Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0
- Length: 382 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-10-01
- ISBN-10: 0367676273
- ISBN-13: 9780367676278
- Sales Rank: #0 (See Top 100 Books)
Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems.
This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era.
This book will be useful for the research community, start-up entrepreneurs, academicians, and data centered industries and professors that are interested in exploring innovations in varied applications and areas of data science.
Cover Half Title Series Page Title Page Copyright Page Contents Editors Contributors 1. Quantum Computing: Computational Excellence for Society 5.0 1.1 Introduction 1.2 Quantum Computing Fundamentals 1.2.1 Key Concepts 1.2.2 Hardware, Software, Alorithms, and Workflow 1.3 Quantum Computing Needs and Service Industry Applications 1.3.1 Business Needs and Concerns 1.3.2 Decision Problem Framing and Computation 1.3.3 The Range of Business Problem Areas That Can Be Addressed 1.3.4 The Unique Role of Quantum Computing in Financial Services Applications 1.4 Application Framework 1.4.1 Algorithm Design 1.4.2 Software Development 1.4.3 Hardware for Quantum Computing 1.4.4 Integration with Other IT Systems in the Firm 1.5 Case: Implementing a Quantum Neural Network for Credit Risk 1.5.1 Credit Risk Assessment 1.5.2 Algorithm Design for a Quantum Neural Network (QNN) 1.5.3 Software Design for QNN 1.5.4 Hardware for Quantum Credit Scoring 1.5.5 Issues for Moving from a Stand-Alone to an Integrated System 1.6 Conclusion Acknowledgments Notes References Appendix A: Glossary of Terms 2. Prediction Models for Accurate Data Analysis: Innovations in Data Science 2.1 Introduction 2.1.1 Overview of Chapter 2.2 Multi Classifier System 2.2.1 Philosophy 2.2.2 History 2.2.3 Need for Multi Classifier System 2.2.4 Distinctive Features of Multi Classifier System 2.2.5 Working of Multi Classifier System 2.3 Ensemble Methods 2.3.1 Comparison Among Ensemble Methods 2.4 Design of Multi Classifier System 2.5 Combination Techniques 2.6 The Topologies for Multi Classifier System 2.7 Ensemble Diversity 2.8 Advantages and Disadvantages of MCS 2.9 Applications of Ensemble 2.10 Emerging Areas 2.11 Challenges in Building Multi Classifier System 2.12 Issues and Challenges Related to Society 5.0 2.13 Conclusion and Future Work References 3. Software Engineering Paradigm for Real-Time Accurate Decision Making for Code Smell Prioritization 3.1 Introduction 3.2 Literature Survey 3.3 Proposed Methodology 3.3.1 Code Smells Detection 3.1.1.1 Feature Envy Code Smell 3.1.1.2 GOD Class Code Smell 3.1.1.3 Long Method Code Smell 3.1.1.4 Long Parameter List Code Smell 3.1.1.5 Refused Bequest Code Smell 3.1.1.6 Shotgun Surgery Code Smell 3.1.1.7 Duplicated Code Smell 3.3.2 Code Smells Prioritization 3.3.3 Refactoring and Quality Improvement 3.4 Experimentation and Results 3.4.1 Experimental Planning and Setup 3.4.2 Research Questions and Evaluation Method 3.4.3 Results Analysis and Interpretations 3.5 Conclusion and Future Work References 4. Evaluating Machine Learning Capabilities for Predicting Joining Behavior of Freshmen Students Enrolled at Institutes of Higher Education: Case Study from a Novel Problem Domain 4.1 Introduction 4.2 Methods and Materials 4.2.1 Demography of the Subjects Used 4.2.2 Novelty and Modifications in Preparing the Data Set 4.2.3 Experiments Designed 4.2.4 Classification Algorithms and Performance Evaluation 4.2.5 Interpretability Techniques 4.3 Proposed Mathematical Framework 4.4 Results and Discussion 4.4.1 Comparison of Models Using Performance Metrics 4.4.2 Interpreting Model Behavior 4.4.3 Year-wise Significant Features 4.5 Conclusion References 5. Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis 5.1 Introduction 5.1.1 Digital Image Processing 5.1.2 Fundamental Key Stages in Digital Image Processing 5.2 Literature Review 5.3 Proposed Methodology 5.3.1 Data Collections 5.3.2 Preprocessing - Image Enhancement 5.3.3 Intensity Transformation Function 5.3.4 Segmentation 5.3.5 Feature Extraction 5.4 Role of Knowledge Management and Effective Information Extraction in Image Processing 5.5 Knowledge Management in Case-Based Reasoning Methodology on Healthcare Handling 5.6 OBIA (Object-Based Image Analysis) 5.7 Results and Discussion 5.7.1 Image Segmentation 5.7.2 Feature Extraction 5.8 Conclusion References 6. Recreating Efficient Framework for Resource-Constrained Environment: HR Analytics and Its Trends for Society 5.0 6.1 Introduction 6.2 Literature Review 6.3 Conceptualizing HR Analytics 6.3.1 Relevance of HR Analytics 6.3.2 Benefits of Using HR Analytics 6.4 Applications of HR Analytics for Industry 5.0 6.4.1 Managing the HR Through Analytics: A Case Study of Google 6.4.2 Employee Recruitment: A Case Study of JP Morgan and Rentokil Initial 6.4.3 Employee Retention: A Case Study of HP, IBM, and WIPRO 6.4.4 Employee Engagement: A Case Study of Clarks and Shell 6.4.5 Compensation and Benefits: A Case Study of Clarks 6.4.6 Employee Training 6.5 Recent Trends in HR Analytics 6.5.1 R Programming Language 6.5.2 Python 6.5.3 Business Intelligence 6.6 Challenges of Implementing Human Resource Analytics 6.7 HR Analytics Framework: The Way Forward 6.7.1 Developing Leaders 6.7.2 Ensuring Requisite Skill Sets 6.7.3 Focussing on Clarity of Vision and Mission of Adopting HR Analytics 6.7.4 Understanding and Connecting with the Business Strategy 6.7.5 Collaborating with Stakeholders 6.8 Discussion 6.9 Conclusion References 7. Integration of Internet of Things (IoT) in Health Care Industry: An Overview of Benefits, Challenges, and Applications 7.1 Introduction 7.1.1 Elements of IoT 7.1.2 Characteristics of IoT 7.2 Architecture of IoT in Healthcare 7.3 Advantages of IoT in Healthcare 7.4 IoT Healthcare Security Challenges 7.5 Design Considerations 7.6 Applications of IoT in Healthcare 7.7 IoT Use Cases 7.8 Conclusion References 8. Cloud, Edge, and Fog Computing: Trends and Case Studies 8.1 Introduction 8.2 Overview of the Multi-Tenancy Cloud Service Models 8.3 Engineering of Cloud Services 8.3.1 Static Binding Variation Techniques 8.3.2 Dynamic Binding Variation Techniques 8.4 Packaging of Cloud Services 8.4.1 Service Level 8.4.2 Tenant Level 8.5 Hosting Cloud Services 8.5.1 Shared Instance 8.5.2 Dedicated Instance 8.6 Discussion on Architecture Choices 8.6.1 Cloud Service Variability 8.6.2 Costs and Benefits of Designing Service Variability 8.6.3 Cloud Service Architecture Models 8.7 Variability Scenarios - Service Provider Perspective 8.7.1 Cost Considerations 8.7.2 Revenue Considerations 8.7.3 Tenant Profile 8.7.4 Market Share Considerations 8.7.5 Service Isolation 8.7.6 Budget Constraints 8.8 Cloud Service Profitability Model 8.8.1 Service Tenants 8.8.2 Range of Service Variability 8.8.3 Service Costs 8.8.4 Service Revenue 8.8.5 Service Profits 8.9 Analyzing Service Profitability Based on Concept Map 8.9.1 Overview of Simulation Process 8.9.2 Simulating with Constraints 8.9.3 Tooling for the Simulation Process 8.10 Experiments and Evaluations 8.10.1 Experiments Overview 8.10.2 Performing Simulations 8.10.3 Budget Constraint Scenario 8.11 Related Works 8.12 Threats to Validity 8.13 Conclusion References 9. A Paradigm Shift for Computational Excellence from Traditional Machine Learning to Modern Deep Learning-Based Image Steganalysis 9.1 Introduction 9.1.1 Motivation 9.1.2 Key Contributions 9.2 Universal Image Steganalysis Preliminaries 9.2.1 The Conceptualization 9.2.2 Rationale for Using CNN Models 9.2.2.1 Image Pre-Processing Layer 9.2.2.2 Convolutional Layer 9.2.2.3 Non Linear Mapping Layer 9.2.2.4 Pooling Layer 9.2.2.5 Batch Normalization 9.2.2.6 Classification Layer 9.2.2.7 Optimization of Gradient Descent 9.3 Recent Advancements 9.4 Open Research Issues and Opportunities 9.5 Conclusion References 10. Feature Engineering for Presentation Attack Detection in Face Recognition: A Paradigm Shift from Conventional to Contemporary Data-Driven Approaches 10.1 