published
ISBN: 9781119775614  |  Hardcover  |  
245 pages
Price: $225 USD
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One Line Description
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.

Audience
Technical software developers, managers, government authorities, policymakers of organizations or institutions, data scientists, information architects, researchers and academicians working in machine learning and data science, and graduate students looking to build a career in this area

Description
Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.

These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

The outstanding new volume focuses on the latest developments in machine learning and data science, as well as on the synergy between data science and machine learning. This book explores new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science.

The book encompasses all aspects of research and development in ML and DS, including but not limited to data discovery, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics and their applications in the areas of engineering, business and social sciences. It covers a broad spectrum of applications in the community, from industry, government, and academia. Whether for the veteran engineer or scientist, the student, or a manager or other technician working in the field, this volume is a must-have for any library.

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Author / Editor Details
Prateek Agrawal, PhD, completed his BTech in computer science engineering from Uttar Pradesh Technical University, Lucknow, India and MTech from ABV-IIITM, Gwalior, India. He also received his PhD from IKG-Punjab Technical University, Punjab, India. He has more than ten years of research and teaching experience. He has worked as a post-doctoral researcher at the department of ITEC, University of Klagenfurt, Austria and also serves as an associate professor in the school of computer science engineering, Lovely Professional University, India. He has authored more than 50 research papers in various peer reviewed journals and conferences. He is a life member of IET research society. He is a reviewer and editorial member of many journals of high repute. He also served as technical program committee member of many international conferences.

Charu Gupta, PhD, graduated with her BE and MTech in computer science and engineering, with honors. She completed her doctoral degree at the Department of Computer Science, Banasthali Vidyapith, Rajasthan, India. Presently serving as assistant professor at Bhagwan Parshuram Institute of Technology, she has over 10 years of teaching experience. She is a faculty coordinator (Delhi Section) of Free and Open Source Cell (FOSS cell) from the International Centre for Free and Open Source Software (ICFOSS), Govt. of Kerala, India. She is also the faculty co-ordinator of the e-Yantra Lab setup initiative (eLSI) in collaboration with IIT Bombay in the Department of Computer Science and Engineering, BPIT. She also has numerous papers in scientific and scholarly journals to her credit.

Anand Sharma, PhD, received his PhD in engineering from MUST, Lakshmangarh, his MTech from ABV-IIITM, Gwalior and BE from RGPV, Bhopal. He has been working with Mody University of Science and Technology, Lakshmangarh for over 10 years and has more than 14 years of experience of teaching and research. He has pioneered research in the areas of information systems, information security, IoT, knowledge management and machine learning. He is the Vice-Chairman of CSI-Lakshmangarh Chapter and Student Branch Coordinator of CSI-MUST, student branch. He has authored or edited five books and has more than 70 papers in scientific and scholarly journals and conferences. He has organized more than 15 conferences, eminars, and workshops and is serving on the editorial boards of several journals as and is on the program committees of several international conferences.

Vishu Madaan, PhD, received her BTech. and MTech degrees in computer science engineering from Lovely Professional University, Phagwara, India and earned her PhD in computer science from IKG-Punjab Technical University, Punjab. She is an assistant professor with Lovely Professional University and has more than eight years of teaching and research experience. She has authored more than 30 research articles in peer-reviewed conferences and journals. She is also a member of IEEE and is a reviewer for many international conferences and technical journals.

Nisheeth Joshi, PhD, is an associate professor at Banasthali Vidyapith, India. He did his PhD in the area of natural language processing and is the recipient of the prestigious ISTE-U.P. Government National Award for Outstanding Work Done in Specified Areas of Engineering and Technology. He has authored several papers in scientific and technical journals and conferences. He has also been a mentor and consultant to various start-ups working in the areas of cognitive computing, natural language processing, speech processing, multimodal information retrieval and machine translation. He was also a consultant to C-DAC Pune where he developed the evaluation methodology for the Mantra Machine Translation System. This Machine Translation System is being used by Rajya Sabha, the Upper House of Parliament of India and Department of Official Languages, with the Government of India. In addition, he has two technology transfers and four copyrights to his credit.

