Frederic Stahl – författare

The 20 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 40 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains.

This book constitutes the refereed proceedings of the 43rd SGAI International Conference on Artificial Intelligence,  AI 2023, held in Cambridge, UK, during December 12–14, 2023.

The 27 full papers and 20 short papers included in this book are carefully reviewed and selected from 67 submissions. They were organized in topical sections as follows: Technical Papers: Speech and Natural Language Analysis, Image Analysis, Neural Nets, Case Based Reasoning and Short Technical Papers. Application Papers: Machine Learning Applications, Machine Vision Applications, Knowledge Discovery and Data Mining Applications, other AI Applications and Short Application Papers.

This two-volume set, LNAI 15446 and LNAI 15447, constitutes the refereed proceedings of the 44th SGAI International Conference on Artificial Intelligence, AI 2024, held in Cambridge, UK, during December 17–19, 2024.

The 36 full papers and 18 short papers presented in these two volumes were carefully reviewed and selected from 80 submissions. Part I includes papers from the Technical stream, whereas Part II includes papers from the Application stream. These volumes are organized into the following topical sections: - 

Part I: Neural nets; Deep learning; Large language models; Machine learning; Evolutionary and genetic algorithms; Knowledge management, Short Technical Papers.

Part II: Machine vision; Evaluation of AI systems; Applications of machine learning; Other AI applications, Short Application Papers.

This two-volume set, LNAI 15446 and LNAI 15447, constitutes the refereed proceedings of the 44th SGAI International Conference on Artificial Intelligence, AI 2024, held in Cambridge, UK, during December 17–19, 2024.

The 36 full papers and 18 short papers presented in these two volumes were carefully reviewed and selected from 80 submissions. Part I includes papers from the Technical stream, whereas Part II includes papers from the Application stream. These volumes are organized into the following topical sections: - 

Part I: Neural nets; Deep learning; Large language models; Machine learning; Evolutionary and genetic algorithms; Knowledge management, Short Technical Papers.

Part II: Machine vision; Evaluation of AI systems; Applications of machine learning; Other AI applications, Short Application Papers.

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.