Applied AI and Machine Learning for Real-World Systems

Artificial intelligence (AI) and machine learning (ML) have rapidly evolved from specialized research domains into transformative technologies that are reshaping nearly every aspect of modern society. Across industries, governments, healthcare systems, educational institutions, and research organizations, AI-driven solutions are increasingly being used to solve complex problems, improve decision-making, automate processes, and drive innovation. As these technologies continue to mature, their influence extends beyond technical efficiency, raising important questions about transparency, accountability, fairness, trustworthiness, and human oversight. As AI becomes embedded in critical sectors, successful implementation requires collaboration to develop systems that are transparent, inclusive, and responsive to human needs. Applied AI and Machine Learning for Real-World Systems explores the practical application of intelligent technologies while emphasizing the human dimensions that accompany their deployment. This book brings together contemporary research, innovative methodologies, emerging frameworks, and real-world case studies that demonstrate how AI and ML can be designed and implemented to address pressing challenges across multiple sectors. Covering topics such as open mapping, digital identity, and adaptive image encryption, this book is an indispensable academic resource for graduate and doctoral students, AI developers, software engineers, data scientists, technology practitioners, digital innovation professionals, policymakers, and more.