Buch, Englisch, 320 Seiten
Buch, Englisch, 320 Seiten
ISBN: 978-1-394-28789-5
Verlag: Wiley
Equip yourself with the essentials of informed decision-making with this practical guide to mastering data-driven modeling and extracting actionable, meaningful patterns from the vast sea of modern data.
In an era defined by data, the ability to transform raw information into actionable insights is a skill set that transcends industries and disciplines. This book is a comprehensive guide designed to unravel the intricacies of extracting meaningful patterns from the vast sea of data that surrounds us. It explores the significance of data-driven modelling, comparing it to traditional approaches and setting the stage for understanding the transformative power and diverse applications of data-driven techniques. This comprehensive resource empowers readers to leverage data for informed decision-making. Whether you are a novice looking to grasp the fundamentals or an experienced professional seeking advanced techniques, this book serves as a practical guide through the dynamic landscape of data-driven modelling. Through clear explanations, hands-on examples, and real-world applications, readers will gain the skills needed to navigate the complexities of modern data analysis.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
Weitere Infos & Material
Preface xv
1 Fundamentals of Data Analysis and Preprocessing 1
Sudipta Hazra and Arindam Mondal
1.1 Introduction 1
1.2 Data Preprocessing 3
1.3 Strategies for Preparing Data 10
1.4 Real-World Applications 17
1.5 Conclusion 18
References 19
2 Advanced Data Control Methods for Data-Driven Modeling: Techniques, Challenges, and Future Directions 23
Aarushi Chatterjee and Souvik Ganguli
2.1 Introduction 24
2.2 Related Works 26
2.3 Data Control Architecture in Modeling 28
2.4 Advanced Techniques for Data Control 37
2.5 Challenges in Data Control for Modeling 44
2.6 Best Practices for Data Control in Data-Driven Modeling 53
2.7 Case Studies in Data Control Methods 62
2.8 Future Directions in Data Control 68
2.9 Concluding Remarks 75
References 75
3 Machine Learning Algorithms for Data-Driven Modeling 81
Souryadip Ghosh, Indrani Mukherjee and Suparna Biswas
3.1 Introduction 82
3.2 What is Machine Learning? 82
3.3 Classification of Machine Learning Methods 83
3.4 Supervised Machine Learning 84
3.5 Support Vector Machine 86
3.6 Hierarchical Clustering 89
3.7 Principal Component Analysis 92
3.8 Conclusion 94
Bibliography 94
4 Neural Networks and Deep Learning in Data-Driven Modeling 99
Tanishka Chakraborty, Indrani Mukherjee and Suparna Biswas
4.1 Introduction 100
4.2 Basic Concept of Neural Network and Deep Learning 101
4.3 Applications of Neural Networks and Deep Learning in Data-Driven Modeling 103
4.4 Techniques of Neural Networks and Deep Learning in Data-Driven Modeling 113
4.5 Methods of Neural Networks and Deep Learning in Data-Driven Modeling 115
4.6 Conclusion 117
Bibliography 118
5 Advances in Time-Series Analysis: Techniques and Applications for Predictive Forecasting 121
A. UmaDevi, Jagendra Singh, Shrinwantu Raha, Nazeer Shaik, Anil V. Turukmane and Ishaan Singh
5.1 Introduction 122
5.2 Foundational Techniques in TSA 1265.2.8 ml Techniques 132
5.3 Applications of TSA 134
5.4 Future Directions and Emerging Trends 136
5.5 Conclusion 139
References 140
6 Ensemble Methods for Data-Driven Modeling in Agriculture and Applications 143
Khalil Ahmed, Mithilesh Kumar Dubey, Kajal and Devendra Kumar Pandey
6.1 Introduction 144
6.2 Data-Driven Agriculture Cycle 148
6.3 Cloud-Based Event and Data Management in Data-Driven Modeling 149
6.4 Ensemble Methods for Data-Driven Modeling in Agriculture 150
6.5 Applications of Data Modeling in Agriculture 156
6.6 Conclusion and Future Directions 159
References 160
7 Artificial Intelligence–Enabled Ensemble Machine Learning Approaches for Solanaceae Crops 165
Kajal, Mithilesh Kumar Dubey, Khalil Ahmed and Devendra Kumar Pandey
7.1 Introduction 166
7.2 Overview of Solanaceae Crops 167
7.3 Data Modeling in Agriculture 169
7.4 Ensemble Machine Learning Methods in Sustainable Farming 172
7.5 Application of Data Modeling and Ensemble Learning in Solanaceae Crops 180
7.6 Conclusion and Future Directions 182
References 182
8 Dynamic Multitask Transfer Learning with Adaptive Feature Sharing for Heterogeneous Data and Continual Learning 187
Toufique Ahammad Gazi
Introduction 188
Methodology 192
Conclusion 200
References 200
9 Forecasting Solar Power Generation in the Future by ARIMA Approach and Stationary Transformation 203
Sudeep Samanta
Introduction 204
Conclusion 218
References 218
10 Prognosticating Plays: ANN-Enabled Score Projection with the Help of FIS 221
Susmit Chakraborty and Sourish Harh
10.1 Introduction 221
10.2 System Model 223
10.3 ANFIS Controller 224
10.4 Results and Analysis 228
10.5 Conclusion 235
References 235
11 Designing a PID Controller for the Two-Area LFC Problem Using Gradient Descent–Based Linear Regression 239
Susmit Chakraborty and Arindam Mondal
11.1 Introduction 240
11.2 Plant Model 241
11.3 PID Controller 241
11.4 LR Model 243
11.5 Result Analysis 246
11.6 Conclusion 253
Appendix 254
References 254
12 Implementing PID Controllers for Data-Driven Recognizing for a Nonlinear System 257
Susmit Chakraborty and Sagnik Agasti
12.1 Introduction 258
12.2 System Model 259
12.3 Nonlinear System 260
12.4 ml Engine 261
12.5 Result Analysis 264
12.6 Conclusion 269
References 269
13 Temporal Resilience Redux: BiLSTM for Short-Term Load Forecasting in Deep Learning Domain 273
Ritu K. R.
13.1 Introduction 274
13.2 Literature Review 275
13.3 Recurrent Neural Networks and LSTM 278
13.4 Bidirectional LSTM 281
13.5 Experimental Settings 288
13.6 Conclusion 291
References 292
Index 295
