Optimization, machine learning, and artificial intelligence are revolutionizing society, medicine, environmental science, transportation, and education. In medicine, AI-driven diagnostics and personalized treatments improve patient outcomes, while optimization streamlines resource allocation. Environmental science benefits from machine learning's ability to analyze complex datasets, enabling sustainable practices and climate predictions. In transportation, optimization enhances logistics and traffic flow, with AI powering autonomous vehicles and predictive maintenance. Across all areas, these technologies drive efficiency, innovation, and smarter decision-making.
Practical Applications of Machine Learning and AI: Medicine, Environmental Science, Transportation, and Education provides deeper understanding of the complexities of optimization, machine learning and AI, examining their theoretical foundations. Furthermore, it contributes to the ongoing advancement of these fields, practical applications, and transformative potentials. Covering topics including Medical Image Segmentation, student performance prediction, and pothole detection, this book is an excellent resource for computer scientists, researchers, scholars, academicians, professionals, and more.