UID:
almafu_9959767614202883
Umfang:
1 online resource (491 pages).
Ausgabe:
1st ed. 2020.
ISBN:
3-030-36056-3
Serie:
Advances in Intelligent Systems and Computing, 978
Inhalt:
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.
Anmerkung:
Includes index.
,
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 Data Workflow 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.
Weitere Ausg.:
ISBN 3-030-36055-5
Sprache:
Englisch
DOI:
10.1007/978-3-030-36056-6
