دانلود کتاب مدل‌سازی داده‌محور

The rapid expansion of data generation across scientific, industrial, and social domains has redefined how systems are modeled, analysed, and controlled. Traditional modeling approaches, once reliant on fixed analytical formulations, are now being complemented—and often surpassed—by data-driven models that learn directly from empirical evidence. This evolution has not only revolutionized how we perceive complex systems but also how we optimize, predict, and control them in real-world environments. The book Data-Driven Modeling brings together a diverse set of perspectives and methods to bridge the gap between theoretical modeling and data-centric intelligence. It is a collaborative work by researchers and practitioners committed to advancing the frontiers of intelligent, explainable, and adaptive systems. The chapters cover foundational theory, methodological innovations, and applied insights from multiple disciplines, forming a coherent narrative from fundamentals to advanced techniques. Chapter 1, Fundamentals of Data Analysis and Preprocessing sets the stage by discussing the critical importance of data preparation—covering data cleaning, integration, transformation, reduction, and discretization— and their collective role in ensuring robust modeling outcomes. The chapter underscores that quality data is the cornerstone of meaningful analysis and reliable decision-making. Chapter 2, Advanced Data Control Methods for Data-Driven Modeling introduces modern approaches to data governance, decentralized control, privacy preservation, and federated data architectures. It explores how data control and adaptive regulation form the backbone of reliable, secure, and scalable modeling in domains such as healthcare, finance, and smart grids. Chapter 3 provides a lucid exposition on Machine Learning Algorithms for Data-Driven Modeling, highlighting supervised, unsupervised, and reinforcement learning paradigms and their applications in classification, clustering, and predictive analysis. Chapter 4, Neural Networks and Deep Learning in Data-Driven Modeling, delves into advanced neural architectures, including CNNs, RNNs, GANs, and attention mechanisms. It emphasizes their role in solving complex problems such as image recognition, NLP, time-series forecasting, and anomaly detection. Chapter 5 advances the discussion into Time-Series Analysis for predictive forecasting—introducing classical models such as ARIMA and state-space approaches, and emerging deep-learning-based methods for dynamic systems prediction. Chapters 6 and 7 bring the theory into Agricultural Applications, demonstrating how ensemble learning (Random Forests, Gradient Boosting, XGBoost) and AI-enabled decision systems can revolutionize smart and sustainable farming, particularly for Solanaceae crops. Chapter 8, Dynamic Multitask Transfer Learning, discusses adaptive feature sharing and continual learning frameworks—critical for evolving data ecosystems where model reuse and cross-domain generalization are essential. Chapter 9 applies data-driven forecasting techniques such as ARIMA to the renewable energy domain, illustrating Solar Power Generation Prediction and the transformation of non-stationary data into actionable insights. Chapter 10, Prognosticating Plays, explores a fascinating application of hybrid Artificial Neural Network–Fuzzy Inference Systems (ANFIS) in sports analytics for dynamic score prediction and strategic modeling. Chapters 11 and 12 showcase the intersection of control theory and machine learning through PID Controller Design and Data-Driven Recognition in Nonlinear Systems, integrating gradient-based optimization and intelligent modeling. Chapter 13, Temporal Resilience Redux concludes the book with deep learning applications for short-term load forecasting using bidirectional LSTM architectures—demonstrating how temporal networks capture dynamic dependencies for precise energy demand estimation. Across these chapters, readers will find not only state-of-the-art methodologies but also conceptual clarity and practical guidance on how to implement data-driven paradigms in diverse sectors—from autonomous vehicles and industrial control to agriculture, finance, and renewable energy systems. The editors have consciously designed the book to transition smoothly from fundamental principles to complex, cross-disciplinary applications, reflecting the true continuum of data-driven research.