Advances in Data Science and Analytics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
ADVANCES in DATA SCIENCE and ANALYTICS 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. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Implementation Tools for Generating Statistical Consequence Using Data Visualization Techniques
1.1 Introduction
1.2 Literature Review
1.3 Tools in Data Visualization
1.4 Methodology
1.5 Conclusion
References
2 Decision Making and Predictive Analysis for Real Time Data
2.1 Introduction
2.2 Data Analytics
2.3 Predictive Modeling
2.4 Categories of Predictive Models
2.5 Process of Predictive Modeling
2.6 Predictive Analytics Opportunities
2.7 Classification of Predictive Analytics Models
2.8 Predictive Analytics Techniques
2.9 Data Analysis Tools
2.10 Advantages & Disadvantages of Predictive Modeling
2.11 Predictive Analytics Biggest Impact
2.12 Application of Predictive Analytics
2.13 Future Scope of Predictive Modeling
2.14 Conclusion
References
3 Optimizing Water Quality with Data Analytics and Machine Learning
3.1 Introduction
3.2 Related Work
3.3 Data Sources and Collection
3.4 Water Demand Forecasting
3.5 Re-Chlorination Optimization
3.6 Conclusion
Acknowledgements
References
4 Lip Reading Framework using Deep Learning and Machine Learning
4.1 Introduction
4.2 The Emergence and Definition of the Lip-Reading System
4.3 Design and Components of Lip-Reading System
4.4 Lip Reading System Architecture
4.5 Testing
4.6 Problems Encountered During Implementation
4.7 Conclusion
4.8 Future Work
References
5 New Perspective to Management, Economic Growth and Debt Nexus Analysis: Evidence from Indian Economy
5.1 Introduction
5.2 Literature Review
5.4 Methodology and Findings
5.5 Conclusion and Policy Implications
Declarations
Availability of Data and Materials
Competing Interests
Funding
Authors’ Contributions
Acknowledgments
References
6 Data-Driven Delay Analysis with Applications to Railway Networks
6.1 Introduction
6.2 Related Works
6.3 Background Knowledge
6.4 Delay Propagation Model
6.5 Primary Delay Tracing Back
6.6 Evaluation on Dwell Time Improvement Strategy
6.7 Experiments
6.8 Conclusion
References
7 Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co-Occurrence Matrix, and Convolutional Neural Network (CNN)
7.1 Introduction & Purpose of Study
7.2 Literature Review & Motivation
7.3 Proposed Work
7.4 Observation Tables and Figures
7.5 Conclusion
7.6 Future Work
References
8 IoT Technologies for Smart Healthcare
8.1 Introduction
8.2 Literature Review
8.3 Findings
8.4 Case Study: CyberMed as an IoT-Based Smart Health Model
8.5 Discussions
8.6 Future Insights
8.7 Conclusions
References
9 Enhancement of Scalability of SVM Classifiers for Big Data
9.1 Introduction
9.2 Support Vector Machine
9.3 Parallel and Distributed Mechanism
9.4 Distributed Big Data Architecture
9.5 Distributed High Performance Computing
9.6 GPU Based Parallelism
9.7 Parallel and Distributed SVM Algorithms
9.8 Conclusion and Future Research Directions
References
10 Electrical Network-Related Incident Prediction Based on Weather Factors
10.1 Introduction
10.2 Related Work
10.3 Methodology
10.4 Experiments
10.5 Conclusion and Future Work
Acknowledgements
References
11 Green IoT: Environment‑Friendly Approach to IoT
11.1 Introduction
11.2 G-IoT (Green Internet of Things)
11.3 Layered Architecture of G-IoT
11.4 Techniques for Implementation of G-IoT
11.5 Power Saving Methods Based on Components
11.6 Applications of G-IoT
11.7 Challenges and Future Scope
11.8 Case Study
11.9 Conclusion
References
12 Big-Data Analytics: A New Paradigm Shift in Micro Finance Industry
12.1 Introduction
12.2 Reality of Area and Transcendent Difficulties
12.3 Data Analytics in Microfinance
12.4 Opportunities and Risks in Using Data Analytics
12.5 Risk in Utilizing Big Data
12.6 Conclusion
References
13 Big Data Storage and Analysis
13.1 Introduction
13.2 Hadoop as a Solution to Challenges of Big Data
13.3 In-Memory Storage and NoSQL
13.4 Advantages of NoSQL Database
13.5 Conclusion
References
14 A Framework for Analysing Social Media and Digital Data by Applying Machine Learning Techniques for Pandemic Management
14.1 Introduction
14.2 Literature Review
14.3 Understanding Pandemic Analogous to a Disaster
14.4 Application of Machine Learning Techniques at Various Phases of Pandemic Management
14.5 Generalized Framework to Apply Machine Learning Techniques for Pandemic Management
14.6 Conclusion
References
About the Editors
Index
Also of Interest
Check out these other related titles from Scrivener Publishing
Other related titles
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Average ammonia in observations for each reservoir.
