{"product_id":"intelligent-techniques-for-predictive-data-analytics-isbn-9781394227969","title":"Intelligent Techniques for Predictive Data Analytics","description":"\u003cp\u003e\u003cb\u003eComprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003eprovides 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. \u003c\/p\u003e\u003cp\u003eReaders 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. \u003c\/p\u003e\u003cp\u003eTraditional 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003ecovers sample topics such as:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eModels to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models \u003c\/li\u003e\n\u003cli\u003ePrice forecasting, quality optimization, and insect and disease plant and monitoring in agriculture \u003c\/li\u003e\n\u003cli\u003eFraud detection and prevention, credit scoring, financial planning, and customer analytics \u003c\/li\u003e\n\u003cli\u003eBig data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting \u003c\/li\u003e\n\u003cli\u003eManagement of uncertainty in predictive data analytics and probable future developments in the field\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003eis 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. \u003c\/p\u003e\u003cp\u003eAbout the Editors xiii\u003c\/p\u003e \u003cp\u003eList of Contributors xv\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Mining for Predictive Analytics 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrakash Kuppuswamy, Mohd Dilshad Ansari, M. Mohan, and Sayed Q.Y. Al Khalidi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Background Study 3\u003c\/p\u003e \u003cp\u003e1.3 Applications of Data Mining 4\u003c\/p\u003e \u003cp\u003e1.4 Challenges of Data Analytics in Data Mining 7\u003c\/p\u003e \u003cp\u003e1.5 Significance of Data Analytics Tools for Data Mining 7\u003c\/p\u003e \u003cp\u003e1.6 Life Cycle of Data Analytics 8\u003c\/p\u003e \u003cp\u003e1.7 Predictive Analytics Model 11\u003c\/p\u003e \u003cp\u003e1.8 Data Analytics Tools 14\u003c\/p\u003e \u003cp\u003e1.9 Benefits of Predictive Analytics Techniques 18\u003c\/p\u003e \u003cp\u003e1.10 Applications of Predictive Analytics Model 18\u003c\/p\u003e \u003cp\u003e1.11 Conclusion 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Challenges in Building Predictive Models 25\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRakesh Nayak, Ch. Rajaramesh, and Umashankar Ghugar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Literature Survey 30\u003c\/p\u003e \u003cp\u003e2.3 Few Suggestions to Overcome the Above Challenges 42\u003c\/p\u003e \u003cp\u003e2.4 Conclusion and Future Directions 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 AI-driven Digital Twin and Resource Optimization in Industry 4.0 Ecosystem 47\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePankaj Bhambri, Sita Rani, and Alex Khang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Digital Twin Technology 50\u003c\/p\u003e \u003cp\u003e3.3 Industry 4.0 Ecosystem 53\u003c\/p\u003e \u003cp\u003e3.4 AI in Digital Twins 56\u003c\/p\u003e \u003cp\u003e3.5 Resource Optimization 57\u003c\/p\u003e \u003cp\u003e3.6 AI-driven Resource Allocation 59\u003c\/p\u003e \u003cp\u003e3.7 Challenges and Consideration 62\u003c\/p\u003e \u003cp\u003e3.8 Future Trends 62\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Predictive Analytics in Healthcare 71\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN. Venkateswarulu, P. Pavan Kumar, and O. Obulesu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Predictive Analytics 71\u003c\/p\u003e \u003cp\u003e4.2 Predictive Analysis in Medical Imaging 73\u003c\/p\u003e \u003cp\u003e4.3 Predictive Analytics in the Pharmaceutical Industry 75\u003c\/p\u003e \u003cp\u003e4.4 Predictive Analytics in Clinical Research 78\u003c\/p\u003e \u003cp\u003e4.5 AI for Disease Prediction 81\u003c\/p\u003e \u003cp\u003e4.6 Medical Image Classification for Disease Prediction 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Review of Automated Sleep Stage Scoring Using Machine Learning Techniques Based on Physiological Signals 89\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSantosh Kumar Satapathy, Poojan Agrawal, Namra Shah, Ranjit Panigrahi, Bidita Khandelwal, Paolo Barsocchi, and Akash Kumar Bhoi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.2 Review of Related Works 91\u003c\/p\u003e \u003cp\u003e5.3 Methodology 98\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 105\u003c\/p\u003e \u003cp\u003e5.5 Future Work 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 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\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBalla Adi Narayana Raju, Deepika Ghai, Suman Lata Tripathi, Sunpreet Kaur Nanda, and Sardar M.N. Islam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Major Contributions 112\u003c\/p\u003e \u003cp\u003e6.3 Related Work 113\u003c\/p\u003e \u003cp\u003e6.4 Proposed Methodology 119\u003c\/p\u003e \u003cp\u003e6.5 Experimental Results and Discussions 126\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Enhancing Stock Market Predictions Through Predictive Analytics 135\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAmeya Patil, Shantanu Saha, and Rajeev Sengupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 135\u003c\/p\u003e \u003cp\u003e7.2 Factors Influencing Stock Prices 137\u003c\/p\u003e \u003cp\u003e7.3 Can Markets Be Predicted? 