Introduction 10.2 Feature Engineering for Secured and Intelligent Face Biometric Systems 10.2.1 Facial Features 10.3 Computational Image Features for Face PAD Mechanisms 10.3.1 Handcrafted Features 10.3.1.1 Local Binary Patterns 10.3.1.2 Binarized Statistical Image Features 10.3.1.3 Local Phase Quantization 10.3.1.4 Speed Up Robust Features 10.3.2 Deep Features Engineering for Face PAD Mechanisms 10.4 Security Evaluation Using Face Anti-Spoofing Data Sets 10.5 Open Research Issues and Opportunities 10.6 Conclusion References 11. Reconfigurable Binary Neural Networks Hardware Accelerator for Accurate Data Analysis in Intelligent Systems 11.1 Introduction 11.2 Related Works 11.3 Binary Neural Network Fundamentals 11.3.1 BNN Representation 11.4 Research Considerations 11.4.1 Data sets 11.4.2 Topologies 11.4.3 Accuracy 11.4.4 Hardware Implementation 11.5 Proposed BNN Architecture 11.6 Results and Discussions 11.7 Conclusion References 12. Recommender System: Techniques for Better Decision Making for Society 5.0 12.1 Introduction 12.2 Literature Survey 12.2.1 Recommendation Techniques: Collaborative Filtering Based 12.2.2 Recommendation Techniques: Content-Based 12.2.3 Recommendation Techniques: Hybrid 12.2.4 Recommendation Techniques: Knowledge-Based 12.2.5 Recommendation Techniques: Context Awareness-Based 12.3 Recommendation Systems and Related Issues 12.3.1 User Preferences 12.3.2 Sparsity in Ratings 12.3.3 Cold-Start Problem (User and Item Point of View) 12.3.4 Overspecialization 12.3.5 Novelty and Diversity of Recommendation 12.3.6 Scalability 12.3.7 Context-Awareness 12.3.8 Prediction Accuracy 12.3.9 Privacy 12.3.10 Adaptivity 12.4 Trends in Recommendation System to Support Decision Making for Society 5.0 12.5 Merits and Limitations of Recommendation Systems in Light of Society 5.0 12.6 Design Guidelines for Attenuating the Challenging Issues in Recommendation System 12.7 Conclusion and Future Scope References 13. Implementation of Smart Irrigation System Using Intelligent Systems and Machine Learning Approaches 13.1 Introduction 13.2 Related Work 13.3 Data Analysis 13.4 Proposed System 13.4.1 Setting Up Soil Moisture Threshold Value 13.4.2 Materials and Methods 13.4.2.1 Hardware Requirements 13.4.2.2 Software Requirements 13.4.2.3 Supporting Materials 13.5 Implementation and Working 13.6 Results and Discussion 13.6.1 Simulation Results 13.7 Conclusion 13.8 Future Research Directions References 14. Lightweight Cryptography Using a Trust-Based System for Internet of Things (IoT) 14.1 Introduction 14.1.1 Challenges In Internet of Things 14.1.2 Destination-Oriented Directed Acyclic Graph 14.1.3 IPv6 Low Power and Lossy Networks Routing Protocol (RPL) 14.1.4 Version Number Attack 14.1.5 Trust and Reputation 14.2 Literature Review 14.2.1 Related Work 14.2.2 Research Challenges 14.3 Research Methodology 14.3.1 Proposed Algorithm 14.4 Result and Discussion 14.4.1 Network Deployment 14.4.2 Version Number Attack 14.4.3 Analysis of Packet Loss and Thoughput 14.5 Conclusion References 15. Innovation in Healthcare for Improved Pneumonia Diagnosis with Gradient-Weighted Class Activation Map Visualization 15.1 Introduction 15.1.1 Deep Learning and Convolutional Neural Networks 15.1.2 Image Preprocessing and Visualization 15.1.3 Relevance and Contributions 15.2 Background and Motivation 15.3 Literature Survey 15.4 Data Analysis 15.4.1 Exploratory Data Analysis 15.5 Framework and Methodology Used 15.5.1 Convolutional Neural Network 15.5.2 Data Preprocessing and Augmentation 15.5.3 Transfer Learning: Pretrained Architecture 15.5.3.1 ResNet-50 15.5.3.2 EfficientNet 15.5.3.3 VGG-16 15.5.3.4 MobileNetV2 15.5.3.5 DenseNet 15.6 Design and Architecture 15.6.1 Data Flow Analysis 15.6.2 Architecture Details 15.7 Experimental Setup 15.7.1 Evaluation 15.7.2 Implementation Details 15.8 Results and Discussion 15.8.1 Results 15.8.2 Discussion 15.9 Future Research Directions 15.10 Conclusion Glossary References Index
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