Data Management, Analytics and Innovation
Proceedings of ICDMAI 2025, Volume 1
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Beschreibung
This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 9th International Conference on Data Management, Analytics and Innovation (ICDMAI 2025), held during 17–19 January 2025 at St. Xavier’s College (Autonomous), Kolkata, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into three volumes.
This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 9th International Conference on Data Management, Analytics and Innovation (ICDMAI 2025), held during 17–19 January 2025 at St. Xavier’s College (Autonomous), Kolkata, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into three volumes.
Presents research works in the field of data management, analytics and innovation Addresses state-of-the-art topics and discusses challenges and solutions for future development Serves as a reference for researchers and practitioners in academia and industry
Autor*in
Saptarsi Goswami
Themen in »Data Management, Analytics and Innovation«
Algorithms and Models Feature Engineering Supervised Learning Unsupervised Learning Semi-supervised Learning Association Rule Mining Recommendation System Fuzzy and Rough Set Reinforcement Learning ICDMAI 2025 Proceedings