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Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 2223, 2020 2020 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 481 pages, height x width: 235x155 mm, weight: 759 g, 156 Illustrations, color; 54 Illustrations, black and white; XVII, 481 p. 210 illus., 156 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Intelligent Systems and Computing 978
  • Izdošanas datums: 05-Dec-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030360555
  • ISBN-13: 9783030360559
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Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 2223, 2020 2020 ed.Palielināt
  • Formāts: Paperback / softback, 481 pages, height x width: 235x155 mm, weight: 759 g, 156 Illustrations, color; 54 Illustrations, black and white; XVII, 481 p. 210 illus., 156 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Intelligent Systems and Computing 978
  • Izdošanas datums: 05-Dec-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030360555
  • ISBN-13: 9783030360559
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030360559.html

This book provides an introduction to data science and offers a practical overview of the concepts and techniques that readers need to get the most out of their large-scale data mining projects and research studies. It discusses data-analytical thinking, which is essential to extract useful knowledge and obtain commercial value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad interdisciplinary range of scientific methods and processes. The book provides readers with sufficient knowledge to tackle a wide range of issues in complex systems, bringing together the scopes that integrate soft computing and data mining in various combinations of applications and practices, since to thrive in these data-driven ecosystems, researchers, data analysts and practitioners must understand the design choice and options of these approaches. This book helps readers to solve complex benchmark problems and to better appreciate the concepts, tools and techniques used.


Chapter 1: An Enhanced Model for Digital Reference Services
(MDRS).- Chapter 2: Fuzzy Random Based Mean Variance Model For Agricultural
Production Planning.
Chapter 3: Residual Neural Network vs Local Binary
Convolutional Neural Networks for Bilingual Handwritten Digit Recognition.-
Chapter 4: Incorporating the Markov Chain Model in WBSN for Improving
Patients Remote Monitoring Systems.
Chapter 5: Designing Deep Neural
Network with Chicken Swarm Optimization for Violence Video Classification
using VSD2014 Dataset.
Chapter 6: Header Based Email Spam Detection
Framework Using Support Vector Machine (SVM) Technique.
Chapter 7: A
Mechanism to Support Agile Frameworks Enhancing Reliability Assessment for
SCS Development: A Case Study of Medical Surgery Departments.
Chapter
8: Link Bandwidth Recommendation for Indonesian E-Health Grid.
Chapter
9: Investigating the Optimal Parameterization of Deep Neural Network and
Synthetic DataWorkflow for Imbalance Liver Disorder Dataset Classification.-
Chapter 10: Genetic Algorithm Based Parallel K-Means Data Clustering
Algorithm Using MapReduce Programming Paradigm on Hadoop Environment
(GAPKCA).
Chapter 11: Android Botnet Detection by Classification Techniques.