ISBN:
9783031244537
Language:
English
Pages:
1 Online-Ressource(XII, 252 p. 100 illus., 76 illus. in color.)
Edition:
1st ed. 2023.
Series Statement:
Studies in Computational Intelligence 1084
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Keywords:
Engineering—Data processing.
;
Computational intelligence.
;
Artificial intelligence.
;
Engineering
Abstract:
Computational Thinking Design Application for STEAM Education -- Education Data for Science: Case of Lithuania -- Imbalanced Data Classification Approach Based on Clustered Training Set -- Baltic States in Global Value Chains: Quantifying International Production Sharing at Bilateral and Sectoral Levels -- The Soft Power Of Understanding Social Media Dynamics: A Data-Driven Approach -- Bootstrapping Network Autoregressive Models for Testing Linearity -- Novel data science methodologies for essential genes identification based on network analysis -- Acoustic Analysis for Vocal Fold Assessment - Challenges, Trends, and Opportunities -- The Paradigm of an Explainable Artificial Intelligence (XAI) and Data Science (DS) Based Decision Support System (DSS) -- Stock Portfolio Risk-Return Ratio Optimisation using Grey Wolf Model -- Towards Seamless Execution of Deep Learning Application on Heterogeneous HPC Systems.
Abstract:
This book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.
DOI:
10.1007/978-3-031-24453-7
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