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Computational Intelligence in Data Science edited by Sarath Chandran K R, Sujaudeen N, Beulah A, Shahul Hamead H. [electronic resource] :

Contributor(s): Material type: TextTextPublication details: Cham Springer Nature Switzerland 2023Edition: 1st ed. 2023Description: XIII, 328 p. 196 illus., 152 illus. in color. online resourceISBN:
  • 9783031382963
Subject(s): DDC classification:
  • 6.312
Online resources: Summary: This book constitutes the proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2023, which took place in Kalavakkam, India, in February 2023. The 24 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The major theme of the conference was intended to be computation intelligence and knowledge management. Various emerging areas like IoT, cyber security and data science need computation intelligence to align with the cutting-edge research. Machine learning delivers insights hidden in data for rapid, automated responses and improved decision making. Machine learning for IoT can be used to project future trends, detect anomalies, and augment intelligence by ingesting image, video, and audio.
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Holdings
Item type Home library Call number Status Notes Date due Barcode Item holds
e-Book e-Book S. R. Ranganathan Learning Hub Online Available Platform:Springer EB2533
Total holds: 0

This book constitutes the proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2023, which took place in Kalavakkam, India, in February 2023. The 24 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The major theme of the conference was intended to be computation intelligence and knowledge management. Various emerging areas like IoT, cyber security and data science need computation intelligence to align with the cutting-edge research. Machine learning delivers insights hidden in data for rapid, automated responses and improved decision making. Machine learning for IoT can be used to project future trends, detect anomalies, and augment intelligence by ingesting image, video, and audio.

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