Chapter 4
Table 4.1 Literature survey.
Table 4.2 Frames in seconds.
Chapter 5
Table 5.1 Variables and their measurements.
Table 5.2 Descriptive statistics.
Table 5.3 Augmented Dickey-Fuller (ADF) test result.
Table 5.4 Johansen cointegration test results.
Table 5.5 Error correction test results.
Table 5.6 Normalized cointegrating coefficients.
Table 5.7 Granger causality tests analysis.
Chapter 6
Table 6.1 Performance of next-6-station temporal prediction by capturing del...
Table 6.2 Performance of spatial prediction on 10 stations.
Table 6.3 Improved values of trip ‘322B’ Affected by Trip ‘308B’.
Chapter 7
Table 7.1 Comparison between different segmentation techniques [3, 5, 8, 13,...
Table 7.2 Comparison between different compression techniques [24–26, 40].
Table 7.3 Comparison between breast cancer research work done (segmentation ...
Table 7.4 Comparison between breast cancer research work done (compression t...
Chapter 9
Table 9.1 Popular kernel functions and optimization parameters.
Table 9.2 Parallel approach and mechanisms for large data samples.
Table 9.3 SVM algorithms and their advantages & disadvantages.
Table 9.4 Comparison of well-studied multiclass SVM methods.
Chapter 10
Table 10.1 Weather variables and their meanings.
Table 10.2 Confusion matrix for binary classification of incident and normal...
Table 10.3 Confusion matrix for best fold of 10-fold cross validation of bin...
Table 10.4 Confusion matrix for worst fold of 10-fold cross validation of bi...
Table 10.5 Classification results for incident categorization using differen...
Table 10.6 Category-wise classification performance for incident categorizat...
Table 10.7 Multi-class classification performance for top four performing ca...
Table 10.8 Performance for the classification of top four performing categor...
Chapter 11
Table 11.1 Techniques used in ICT [1–5].
Table 11.2 Power saving techniques in various components [1–5].
Chapter 14
Table 14.1 Measures to be considered under the four phases of pandemic.
Table 14.2 Machine learning techniques suitable for mitigation phase.
Table 14.3 Machine learning techniques suitable for the preparedness phase....
Table 14.4 Machine learning techniques suitable for the response phase.
Table 14.5 Machine learning techniques suitable for recovery phase.
List of Illustrations
Chapter 1
Figure 1.1 Stages of Data Science.
Figure 1.2 Google chart dashboard [18].
Figure 1.3 Tableau dashboard [17].
Figure 1.4 Jupyter dashboard [16].
Figure 1.5 Relationship between Time (in hours) & Distance (in miles).
Figure 1.6 Relationship between Time (in hours) & Distance (in miles) (only ...
Figure 1.8 Quantifying data.
Figure 1.9 Relationship between covariance vs. correlation.
