Intelligent Techniques for Predictive Data Analytics

Neha Singh , Shilpi Birla , Mohd Dilshad Ansari , Neeraj Kumar Shukla 지음

Wiley-IEEE Press 2024년 06월 21일 출간

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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 A

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About the Editors xiii List of Contributors xv Preface xix Acknowledgments xxi 1 Data Mining for Predictive Analytics 1Prakash 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 25Rakesh 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 47Pankaj 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 71N. 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

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작가정보

저자(글) Neha Singh

저자(글) Shilpi Birla

저자(글) Mohd Dilshad Ansari

저자(글) Neeraj Kumar Shukla