Yves Rybarczyk

Dalarna University

Yves Rybarczyk holds a Ph.D. in Robotics from the University of Evry, France. His teaching and research activities focus on artificial intelligence and human–machine interaction. He was an assistant professor at the Nova University of Lisbon between 2007 and 2015. Then, he moved to South America, where he was Associate Professor and Head of the Intelligent & Interactive Systems Lab, Universidad de Las Américas, Ecuador, until 2019. Currently, he is a Full Professor of Data Analytics at Dalarna University, Sweden. He has participated in several projects on the modeling and development of complex and interactive systems. He is the author of more than 100 publications in scientific journals, book chapters, and conference papers. Besides his editorial activities in renowned journals, such as Frontiers in Big Data, Prof. Rybarczyk’s work is recognized as pioneer research in the development of machine learning algorithms for predicting air quality.

Yves Rybarczyk

3books edited

4chapters authored

Latest work with IntechOpen by Yves Rybarczyk

For contemporary societies, data mining has emerged as a serious challenge. Thanks to more advanced analytical tools, the Big Data explosion has enabled businesses to assess their performance more thoroughly and accurately. For example, transitioning from using a basic spreadsheet to using data lake modeling offers more flexibility in terms of consulting and summarizing vast amounts of data from many business angles. Data mining, which is the foundation for this optimization of data analysis, has been strengthened by artificial intelligence and machine learning to find patterns in this deluge of data and build future prediction models, turning it into a critical tool for decision-making. This book provides an understanding of the most modern techniques and uses for data mining. It examines data mining in order to classify datasets, predict outcomes, and optimize analyses. Furthermore, the book demonstrates these technological developments by highlighting relevant applications of data mining in industry, biology, education, medicine, and health.

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