Chapter 2
Figure 2.1 Process of Predictive Analytics.
Chapter 3
Figure 3.1 For any time, if all but one of the flows are known, the remainde...
Figure 3.2 Variations in water consumed during a day.
Figure 3.3 Importance of different features in forecasting future water dema...
Figure 3.4 Forecasted cumulative demand over 24-hour horizon for one supply ...
Figure 3.5 Average forecast error measured as average ratio of root mean squ...
Figure 3.6 Average number of points that fall above/inside/below the forecas...
Figure 3.7 The Cl:N ratio and total chlorine decay rate [29].
Figure 3.8 Re-chlorination framework (rectangles are components needed for o...
Figure 3.9 Water plant data in time series. All observations are included.
Figure 3.10 Ammonia data in different times of day (each point is an observa...
Figure 3.11 Travel time estimation for each zone.
Figure 3.12 Decay and re-chlorination.
Figure 3.13 Feature importance.
Figure 3.14 Prediction error for more reservoirs.
Figure 3.15 Explanation of objective function.
Figure 3.16 Balance between upper limit and lower limit when setting differe...
Figure 3.17 Evaluation on one reservoir.
Figure 3.18 Suggested chlorine level (mg/L) on 1, Feb 2014.
Figure 3.19 Performance examination for every 5 minutes.
Figure 3.20 Confidence interval for tap users.
Figure 3.21 Performance on other reservoir zones.
Figure 3.22 Optimization for both reservoir chlorine and ammonia in WFP.
Figure 3.23 Evaluation for optimized reservoir chlorine and ammonia in WFP....
Chapter 4
Figure 4.1 Lip-reading system.
Figure 4.2 Face detection.
Figure 4.3 RGB matrix representation of detected face.
Figure 4.4 Mel spectrogram.
Figure 4.5 System architecture.
Figure 4.6 System flowchart of a lip-reading system.
Chapter 6
Figure 6.1 Delay types under different standards.
Figure 6.2 Delay propagation and impacts.
Figure 6.3 Four scenarios of delay propagation: Self-propagation; Cross-...
Figure 6.4 Relationship between dwell time and average passenger flow on thr...
Figure 6.5 Delay propagation between trains.
Figure 6.6 Delays gradually increase and spread out.
Figure 6.7 Effect of dwell improvement on delay distribution.
Figure 6.8 Effect of dwell improvement on different delay time bins.
Figure 6.9 An example of dwell improvement estimation.
Figure 6.10 Dwell improvement expectation: inputs and outputs.
Figure 6.11 Geographical map of Sydney trains railway network.
Figure 6.12 Location of 10 selected stations.
Figure 6.13 Primary delay tracing down tree generated by our model.
Chapter 7
Figure 7.1 Types of image segmentation.
Figure 7.2 Types of image compression.
Figure 7.3 Proposed work flowchart.
Chapter 9
Figure 9.1 Linear separation of data points into two classes.
Figure 9.2 Parallel and distributed computing of SVMs containing algorithms ...
Figure 9.3 Illustration of MapReduce example to count shapes.
Figure 9.4 Spark model implementation process.
Figure 9.5 Illustration of job operator (map, split, filter, flatmap, merge)...
Figure 9.6 Cascade SVM working model.
Chapter 10
Figure 10.1 Number of samples for fifteen categories of incidents.
Figure 10.2 Feature importance for binary classification of incident and nor...
Figure 10.3 Feature importance for classification of top four performing cat...
Chapter 11
Figure 11.1 Components of an IoT system.
Figure 11.2 Green-Internet of Things (G-IoT).
Figure 11.3 IoT components classification.
Figure 11.4 Pillars of G-IoT.
Figure 11.5 Architecture of G-IoT.
Figure 11.6 Components of G-IoT System.
Chapter 12
Figure 12.1 Multiple data types used in big data.
Chapter 13
Figure 13.1 Common sources of big data generation.
Figure 13.2 Structured data in form of relational table.
