Machine Learning and Data Science: Fundamentals and Applications

✍ Scribed by Prateek Agrawal (editor), Charu Gupta (editor), Anand Sharma (editor), Vishu Madaan (editor), Nisheeth Joshi (editor)

• Publisher: Wiley-Scrivener
• Year: 2022
• Tongue: English
• Leaves: 271
• Edition: 1
• Category: Library

✦ Synopsis

MACHINE LEARNING AND DATA SCIENCE

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.

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.

✦ Table of Contents

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Preface
Book Description
1 Machine Learning: An Introduction to Reinforcement Learning
1.1 Introduction
1.1.1 Motivation
1.1.2 Machine Learning
1.1.3 How Machines Learn
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
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
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
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
5.1 Introduction
5.2 Materials Methods
5.2.1 Data Acquisition
5.2.2 Exploratory Data Analysis (EDA)
5.3 Concl usion and Future Work
References
6 A Case Study on Cluster Based Application Mapping Method for Power Optimization in 2D NoC
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 Performance of Tabu Search for Power Optimization with Mesh Topology
6.5.3 Performance of Tabu Search for Power Optimization with Ring Topology
6.5.4 Average Hop Counts for 2D NoC
6.6 Conclusion
References
7 Healthcare Case Study: COVID19 Detection, Prevention Measures, and Prediction Using Machine Learning & Deep Learning Algorithms
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
7.5 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
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
9.1 Introduction
9.1.1 Data Science
9.1.2 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
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 Networking Technologies and Challenges for Green IOT Applications in Urban Climate
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
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
12.1 Introduction
12.2 Methodology
12.2.1 Phases of NLP
d. Semantic Analysis
e. Pragmatic Analysis
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
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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