Intelligent Techniques for Predictive Data Analytics

Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field. Cover Series Page Title Page Copyright Page Contents About the Editors List of Contributors Preface Acknowledgments Chapter 1 Data Mining for Predictive Analytics 1.1 Introduction 1.1.1 Data Mining Process 1.2 Background Study 1.3 Applications of Data Mining 1.3.1 Healthcare Industry 1.3.2 Finance and Banking 1.3.3 Retail and E-commerce 1.3.4 Manufacturing and Supply Chain 1.3.5 Recommender Systems 1.3.6 Social Media Analysis 1.4 Challenges of Data Analytics in Data Mining 1.5 Significance of Data Analytics Tools for Data Mining 1.6 Life Cycle of Data Analytics 1.6.1 Defining the Problem 1.6.2 Data Collection and Preparation 1.6.3 Data Exploration and Visualization 1.6.4 Model Development 1.6.5 Model Training and Validation 1.6.6 Model Evaluation and Selection 1.6.7 Model Deployment and Integration 1.6.8 Model Monitoring and Maintenance 1.6.9 Interpretation and Reporting 1.7 Predictive Analytics Model 1.7.1 Regression Analysis 1.7.2 Time Series Analysis 1.7.3 Classification 1.7.4 Clustering 1.7.5 Neural Networks 1.7.6 Decision Trees 1.7.7 Ensemble Methods 1.7.8 Text Analytics 1.7.9 Anomaly Detection 1.7.10 Collaborative Filtering 1.8 Data Analytics Tools 1.8.1 RapidMiner 1.8.2 Konstanz Information Miner (KNIME) 1.8.3 Statistical Analysis System (SAS) 1.8.4 Python with Scikit-learn 1.8.5 Programming R 1.8.6 Waikato Environment for Knowledge Analysis (Weka) 1.8.7 Tableau 1.8.8 Microsoft Power BI 1.9 Benefits of Predictive Analytics Techniques 1.10 Applications of Predictive Analytics Model 1.10.1 Business Intelligence 1.10.2 Healthcare 1.10.3 Finance 1.10.4 Supply Chain Management 1.10.5 Manufacturing 1.10.6 Marketing and Advertising 1.10.7 Smart Cities 1.11 Conclusion References Chapter 2 Challenges in Building Predictive Models 2.1 Introduction 2.1.1 Different Predictive Models 2.1.2 Challenges Associated with Data Acquisition 2.2 Literature Survey 2.2.1 Challenges in Feature Selection 2.2.2 Challenges in Model Selection 2.2.3 Challenges in Training and Evaluating the Model 2.2.4 Challenges in Deploying and Maintaining the Model Parameter Tuning 2.2.5 Challenges in Interpretability, Explain Ability and Scalability 2.2.6 Challenges for Interpretability 2.2.6.1 Challenges for Explainability 2.2.6.2 Challenges for Scalability 2.2.6.3 Challenges in Ethical and Regulatory Challenges 2.3 Few Suggestions to Overcome the Above Challenges 2.4 Conclusion and Future Directions References Chapter 3 AI-driven Digital Twin and Resource Optimization in Industry 4.0 Ecosystem 3.1 Introduction 3.1.1 Overview of Industry 4.0 3.1.2 Role of AI-driven Digital Twin 3.2 Digital Twin Technology 3.2.1 Definition and Concepts 3.2.2 Components of a Digital Twin 3.2.3 Real-Time Data Integration 3.3 Industry 4.0 Ecosystem 3.3.1 Components and Technologies 3.3.2 IoT Integration 3.3.3 Cyber–Physical Systems 3.4 AI in Digital Twins 3.4.1 Machine Learning and AI Algorithms 3.4.2 Data Analytics for Predictive Maintenance 3.5 Resource Optimization 3.5.1 Importance in Industry 4.0 3.5.2 Cost Reduction Strategies 3.5.3 Energy Efficiency 3.6 AI-driven Resource