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Decision Mathematics, Statistical Learning and Data Mining

213,99 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 18048eab7fccbe4531bc9ad2707d288e33
Themengebiete: Applied Mathematics Big Data Analytics Computer Science Computing Technology Data Mining Decision Mathematics Digital Resilience Information Technology Statistical Learning Statistical Mining
Veröffentlichungsdatum: 27.10.2024
EAN: 9789819734498
Sprache: Englisch
Seitenzahl: 381
Produktart: Gebunden
Herausgeber: Mehmood, Obaid Ullah Wah, Yap Bee Wan Yaacob, Wan Fairos
Verlag: Springer Singapore
Untertitel: Selected Contributions from ICMSCT2023, Manila, Philippines, September 20-21
Produktinformationen "Decision Mathematics, Statistical Learning and Data Mining"
This book is a collection of selected research papers presented at the Mathematics, Statistics and Computing Technology (ICMSCT2023), held at the UST Angelicum College, Philippines, from 20th to 21st September 2023. This biennial event is a result from collaborations of university partners in Malaysia, Thailand, Indonesia and Philippines.Increasing investment in digital technologies is a challenge faced by most countries after the crisis caused by COVID-19 and the demand of technological revolution 4.0. Indirectly, regardless of their level of development, they take into account the importance of redesigning strategies for resilient and sustainable regional economic development, increasing regional resilience and minimizing recovery costs as a basis for development. In such situation, this book gather discussion, viewpoints and findings on the recent works of mathematical and computing technology applications in order to propose solutions to overcome adversity of digital resilience.This book covers a wide range of topics on applied mathematics, which includes decision mathematics and also applied statistics covering statistical learning with applications. In addition, the book also highlight the latest application of statistical mining and data visualization, particularly on data mining, machine learning and data visualization. Editors believe this book will interest and influence researchers on the recent techniques, methodologies and applications to ensure digital resilience and support future research.

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