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Table of Contents
Preface
Book Description
1. Machine Learning: An Introduction to Reinforcement Learning
Sheikh Amir Fayaz, Dr. S Jahangeer Sidiq, Dr. Majid Zaman and Dr. Muheet Ahmed Butt
1.1 Introduction
1.1.1 Motivation
1.1.2 Machine Learning
1.1.3 How Machines Learn
1.1.3.1 Supervised Learning
1.1.3.2 Unsupervised Learning
1.1.3.3 Reinforcement Learning
1.1.4 Analogy
1.1.5 Reinforcement Learning Process
1.1.6 Reinforcement Learning Definitions: Basic Terminologies
1.1.7 Reinforcement Learning Concepts
1.2 Reinforcement Learning Paradigm: Characteristics
1.3 Reinforcement Learning Problem
1.4 Applications of Reinforcement Learning
Conclusion
References
2. Data Analysis Using Machine Learning: An Experimental Study on UFC
Prashant Varshney, Charu Gupta, Palak Girdhar, Anand Mohan, Prateek Agrawal and Vishu Madaan
2.1 Introduction
2.2 Proposed Methodology
2.2.1 Data Extraction: Preliminary
2.2.2 Pre-Processing Dataset
2.3 Experimental Evaluation and Visualization
2.4 Conclusion
References
3. Dawn of Big Data with Hadoop and Machine Learning
Balraj Singh and Harsh Kumar Verma
3.1 Introduction
3.2 Big Data
3.2.1 The Life Cycle of Big Data
3.2.2 Challenges in Big Data
3.2.3 Scaling in Big Data Platforms
3.2.4 Factors to Understand Big Data Platforms and Their Selection Criteria
3.2.5 Current Trends in Big Data
3.2.6 Big Data Use Cases
3.3 Machine Learning
3.3.1 Machine Learning Algorithms
3.4 Hadoop
3.4.1 Components of the Hadoop Ecosystem
3.4.2 Other Important Components of the Hadoop Ecosystem for Machine Learning 3.4.3 Benefits of Hadoop with Machine Learning
3.5 Studies Representing Applications of Machine Learning Techniques with Hadoop
3.6 Conclusion
References
4. Industry 4.0: Smart Manufacturing in Industries - The Future
Dr. K. Bhavana Raj
4.1 Introduction
Challenges or Responses
Shared Infrastructure
Security
Costs or Profitability
Future Proofing
Conclusion
References
5. COVID-19 Curve Exploration Using Time Series Data for India
Apeksha Rustagi, Divyata, Deepali Virmani, Ashok Kumar, Charu Gupta, Prateek Agrawal and Vishu Madaan
5.1 Introduction
5.2 Materials Methods
5.2.1 Data Acquisition
5.2.2 Exploratory Data Analysis (EDA)
5.3 Conclusion and Future Work
References
6. A Case Study on Cluster Based Application Mapping Method for Power Optimization in 2D NoC
Aravindhan Alagarsamy and Sundarakannan Mahilmaran
6.1 Introduction
6.2 Concept Graph Theory and NOC
Definition 1.1
Definition 1.2
Definition 1.3
Definition 1.4
6.3 Related Work
6.3.1 Cluster-Based Mapping with KL Algorithm
6.3.2 Cluster-Based Mapping with Tailor Made Algorithm
6.3.3 Cluster-Based Mapping with Depth First Search (DFS) Algorithm
6.4 Proposed Methodology
6.4.1 Cluster-Based Mapping with FM Algorithm
6.4.2 Calculation of Total Power Consumption
6.4.3 Total Power Calculation by Using Tabu Search
6.5. Experimental Results and Discussion
6.5.1 Total Power Consumption in 2D NoC
6.5.2 Total Power Consumption in 2D NoC
6.5.3 Performance of Tabu Search for Power Optimization with Mesh Topology 6.5.4 Performance of Tabu Search for Power Optimization with Ring Topology
6.5.5 Average Hop Counts for 2D NoC
6.6 Conclusion
Bibliography
7. Healthcare Case Study: COVID19 Detection, Prevention Measures, and Prediction Using Machine Learning & Deep Learning Algorithms
Devesh Kumar Srivastava, Mansi Chouhan and Amit Kumar Sharma
7.1 Introduction
7.2 Literature Review
7.3 Coronavirus (Covid19)
7.3.1 History of Coronavirus
7.3.2 Transmission Stages of COVID19
7.3.3 Restrictions of COVID19
7.3.4 Symptoms of COVID19
7.3.5 Prevention of COVID19
7.3.6 COVID19 Diagnosis and Awareness