Figure 13.3 The Hadoop Ecosystem.
Figure 13.4 Working of a MapReduce program for word count example.
Figure 13.5 Pig architecture.
Figure 13.6 ETL operation in Sqoop.
Figure 13.7 Diagrammatic representation of rack awareness policy.
Chapter 14
Figure 14.1 Pandemic management cycle.
Figure 14.2 Framework to apply machine learning techniques for pandemic mana...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
About the Editors
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Advances in Data Engineering and Machine Learning
Series Editors: 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 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.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Advances in Data Science and Analytics
Concepts and Paradigms
Edited by
M. NiranjanamurthyHemant Kumar Gianey
and
Amir H. Gandomi
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-79188-1
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
Data science is an inter-disciplinary field that uses scientific methods, processes, and algorithms systems to extract knowledge and insights from many structured and unstructured data. Data science is related to data mining, deep learning, and Big Data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Data Analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. This book provides the information related to components of data science, including Machine Learning, Big Data, and Business Intelligence and four types of Analytics, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. The main goal of this book is to explore the concept and solutions for analysis and develop multidisciplinary applications using advances in Data Science and Analytics techniques. This book features innovative research on advances in Data Science and Analytics techniques and applications which put a great impact on human lives. Highlighting the possible data science applications in multiple domains to ease the day to day challenges, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, and students. Topics discussed in this book are implementation tools for generating statistical consequence using data visualization techniques, decision making and Predictive Analysis for real-time data, optimizing water quality with Data Analytics and machine learning, lip reading framework using deep learning and machine learning, a new perspective to economic growth and debt nexus analysis through evidence from the Indian economy, data-driven Delay Analysis with applications to railway networks, a proposed framework to analyze breast cancer in mammogram images using global thresholding, Gray Level Co-Occurrence Matrices And Convolutional Neural Networks (CNN), IoT technologies for smart healthcare, enhancement of scalability of SVM classifiers for Big Data, electrical network-related incidents prediction based on weather factors, Green IoT, an environment-friendly approach to IoT, Big Data Analytics as a new paradigm shift in the micro-finance industry, Big Data storage and analysis, and a framework for analyzing social media and digital data by applying machine learning techniques for pandemic management.
2Decision Making and Predictive Analysis for Real Time Data
Umesh Pratap Singh*
Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow
Abstract
Data Analysis is required for any business either starting a new venture, planning marketing decisions, or going for a shuttle down the business. The statistical probabilities calculated using data analysis help to make the critical decisions over human bias. Before selecting a data analytical tool, it is necessary to take into account the scope of work, economic feasibility, infrastructure constraints, and the final report is prepared. Analytics models aim to understand business planning and operations for future improvements in business models by using systematic data-driven processes which link historical data about the business. Sometimes a with dramatic change in an uncertain world, we need a predictive analytic model to improve business areas. The need for predictive analytics applications comes with better risk management, fraud, system failure, or interaction of customers in any business. Predictive analytics makes artificial business decisions and usage of IT helps to identify the operational tradeoffs that impact the business decisions being simulated. It can reduce technical debt and financial costs included with the decision and understanding the customers. IT’s role is complex for the selection of an architecture that fulfills future demands around manifold analytical data preparation tasks, reducing model building duration and deploying operational model systems. Technology is required with the needs of business analysts, marketing data scientists, and domain experts. Analytical data preparation is different in every business problem-solving model with the help of statisticians or business analysts using a lot of interactive and collaborative effort. The creative ability to look at data from various perspectives and quickly manipulate for finding missing data or variables, eliminating unnecessary data, and bringing unique new data to improve results from the modeling exercise.
Predictive analytics benefits all small companies as well as large companies for making better decisions in uncertain worlds. Predictive analytics are a key strategy for growing successful businesses in the long run. This is the critical concept of analysts to develop model performance monitoring systems for business.
