Home Book Artificial Intelligence and Machine Learning Methods for Engineering Systems: Forecasting and Mathematical Insights
Keywords
Machine Learning applications,Artificial Intelligence in healthcare,Deep learning research papers,AI-based disease prediction,Solar power forecasting using LSTM,IoT cybersecurity and botnet detection,Skin disease detection using AI,Cardiac disease prediction models,AI in newable energy systems,Smart healthcare analytics,Deep learning in medical diagnosis,Predictive nalytics in healthcare,AI for crop disease detection,Sleep disorder classification using machine learning,Edge AI in cybersecurity,Hybrid deep learning models,AI in education and student retention,Computer vision in medical imaging,Data-driven health risk prediction,Intelligent systems and machine learning research
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<p style="text-align:justify">This book presents a comprehensive exploration of advanced Artificial Intelligence and Machine Learning applications across diverse real-world domains. It brings together cutting-edge research in healthcare, renewable energy, cybersecurity, agriculture, and human behavior analytics, demonstrating how intelligent systems are transforming modern society. The volume covers deep learning architectures, predictive modeling, IoT security frameworks, medical diagnosis systems, and energy forecasting techniques, offering both theoretical foundations and practical implementations.</p>
<p style="text-align:justify">With contributions from contemporary research, the book highlights innovative solutions such as disease prediction systems, solar energy forecasting using LSTM networks, AI-based intrusion detection, and smart healthcare analytics. It also emphasizes hybrid models, edge AI, and data-driven decision-making approaches.</p>
<p style="text-align:justify">Designed for researchers, academicians, industry professionals, and postgraduate students, this book serves as a valuable resource for understanding how AI-powered technologies are shaping the future of intelligent systems and sustainable innovation across multiple disciplines.</p>
