Data-Driven Modeling

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.

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