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Record Nr. |
UNINA9910568247203321 |
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Titolo |
Recent Advances in Soft Computing and Data Mining : Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022 / / edited by Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, Jemal H. Abawajy, Nureize Arbaiy |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (415 pages) |
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Collana |
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Lecture Notes in Networks and Systems, , 2367-3389 ; ; 457 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Engineering—Data processing |
Artificial intelligence |
Computational Intelligence |
Data Engineering |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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This book unfolds ways to transform data into innovative solutions perceived as new remarkable and meaningful value. It offers practical views of the concepts and techniques readers need to get the most out of their large-scale research and data mining projects. It strides them through the data-analytical thinking, circumvents the difficulty in deciphering complex data systems and obtaining commercialization value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad spectrum, an interdisciplinary field of scientific methods and processes. The book, Recent Advances in Soft Computing and Data Mining, delivers sufficient knowledge to tackle a wide range of issues seen in complex systems. This is done by exploring a vast combination of practices and applications by incorporating these two domains. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners |
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must choose the best design to approach the problem with the most efficient tools and techniques. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must understand the design choice and options of these approaches, thus to better appreciate the concepts, tools, and techniques used. |
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