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.

Table of Contents:
About the Editors xiii List of Contributors xv Preface xix Acknowledgments xxi 1 Data Mining for Predictive Analytics 1 Prakash Kuppuswamy, Mohd Dilshad Ansari, M. Mohan, and Sayed Q.Y. Al Khalidi 1.1 Introduction 1 1.2 Background Study 3 1.3 Applications of Data Mining 4 1.4 Challenges of Data Analytics in Data Mining 7 1.5 Significance of Data Analytics Tools for Data Mining 7 1.6 Life Cycle of Data Analytics 8 1.7 Predictive Analytics Model 11 1.8 Data Analytics Tools 14 1.9 Benefits of Predictive Analytics Techniques 18 1.10 Applications of Predictive Analytics Model 18 1.11 Conclusion 20 2 Challenges in Building Predictive Models 25 Rakesh Nayak, Ch. Rajaramesh, and Umashankar Ghugar 2.1 Introduction 25 2.2 Literature Survey 30 2.3 Few Suggestions to Overcome the Above Challenges 42 2.4 Conclusion and Future Directions 44 3 AI-driven Digital Twin and Resource Optimization in Industry 4.0 Ecosystem 47 Pankaj Bhambri, Sita Rani, and Alex Khang 3.1 Introduction 47 3.2 Digital Twin Technology 50 3.3 Industry 4.0 Ecosystem 53 3.4 AI in Digital Twins 56 3.5 Resource Optimization 57 3.6 AI-driven Resource Allocation 59 3.7 Challenges and Consideration 62 3.8 Future Trends 62 3.9 Conclusion 63 4 Predictive Analytics in Healthcare 71 N. Venkateswarulu, P. Pavan Kumar, and O. Obulesu 4.1 Predictive Analytics 71 4.2 Predictive Analysis in Medical Imaging 73 4.3 Predictive Analytics in the Pharmaceutical Industry 75 4.4 Predictive Analytics in Clinical Research 78 4.5 AI for Disease Prediction 81 4.6 Medical Image Classification for Disease Prediction 83 5 A Review of Automated Sleep Stage Scoring Using Machine Learning Techniques Based on Physiological Signals 89 Santosh Kumar Satapathy, Poojan Agrawal, Namra Shah, Ranjit Panigrahi, Bidita Khandelwal, Paolo Barsocchi, and Akash Kumar Bhoi 5.1 Introduction 89 5.2 Review of Related Works 91 5.3 Methodology 98 5.4 Conclusion 105 5.5 Future Work 105 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 111 Balla Adi Narayana Raju, Deepika Ghai, Suman Lata Tripathi, Sunpreet Kaur Nanda, and Sardar M.N. Islam 6.1 Introduction 111 6.2 Major Contributions 112 6.3 Related Work 113 6.4 Proposed Methodology 119 6.5 Experimental Results and Discussions 126 6.6 Conclusion 130 7 Enhancing Stock Market Predictions Through Predictive Analytics 135 Ameya Patil, Shantanu Saha, and Rajeev Sengupta 7.1 Introduction 135 7.2 Factors Influencing Stock Prices 137 7.3 Can Markets Be Predicted? 138 7.4 Using Predictive Analytics for Stock Prediction 140 7.5 Neural Networks 141 7.6 Conclusion 146 8 Predictive Analytics and Cybersecurity 151 Mohammed Sayeeduddin Habeeb 8.1 Introduction 151 8.2 Cybersecurity and Predictive Analysis 152 8.3 Machine Learning 153 8.4 Proactive Cybersecurity and Real-Time Threat Detection 156 8.5 Network Security Analytics 159 8.6 Cyber Risk Analytics 160 8.7 Impact of Predictive Analytics on the Cybersecurity Landscape 162 8.8 Challenges in Applying Predictive Analytics to Cybersecurity 162 8.9 Conclusion 164 9 Precision Agriculture and Predictive Analytics: Enhancing Agricultural Efficiency and Yield 171 Nafees Akhter Farooqui, Mohd. Haleem, Wasim Khan, and Mohammad Ishrat 9.1 Introduction 171 9.2 Background 173 9.3 Precision Agriculture Technologies and Methods 178 9.4 Smart Agriculture Cultivation Recommender System 183 9.5 Conclusion 184 10 A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture 189 Karan Aggarwal, Ruchi Doshi, Maad M. Mijwil, Kamal Kant Hiran, Murat Gök, and Indu Bala 10.1 Introduction 189 10.2 Machine Learning 191 10.3 Deep Learning 192 10.4 Data Science 193 10.5 AI in the Agriculture Industry 194 10.6 Conclusions 198 11 An Overview of Predictive Maintenance and Load Forecasting 203 Nand Kishor Gupta, Vivek Upadhyaya, and Vijay Gali 11.1 Introduction 203 11.2 PdM: Revolutionizing Asset Management 204 11.3 Load Forecasting: Illuminating the Path Ahead 216 11.4 Synergies and Future Prospects 222 11.5 Conclusion 225 12 Predictive Analytics: A Tool for Strategic Decision of Employee Turnover 231 SMD Azash, Potala Venkata Subbaiah, and Lucia Vilcekova 12.1 Introduction 231 12.2 Literature Review 232 12.3 Need and Importance of the Study 233 12.4 Objectives of the Study 235 12.5 Hypothesis of the Study 235 12.6 Research Method 235 12.7 Data Analysis Procedures and Discussion 236 12.8 Recommendations 240 12.9 Conclusion 241 References 242 Index 245