Intelligent Techniques for Predictive Data Analytics
1st Edition
Published on 25. June 2024
Book
Hardback
272 pages
978-1-394-22796-9 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 16 mm
Weight
531 gr
ISBN-13
978-1-394-22796-9 (9781394227969)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Neha Singh | Shilpi Birla | Mohd Dilshad Ansari
Intelligent Techniques for Predictive Data Analytics
E-Book
06/2024
1st Edition
Wiley
€123.99
Available for download
Neha Singh | Shilpi Birla | Mohd Dilshad Ansari
Intelligent Techniques for Predictive Data Analytics
E-Book
06/2024
1st Edition
Wiley
€123.99
Available for download
Persons
Dr. Neha Singh is an Assistant Professor in the Electronics & Communication Engineering Department at Manipal University Jaipur, India.
Dr. Shilpi Birla is an Associate Professor in the Electronics & Communication Department at Manipal University Jaipur, India.
Dr. Mohd Dilshad Ansari is an Associate Professor in the Computer Science & Engineering Department at SRM University Delhi-NCR, Sonepat, Haryana, India.
Dr. Neeraj Kumar Shukla is an Associate Professor in the Electrical Engineering Department at King Khalid University, Saudi Arabia.
Dr. Shilpi Birla is an Associate Professor in the Electronics & Communication Department at Manipal University Jaipur, India.
Dr. Mohd Dilshad Ansari is an Associate Professor in the Computer Science & Engineering Department at SRM University Delhi-NCR, Sonepat, Haryana, India.
Dr. Neeraj Kumar Shukla is an Associate Professor in the Electrical Engineering Department at King Khalid University, Saudi Arabia.
Content
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 Goek, 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
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 Goek, 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