Data-Driven Modeling

Arindam Mondal, PhD is a Professor at Dr. B.C. Roy Engineering College with more than 20 years of experience. He has published more than 35 papers for scientific and technical journals and conferences. He has 18 patents to his credit and has won several awards for his scholarship. Souvik Ganguli, PhD is an Assistant Professor at the Thapar Institute of Engineering and Technology with more than 19 years of teaching experience. He has published more than 50 papers in leading journals, conferences, and book chapters. He has 15 granted patents to his credit and has won several awards for his scholarly activities.

• Preface xv1 Fundamentals of Data Analysis and Preprocessing 1Sudipta Hazra and Arindam Mondal1.1 Introduction 11.2 Data Preprocessing 31.3 Strategies for Preparing Data 101.4 Real-World Applications 171.5 Conclusion 18References 192 Advanced Data Control Methods for Data-Driven Modeling: Techniques, Challenges, and Future Directions 23Aarushi Chatterjee and Souvik Ganguli2.1 Introduction 242.2 Related Works 262.3 Data Control Architecture in Modeling 282.4 Advanced Techniques for Data Control 372.5 Challenges in Data Control for Modeling 442.6 Best Practices for Data Control in Data-Driven Modeling 532.7 Case Studies in Data Control Methods 622.8 Future Directions in Data Control 682.9 Concluding Remarks 75References 753 Machine Learning Algorithms for Data-Driven Modeling 81Souryadip Ghosh, Indrani Mukherjee and Suparna Biswas3.1 Introduction 823.2 What is Machine Learning? 823.3 Classification of Machine Learning Methods 833.4 Supervised Machine Learning 843.5 Support Vector Machine 863.6 Hierarchical Clustering 893.7 Principal Component Analysis 923.8 Conclusion 94Bibliography 944 Neural Networks and Deep Learning in Data-Driven Modeling 99Tanishka Chakraborty, Indrani Mukherjee and Suparna Biswas4.1 Introduction 1004.2 Basic Concept of Neural Network and Deep Learning 1014.3 Applications of Neural Networks and Deep Learning in Data-Driven Modeling 1034.4 Techniques of Neural Networks and Deep Learning in Data-Driven Modeling 1134.5 Methods of Neural Networks and Deep Learning in Data-Driven Modeling 1154.6 Conclusion 117Bibliography 1185 Advances in Time-Series Analysis: Techniques and Applications for Predictive Forecasting 121A. UmaDevi, Jagendra Singh, Shrinwantu Raha, Nazeer Shaik, Anil V. Turukmane and Ishaan Singh5.1 Introduction 1225.2 Foundational Techniques in TSA 1265.2.8 ml Techniques 1325.3 Applications of TSA 1345.4 Future Directions and Emerging Trends 1365.5 Conclusion 139References 1406 Ensemble Methods for Data-Driven Modeling in Agriculture and Applications 143Khalil Ahmed, Mithilesh Kumar Dubey, Kajal and Devendra Kumar Pandey6.1 Introduction 1446.2 Data-Driven Agriculture Cycle 1486.3 Cloud-Based Event and Data Management in Data-Driven Modeling 1496.4 Ensemble Methods for Data-Driven Modeling in Agriculture 1506.5 Applications of Data Modeling in Agriculture 1566.6 Conclusion and Future Directions 159References 1607 Artificial Intelligence–Enabled Ensemble Machine Learning Approaches for Solanaceae Crops 165Kajal, Mithilesh Kumar Dubey, Khalil Ahmed and Devendra Kumar Pandey7.1 Introduction 1667.2 Overview of Solanaceae Crops 1677.3 Data Modeling in Agriculture 1697.4 Ensemble Machine Learning Methods in Sustainable Farming 1727.5 Application of Data Modeling and Ensemble Learning in Solanaceae Crops 1807.6 Conclusion and Future Directions 182References 1828 Dynamic Multitask Transfer Learning with Adaptive Feature Sharing for Heterogeneous Data and Continual Learning 187Toufique Ahammad GaziIntroduction 188Methodology 192Conclusion 200References 2009 Forecasting Solar Power Generation in the Future by ARIMA Approach and Stationary Transformation 203Sudeep SamantaIntroduction 204Conclusion 218References 21810 Prognosticating Plays: ANN-Enabled Score Projection with the Help of FIS 221Susmit Chakraborty and Sourish Harh10.1 Introduction 22110.2 System Model 22310.3 ANFIS Controller 22410.4 Results and Analysis 22810.5 Conclusion 235References 23511 Designing a PID Controller for the Two-Area LFC Problem Using Gradient Descent–Based Linear Regression 239Susmit Chakraborty and Arindam Mondal11.1 Introduction 24011.2 Plant Model 24111.3 PID Controller 24111.4 LR Model 24311.5 Result Analysis 24611.6 Conclusion 253Appendix 254References 25412 Implementing PID Controllers for Data‐Driven Recognizing for a Nonlinear System 257Susmit Chakraborty and Sagnik Agasti12.1 Introduction 25812.2 System Model 25912.3 Nonlinear System 26012.4 ml Engine 26112.5 Result Analysis 26412.6 Conclusion 269References 26913 Temporal Resilience Redux: BiLSTM for Short-Term Load Forecasting in Deep Learning Domain 273Ritu K. R.13.1 Introduction 27413.2 Literature Review 27513.3 Recurrent Neural Networks and LSTM 27813.4 Bidirectional LSTM 28113.5 Experimental Settings 28813.6 Conclusion 291References 292Index 295