Book

Description

This book is divided into two parts, each addressing cutting-edge AI applications that drive innovation and enhance decision-making. Part One: AI for Financial Forecasting The first three chapters delve into advanced AI techniques for predicting market trends and asset prices. Chapter 1 introduces Integral Long Short-Term Memory (LSTM), a novel neural network architecture designed for futures return prediction, outperforming traditional autoregressive models. Chapter 2 presents Compound Interest LSTM, an enhanced deep learning model tailored for stock return forecasting, demonstrating superior accuracy in dynamic markets. Chapter 3 shifts focus to real estate, proposing a machine learning framework with DBSCAN clustering and Enhanced LSTM for precise house price predictions, validated through extensive experiments. Part Two: AI for Cybersecurity The final two chapters tackle security challenges with AI-driven solutions. Chapter 4 introduces Summation of Multi-order Derivatives LSTM (SOMD-LSTM), a robust approach for Android malware detection, achieving high detection rates with interpretable feature analysis. Chapter 5 leverages Deep Reinforcement Learning to develop an adaptive intrusion detection system, showcasing its effectiveness against evolving cyber threats. Bridging theory and practice, AI and Security offers researchers and practitioners actionable insights into AI’s potential to revolutionize financial markets and safeguard digital ecosystems. Through rigorous experimentation and innovative methodologies, this book sets a new benchmark for AI applications in economics and security. Ideal for data scientists, financial analysts, and cybersecurity experts, this book paves the way for intelligent systems that predict, protect, and prosper.