138\u003c\/p\u003e \u003cp\u003e7.4 Using Predictive Analytics for Stock Prediction 140\u003c\/p\u003e \u003cp\u003e7.5 Neural Networks 141\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Predictive Analytics and Cybersecurity 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMohammed Sayeeduddin Habeeb\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Cybersecurity and Predictive Analysis 152\u003c\/p\u003e \u003cp\u003e8.3 Machine Learning 153\u003c\/p\u003e \u003cp\u003e8.4 Proactive Cybersecurity and Real-Time Threat Detection 156\u003c\/p\u003e \u003cp\u003e8.5 Network Security Analytics 159\u003c\/p\u003e \u003cp\u003e8.6 Cyber Risk Analytics 160\u003c\/p\u003e \u003cp\u003e8.7 Impact of Predictive Analytics on the Cybersecurity Landscape 162\u003c\/p\u003e \u003cp\u003e8.8 Challenges in Applying Predictive Analytics to Cybersecurity 162\u003c\/p\u003e \u003cp\u003e8.9 Conclusion 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Precision Agriculture and Predictive Analytics: Enhancing Agricultural Efficiency and Yield 171\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNafees Akhter Farooqui, Mohd. Haleem, Wasim Khan, and Mohammad Ishrat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 171\u003c\/p\u003e \u003cp\u003e9.2 Background 173\u003c\/p\u003e \u003cp\u003e9.3 Precision Agriculture Technologies and Methods 178\u003c\/p\u003e \u003cp\u003e9.4 Smart Agriculture Cultivation Recommender System 183\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture 189\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKaran Aggarwal, Ruchi Doshi, Maad M. Mijwil, Kamal Kant Hiran, Murat Gök, and Indu Bala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 189\u003c\/p\u003e \u003cp\u003e10.2 Machine Learning 191\u003c\/p\u003e \u003cp\u003e10.3 Deep Learning 192\u003c\/p\u003e \u003cp\u003e10.4 Data Science 193\u003c\/p\u003e \u003cp\u003e10.5 AI in the Agriculture Industry 194\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 An Overview of Predictive Maintenance and Load Forecasting 203\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNand Kishor Gupta, Vivek Upadhyaya, and Vijay Gali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 203\u003c\/p\u003e \u003cp\u003e11.2 PdM: Revolutionizing Asset Management 204\u003c\/p\u003e \u003cp\u003e11.3 Load Forecasting: Illuminating the Path Ahead 216\u003c\/p\u003e \u003cp\u003e11.4 Synergies and Future Prospects 222\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Predictive Analytics: A Tool for Strategic Decision of Employee Turnover 231\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSMD Azash, Potala Venkata Subbaiah, and Lucia Vilcekova\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 231\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 232\u003c\/p\u003e \u003cp\u003e12.3 Need and Importance of the Study 233\u003c\/p\u003e \u003cp\u003e12.4 Objectives of the Study 235\u003c\/p\u003e \u003cp\u003e12.5 Hypothesis of the Study 235\u003c\/p\u003e \u003cp\u003e12.6 Research Method 235\u003c\/p\u003e \u003cp\u003e12.7 Data Analysis Procedures and Discussion 236\u003c\/p\u003e \u003cp\u003e12.8 Recommendations 240\u003c\/p\u003e \u003cp\u003e12.9 Conclusion 241\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003eIndex 245\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eDr. Neha Singh\u003c\/b\u003e is an Assistant Professor in the Electronics \u0026amp; Communication Engineering Department at Manipal University Jaipur, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Shilpi Birla\u003c\/b\u003e is an Associate Professor in the Electronics \u0026amp; Communication Department at Manipal University Jaipur, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Mohd Dilshad Ansari\u003c\/b\u003e is an Associate Professor in the Computer Science \u0026amp; Engineering Department at SRM University Delhi-NCR, Sonepat, Haryana, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Neeraj Kumar Shukla\u003c\/b\u003e is an Associate Professor in the Electrical Engineering Department at King Khalid University, Saudi Arabia.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eComprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003eprovides 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. \u003c\/p\u003e\u003cp\u003eReaders 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. \u003c\/p\u003e\u003cp\u003eTraditional 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003ecovers sample topics such as:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eModels to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models \u003c\/li\u003e\n\u003cli\u003ePrice forecasting, quality optimization, and insect and disease plant and monitoring in agriculture \u003c\/li\u003e\n\u003cli\u003eFraud detection and prevention, credit scoring, financial planning, and customer analytics \u003c\/li\u003e\n\u003cli\u003eBig data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting \u003c\/li\u003e\n\u003cli\u003eManagement of uncertainty in predictive data analytics and probable future developments in the field\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eIntelligent Techniques for Predictive Data Analytics \u003c\/i\u003eis 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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989441790181,"sku":"NP9781394227969","price":145.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394227969.jpg?v=1761784113","url":"https:\/\/k12savings.com\/products\/intelligent-techniques-for-predictive-data-analytics-isbn-9781394227969","provider":"K12savings","version":"1.0","type":"link"}