Data Science in Applications

ISBN : 9783031884856

Author : Gintautas Dzemyda

Publisher : Springer

Year : 2025

Language : English

Type : Book

Description : This book provides a forum for presenting and discussing new and promising ideas in the broadly understood data science field and scope. Data science is a broad discipline. Related and partially overlapping fields are data mining, pattern recognition, neurocomputing, statistics, mathematics, data visualisation, databases, data processing, knowledge discovery in databases, big data analysis, computer science, cloud computing, machine learning, and artificial intelligence. Recent research has focused on unlocking the new possibilities of artificial intelligence, not only from a theoretical point of view but also from an applied perspective. In light of recent developments and trends in these fields, the topics covered in the book have been expanded and extended to include various aspects of artificial intelligence (AI), advanced data analysis, data analytics, machine learning, and multimedia, both from a theoretical and a practical application perspective. All of these are coming together, so we are seeing a variety of AI-driven approaches. This book contains 12 chapters by data science researchers. They are divided into “AI-supported multimedia systems” and “Developments, challenges, and applications of advanced data analysis and machine learning.” The first part of the book contains chapters that discuss various aspects of multimedia systems, notably text, voice, and image, in particular from the point of view of how new developments in AI, advanced data analyses, etc., can provide new, effective and efficient, tools and techniques, maybe even imply research and implementation breakthroughs. The second part of the book, “Developments, challenges and applications of advanced data analysis and machine learning,” is concerned with various aspects, problems, solutions, and applications for new sophisticated tools and techniques of data analyses, data analytics, and machine learning providing tools and techniques to take advantage of what is available in data.

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