Allocation 3.6.1 Resource Monitoring 3.6.2 Roles and Types of Optimization Algorithms 3.6.3 Optimization Algorithms for AI-Driven Resource Allocation 3.7 Challenges and Consideration 3.8 Future Trends 3.9 Conclusion References Chapter 4 Predictive Analytics in Healthcare 4.1 Predictive Analytics 4.2 Predictive Analysis in Medical Imaging 4.3 Predictive Analytics in the Pharmaceutical Industry 4.4 Predictive Analytics in Clinical Research 4.5 AI for Disease Prediction 4.6 Medical Image Classification for Disease Prediction References Chapter 5 A Review of Automated Sleep Stage Scoring Using Machine Learning Techniques Based on Physiological Signals 5.1 Introduction 5.2 Review of Related Works 5.2.1 Electroencephalogram (EEG) 5.2.2 Electrocardiogram (ECG) 5.2.3 Respiratory Effort and Electrooculography 5.2.4 Multiple Signals Integration (PSG) 5.3 Methodology 5.3.1 Preprocessing 5.3.2 Feature Extraction 5.3.3 Feature Selection 5.3.4 ML and DL for Sleep Disorder Detection 5.3.4.1 Limitations of Traditional Methods 5.3.4.2 Need for Automated Approaches (ML/DL) 5.4 Conclusion 5.5 Future Work References Chapter 6 Predictive Analytics for Marketing and Sales of Products Using Smart Trolley with Automated Billing System in Shopping Malls Using LBPH and Faster R-CNN 6.1 Introduction 6.2 Major Contributions 6.3 Related Work 6.4 Proposed Methodology 6.4.1 Module 1 – Face Recognition 6.4.2 Module 2 – Trolley Movement 6.4.3 Module 3 – Items Detection 6.4.4 Module 4 – Object and Item Classification 6.5 Experimental Results and Discussions 6.5.1 Experimental Results 6.5.2 Discussions on the Security Aspect of IoT 6.6 Conclusion References Chapter 7 Enhancing Stock Market Predictions Through Predictive Analytics 7.1 Introduction 7.2 Factors Influencing Stock Prices 7.3 Can Markets Be Predicted? 7.3.1 Different Stock Prediction Techniques 7.4 Using Predictive Analytics for Stock Prediction 7.4.1 Stock Prediction Using ML and AI 7.5 Neural Networks 7.5.1 Artificial Neural Network 7.5.1.1 Recurrent Neural Network (RNN) 7.5.1.2 Deep Learning Models 7.6 Conclusion References Chapter 8 Predictive Analytics and Cybersecurity 8.1 Introduction 8.2 Cybersecurity and Predictive Analysis 8.3 Machine Learning 8.3.1 Supervised and Unsupervised Learning 8.3.2 ML Approach to Cybersecurity 8.4 Proactive Cybersecurity and Real-Time Threat Detection 8.4.1 Understanding Proactive Cybersecurity 8.4.2 Real-Time Threat Detection 8.4.3 Use of Predictive Analytics for Threat Detection 8.5 Network Security Analytics 8.5.1 Enhancing Network Security with Predictive Analytics 8.5.2 The Role of ML in Network Security 8.6 Cyber Risk Analytics 8.6.1 Understanding Cyber Risk Management 8.6.2 Predictive Modeling for Cyber Risk Assessment 8.7 Impact of Predictive Analytics on the Cybersecurity Landscape 8.8 Challenges in Applying Predictive Analytics to Cybersecurity 8.9 Conclusion References Chapter 9 Precision Agriculture and Predictive Analytics: Enhancing Agricultural Efficiency and Yield 9.1 Introduction 9.2 Background 9.2.1 IoT and Networks 9.2.2 Predictive Analytics 9.2.3 Applications of Precision Agriculture System 9.3 Precision Agriculture Technologies and Methods 9.3.1 The Role