Nature Inspired Computing for Data Science -
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This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as diseas ... Pilns apraksts
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Aprašymas
This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
Vairāk informācijas
| Izdevējs | Springer Nature Switzerland |
|---|---|
| Series | Studies in Computational Intelligence |
| Izlaides gads | 2020 |
| Vāka tips | Cietais vāks |
| EAN | 9783030338190 |