7.3.7 Measures to Perform by COVID19 Patients
7.3.8 High-Risk People
7.3.9 Problem Formulation
7.4 Proposed Working Model
7.4.1 Data Selection
7.4.2 Important Symptoms for Prediction
7.4.3 Data Classification
7.4.3.1 Logistic Regression
7.4.3.2 Naïve Bayes
7.4.3.3 Adaboost
7.4.3.4 Random Forest
7.4.3.5 Multilayer Perceptron
7.4.3.6 J48
7.4.3.7 Voted Perceptron
7.5 Experimental Evaluation
7.5.1 Experiment Results
7.5.2 Experiment Analysis
7.6 Conclusion and Future Work
References
8. Analysis and Impact of Climatic Conditions on COVID-19 Using Machine Learning
Prasenjit Das, Shaily Jain, Shankar Shambhu and Chetan Sharma
8.1 Introduction
8.1.1 Types of Coronavirus
8.1.2 Transmission of Virus
8.2 COVID-19
8.3 Experimental Setup
8.4 Proposed Methodology
8.5 Results Discussion
8.6 Conclusion and Future Work
References
9. Application of Hadoop in Data Science
Balraj Singh and Harsh Kumar Verma
9.1 Introduction
9.1.1 Data Science
9.1.1.1 Life Cycle of Data Science Process
9.1.1.2 Applications of Data Science
9.1.2 Big Data
9.1.2.1 Benefits of Big Data
9.1.2.2 Challenges in Big Data
9.1.2.3 Characteristics of Big Data
9.1.2.4 Sources of Big Data
9.2 Hadoop Distributed Processing
9.2.1 Anatomy of the Hadoop Ecosystem
9.2.2 Other Important Components of Hadoop Ecosystem
9.2.3 MapReduce
9.2.4 Need for Hadoop
9.2.5 Applications of Hadoop
9.2.6 Use of Hadoop with Data Science
9.3 Using Hadoop with Data Science 160
9.3.1 Reasons for Hadoop Being a Preferred Choice for Data Science
9.3.2 Studies Using Data Science with Hadoop
9.4 Conclusion
References
10. NetworkingTechnologiesandChallengesforGreenIOT Applications in Urban Climate
Saikat Samanta, Achyuth Sarkar and Aditi Sharma
10.1 Introduction
10.2 Background
10.2.1 Internet of Things
10.3 Green Internet of Things
10.3.1 Green IOT Networking Technologies
10.3.2 Green IOT Applications in Urban Climate
10.3.3 Intelligent Housing
10.3.4 Intelligent Industrial Technology
10.3.5 Intelligent Healthcare
10.3.6 Intelligent Grid
10.3.7 Intelligent Harvesting
10.4 Different Energy-Efficient Implementation of Green IOT
10.5 Recycling Principal for Green IOT
10.6 Green IOT Architecture of Urban Climate
10.7 Challenges of Green IOT in Urban Climate
10.8 Discussion & Future Research Directions
10.9 Conclusion
References
11. Analysis of Human Activity Recognition Algorithms Using Trimmed Video Datasets
Disha G. Deotale, Madhushi Verma, P. Suresh, Divya Srivastava, Manish Kumar and Sunil Kumar Jangir
11.1 Introduction
11.2 Contributions in the Field of Activity Recognition from Video Sequences
11.2.1 Activity Recognition from Trimmed Video Sequences Using Convolutional Neural Networks
11.3 Conclusion
References
12. Solving Direction Sense Based Reasoning Problems Using Natural Language Processing
Vishu Madaan, Komal Sood, Prateek Agrawal, Ashok Kumar, Charu Gupta, Anand Sharma and Awadhesh Kumar Shukla
12.1 Introduction
12.2 Methodology
12.2.1 Phases of NLP
12.3 Description of Position
12.3.1 Distance Relation
12.3.2 Direction Relation
12.3.3 Description of Combined Distance and Direction Relation
12.4 Results and Discussion
12.5 Graphical User Interface
Conclusion
References
13. Drowsiness Detection Using Digital Image Processing
G.Ramesh babu, Chinthagada Naveen Kumar and Maradana Harish
13.1 Introduction
13.2 Literature Review
13.3 Proposed System
13.4 The Dataset
13.5 Working Principle
13.5.1 Face Detection
13.5.2 Drowsiness Detection Approach
13.6 Convolutional Neural Networks
13.6.1 CNN Design for Decisive State of the Eye
13.7 Performance Evaluation
13.7.1 Observations
13.8 Conclusion
References
Index

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