of GPS in Modern Crop Management 9.3.2 Architecture of a Smart Farming Monitoring System 9.3.3 Agricultural Economic Management 9.4 Smart Agriculture Cultivation Recommender System 9.5 Conclusion References Chapter 10 A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture 10.1 Introduction 10.2 Machine Learning 10.3 Deep Learning 10.4 Data Science 10.5 AI in the Agriculture Industry 10.5.1 Techniques in Agriculture 10.5.2 Smart Agriculture 10.6 Conclusions References Chapter 11 An Overview of Predictive Maintenance and Load Forecasting 11.1 Introduction 11.2 PdM: Revolutionizing Asset Management 11.2.1 Development of Maintenance Procedures 11.2.1.1 Reactive Maintenance 11.2.1.2 Preventive Maintenance 11.2.1.3 Predictive Maintenance 11.2.2 Data-Driven Insights for Proactive Decision-Making 11.2.2.1 Role of IoT and Sensor Networks 11.2.2.2 Big Data Analytics and Machine Learning 11.2.2.3 Predictive Algorithms 11.2.2.4 Real-TimeMonitoring and Alerts 11.2.2.5 Benefits of Data-DrivenInsights 11.2.2.6 Limitations and Challenges 11.2.3 Benefits of PdM 11.2.4 Challenges in Implementing PdM 11.3 Load Forecasting: Illuminating the Path Ahead 11.3.1 Navigating Energy Demand Variability 11.3.1.1 Load Patterns and Peaks 11.3.1.2 Seasonal and Weather Influences 11.3.2 Techniques for Accurate Load Forecasting 11.3.2.1 Time Series Analysis 11.3.2.2 Machine Learning Models 11.3.2.3 Hybrid Approaches 11.3.3 Advantages of Load Forecasting 11.3.3.1 Grid Stability and Resource Planning 11.3.3.2 Integration of Renewable Energy Sources 11.3.3.3 Demand-sideManagement and Consumer Empowerment 11.3.4 Troubleshooting Load Forecasting 11.3.4.1 Data Granularity and Quality 11.3.4.2 Incorporation of Dynamic Factors 11.3.4.3 Regulatory and Market Uncertainties 11.3.5 Application Scenarios: From Smart Cities to Rural Electrification 11.3.5.1 Smart Grids: Enabling DR 11.3.5.2 Developing Countries: Bridging the Energy Gap 11.4 Synergies and Future Prospects 11.4.1 Convergence of PdM and Load Forecasting 11.4.1.1 Holistic Asset Management and Increasing Efficiency 11.4.1.2 Real-timeDecision-Makingfor Energy Systems 11.4.2 The Role of Artificial Intelligence (AI) and Edge Computing 11.4.2.1 Edge Analytics for Localized Statistics 11.4.2.2 Automation of Maintenance and Energy OperationsControlled by AI 11.4.3 Sustainability and Environmental Impact 11.4.3.1 Reduced Carbon Footprint through Optimized Operations 11.4.3.2 Integration of Electric Vehicles (EVs) and Energy Storage 11.4.4 Ethical Considerations and Data Privacy 11.4.4.1 Balancing Data Utilization and Consumer Privacy 11.4.4.2 Transparency and Accountability in Automated Systems 11.4.5 Future Trajectories and Uncharted Territories 11.4.5.1 Cognitive PdM: Merging AI and Human Expertise 11.4.5.2 Load Forecasting in a Decentralized Energy Landscape 11.5 Conclusion References Chapter 12 Predictive Analytics: A Tool for Strategic Decision of Employee Turnover 12.1 Introduction 12.2 Literature Review 12.3 Need and Importance of the Study 12.4 Objectives of the Study 12.5 Hypothesis of the Study 12.6 Research Method 12.7 Data Analysis Procedures and Discussion 12.8 Recommendations 12.9 Conclusion References Index EULA