Keywords: Model, business decision, data analytics, performance, innovation, process
2.1 Introduction
Data analytics are required for more worthy utilization of predictive analytics to eliminate hidden patterns and their relationships to visualize and explore the information. Predictive analytics has given another technique for auto-driven computerization and choice administration that has a choice for cutting operational edge. It is the part of information mining with the prediction of future probabilities and patterns and furthermore anticipates risk, division, and associations. With predictive analytics, associations can accomplish growth development and revenues by key measurements. Various models are used in predictive analytics to monitor various boundaries at various stages of the business system.
The aim of business analytics is to help managers understand their organization’s components, market trends, and challenges existing. While estimating stock requirements and recruiting talent, companies are grasping systematic statistical analysis methods for improving effectiveness and earnings.
Predictive analytics is an advanced approach for analyzing the current and previous data for predicting the future by hiring techniques using statistics, artificial intelligence, and machine learning. Organizations can use big data for finding their profit by applying predictive analytics. It helps organizations with a proactive approach that is forward-looking and decision-based on the data [1, 2]. The importance of key market dynamics is emerging as a wide variety of tools and applications for data analytics are available.
The value of data knowledge is growing as a rising organization selects buyer-oriented marketing decisions to help key business shortcoming analytics.
Suppose an e-commerce company employs their retailing company globally via the internet and sells numerous products. Millions of customers browse the company website to search for their needed products. They examine the features, price, and offers related to that product displayed on the website. A lot of products are in demand on a seasonal basis [3, 4].
For example, air-conditioner’s demand increases in summers and geysers’ demand increases in winter. Customers browse for the item requirement in the season. The organization collects the customer searching data on a season-wise product interest with its price [5, 6]. How do offers attract customers to products? What additional products are bought by customers with selected products? The organization will apply analytics after collecting data to identify the customer requirements and then approach the customers through various mediums such as emails and messages. If the customer browses the website again to buy the same product, then the organization will suggest the new products in combination. If a customer starts frequently buying, then the company increases the price and removes the offer for that individual customer. This is an example of one of the many applications of predictive analytics .
2.2 Data Analytics
The classification of data analytics are as follows.
2.2.1 Descriptive Analytics
Descriptive Analytics are used for data illustration. An example of a product is an administration (SaaS) organization that sold 500 licenses in the current year and 200 licenses last year. Descriptive analysis responds to the number of licenses sold in current versus last year.
2.2.2 Diagnostic Analytics
Diagnostic Analytics is the reason for descriptive analytics. Utilizing the past model, the symptomatic analysis takes information a stride further. An information expert can bore down into quarterly programming permit deals and decide deals and promote endeavors inside every district to reference them against deals development [7, 8]. They could likewise check whether a business increment was an aftereffect of high-performing sales reps or rising enthusiasm inside a specific industry.
2.2.3 Predictive Analytics
Predictive Analytics uses techniques, for example, data mining and machine learning to anticipate what may occur straight away. Data analysis can develop predictive models after gathering large amounts of information to achieve an expected outcome. Predictive analytics is different from data mining on the grounds that it spotlights the revelation of the shrouded connections between factors. A SaaS organization can demonstrate authentic deal data against showcasing uses over every area to make a predicted model for future income dependent on marketing expenses.
2.2.4 Prescriptive Analytics
Prescriptive Analytics makes the last stride and provides a suggestion determined by an anticipated result. When a prescient model is set up, it can suggest activities dependent on recorded information, outer information sources, and machine learning calculations [9, 10].
2.3 Predictive Modeling
Predictive modeling is most firmly identified with the predictive analytics classification. It is a cycle that utilizes information to predict results with data models. This technique can be utilized to predict anything from a rating of television and cricket results. This model is also called:
Predictive Analysis
Predictive Analytics
Machine Learning
Predictive Analytics are frequently used by businesses for predictive modeling. Machine learning is additionally particular from predictive modeling that scientists demonstrate and is characterized by the utilization of statistical methods to permit a system for developing predictive models.
