Data Science: From Research to Application (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0.
Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017.
This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to shareideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems.
Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians.

Produktdetails

• Verlag: Springer International Publishing
• Erscheinungstermin: 28. Januar 2020
• Englisch
• ISBN-13: 9783030373092
• Artikelnr.: 58594540

Inhaltsangabe

Efficient Cluster Head Selection using the Non-Linear Programming Method for Wireless Sensor Networks.- A New Distributed Ensemble Method with Applications to Machine Learning.- Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches.- Forecasting of Customer Behavior using Time Series Analysis.- Using Augmented Genetic Algorithm for Search-Based Software Testing.- A Novel Topological Descriptor for ASL.- Extracting Cellphone users’ Stay Locations by Multiple-Steps Clustering Approach.

---

Efficient Cluster Head Selection using the Non-Linear Programming Method for Wireless Sensor Networks.- A New Distributed Ensemble Method with Applications to Machine Learning.- Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches.- Forecasting of Customer Behavior using Time Series Analysis.- Using Augmented Genetic Algorithm for Search-Based Software Testing.- A Novel Topological Descriptor for ASL.- Extracting Cellphone users' Stay Locations by Multiple-Steps Clustering Approach.