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