Data Science in Engineering and Management: Applications, New Developments, and Future Trends

✍ Scribed by Zdzislaw Polkowski (editor), Sambit Kumar Mishra (editor), Julian Vasilev (editor)

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 161
• Edition: 1
• Category: Library

✦ Synopsis

This book brings insight into Data Science and offers applications and implementation strategies. It includes recent developments and future trends and covers the concept of Data Science along with its origin. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and predictive analysis.

Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origin, as well as presents recent trends, highlights future developments, along with discussing problems, and offering solutions. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. It provides an overview of recent developments and applications on data linked to engineering and management perspectives and also covers how data scientists, analyst and program managers that are interested in productivity and improving their business can do so by incorporating a data science workflow effectively.

This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
1 Concepts for Effective Mobile Device Management in an Enterprise Environment
1.1 Introduction
1.2 Review of the Literature
1.3 Problem Statement
1.4 Methodology
1.5 Experimental Analysis
1.6 Results Obtained From Experimentation
1.7 Discussion and Future Directions
1.8 Conclusion
References
2 Prioritization of Informational Factors and Query Intensification Using a Meta-Heuristic Approach
2.1 Introduction
2.2 Review of Literature
2.3 Schema Update and Modification With Heterogeneity
2.4 Allocation of Dynamically Linked Queries
2.5 Significance of Join Ordering Algorithms
2.5.1 Algorithm: Implementation Using the Iterative Approach
2.5.2 Intensification of Queries in Databases
2.6 Implementation of a Meta-Heuristic Approach
2.6.1 Application of Firefly Algorithms
2.6.2 Pseudo-Code for Query Intensification Using the Firefly Approach
2.6.3 Algorithm: Implementation Using the Firefly Approach
2.6.4 Query Response Time and Linkage to Query Intensification
2.7 Discussion and Future Directions
2.8 Conclusion
References
3 Estimation and Potential Evaluation of Data Linked to Virtual Machines Using a Computational Approach
3.1 Introduction
3.2 Review of Literature
3.3 Implementation Using Particle Swarm Optimization
3.4 Accumulation of Materialized Data By Selecting and Implementing Particle Swarm Optimization
3.4.1 Procedure for Importing and Accessing Data
3.4.2 Algorithm to Implement Data Using PSO
3.4.3 Algorithm to Initialize Swarm Variables to Update Particle Position
3.5 Experimental Analysis
3.6 Discussion and Future Directions
3.7 Conclusion
References
4 Potential Applications of Blockchain Technology in the Construction Sector
4.1 Introduction
4.2 Review of Literature
4.3 Overview of Blockchain in Construction
4.4 Potential Applications of Blockchain Technology in Construction
4.4.1 Smart Contracts
4.4.2 Combining Smart Contracts to Create a Decentralized Autonomous Organization
4.4.3 Building Information Modeling
4.4.4 Decentralized Network Management of Devices
4.4.5 Rationalization of Financing and Payments
4.4.6 Compliance Simplification
4.4.7 Supply Chain Management (Origin and Traceability)
4.4.8 3D Printing of New Construction Parts
4.5 Conclusion
References
5 Artificial Neural Network Applications in Social Media Activities: Impact On Depression During the COVID-19 Pandemic
5.1 Introduction
5.2 Methods
5.2.1 Data Collection
5.2.2 Data Analysis
5.3 Results
5.3.1 Regression Model
5.3.2 Classification Models
5.4 Discussion
Bibliography
6 Analysis of CCT Through ANFIS for a Grid-Connected SPV System
6.1 Introduction
6.2 Fuzzy Control Techniques
6.2.1 Proposed Control Strategies
6.3 Design of THE Fuzzy Logic Controller for THE Inner Current Control Loop
6.3.1 Design of the Fuzzy Logic Controller for the Outer Voltage Control Loop
6.3.2 Design of the ANFIS Controller
6.4 Analysis of Results
6.5 Conclusion
Bibliography
7 Frequency Analysis of Human Brain Response to Sudarshan Kriya Meditation
7.1 Introduction
7.1.1 Brain State/Human State of Mind
7.2 Levels of Consciousness
7.3 Discussion of Techniques Available to Record the Human Brain Response
7.4 Objective
7.5 Literature Survey
7.6 Contributory Work
7.7 Experimental Setup
7.8 Conclusion
7.9 Future Work
Acknowledgments
References
8 Coordinated Control Action Between a DFIG-Grid Interconnected System Using a PI-Based SVM Controller
8.1 Introduction
8.2 Problem Formulation and Solution
8.3 Analysis of Results
8.4 Conclusion
Acknowledgment
Bibliography
9 Acceptance of New Schools in Semi-Urban Areas of India: An Application of Data Mining
9.1 Introduction
9.2 Literature Review
9.3 Cluster Analysis
9.4 Proposed Model
9.5 Information Gain
9.6 Experimental Setup
9.6.1 Pseudo-Code
9.6.2 Benefits of C4.5 Compared With Other Existing Decision Tree Systems
9.7 Results and Analysis
9.8 Conclusion and Future Scope
Bibliography
10 Two-Phase Natural Convection of Dusty Fluid Boundary Layer Flow Over a Vertical Plate
10.1 Introduction
10.2 Mathematical Formulation
10.3 Method of Solution
10.4 Results and Discussion
References
11 An Interactive Injection Mold Design With CAE and Moldflow Analysis for Plastic Components
11.1 Introduction
11.2 Injection Molding
11.3 Injection Mold Design
11.4 Moldflow Analysis
11.5 Results and Discussion
11.6 Conclusion
References
12 Study of Collaborative Filtering-Based Personalized Recommendations Quality, Relevance, and Timing Effect On Users’ ...
12.1 Introduction
12.2 Related Work
12.2.1 Personalized Recommendations On Amazon.in Website
12.3 Research Framework and Hypotheses
12.3.1 Personalized Recommendations, Privacy Concerns
12.3.2 Personalized Recommendations and Trust
12.3.3 Privacy Concerns and Trust
12.3.4 Privacy Concerns and Purchase Intentions
12.3.5 Trust and Purchase Intentions
12.4 Research Method
12.4.1 Data Collection and Sampling
12.4.2 Measurement Model
12.4.2.1 Structural Equation Modeling
12.5 Results and Discussion
12.6 Conclusions and Future Scope of Research
References
Index