Enabling AI Applications in Data Science Editors: Aboul-Ella Hassanien, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa

Enabling AI Applications in Data Science is a cutting-edge volume from the Studies in Computational Intelligence series (Volume 911), published by Springer. Edited by renowned researchers Aboul-Ella Hassanien, Mohamed Hamed N. Taha, and Nour Eldeen M. Khalifa, this book offers a deep dive into the synergy between artificial intelligence and data science—two of the most transformative fields in modern technology.

Bridging theoretical foundations and applied innovation, the book covers a wide range of topics including:

AI-driven data analysis and pattern recognition

Machine learning algorithms tailored to data-rich environments

Intelligent decision-making systems

Real-world case studies in finance, healthcare, logistics, and cybersecurity

Emerging trends such as deep learning and hybrid analytics frameworks

Each chapter is authored by domain experts and brings together practical insights, research findings, and implementation strategies. The book is ideal for students, practitioners, and researchers aiming to build smart systems, optimize data workflows, or understand how AI can elevate decision processes.

Whether you're managing big data infrastructures or designing intelligent applications, Enabling AI Applications in Data Science equips you with the tools to navigate the evolving landscape of AI-powered analytics. It’s a valuable resource for anyone committed to unlocking actionable knowledge from data.