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＜電子ブック＞
Big Data Analytics for Smart Transport and Healthcare Systems / by Saeid Pourroostaei Ardakani, Ali Cheshmehzangi
(Urban Sustainability. ISSN:27316491)

版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XVII, 190 p. 75 illus., 72 illus. in color : online resource
著者標目	*Pourroostaei Ardakani, Saeid author Cheshmehzangi, Ali author SpringerLink (Online service)
件　名	LCSH:Big data LCSH:Transportation LCSH:Health services administration FREE:Big Data FREE:Transportation Economics FREE:Health Care Management
一般注記	The Role of Big Data Analytics in Urban Systems: Review and Prospect for Smart Transport and Healthcare Systems -- Smart Transport -- Big Data Analysis for an Optimised Classification for Flight Status: Prediction Analysis using Machine Learning Classifiers -- On-Board Unit Freight Transport Data Analysis and Prediction: Big Data Analysis for Data Pre-processing and Result Accuracy -- Data-driven Multi-target Prediction Analysis for Driving Pattern Recognition: A Machine Learning Approach to enhance Prediction Accuracy -- A Predictive Data Analysis for Traffic Accidents: Real-time Data use for Mobility Improvement and Accident Reduction -- Smart Healthcare -- Healthcare Infrastructure Development and Pandemic Prevention: An Optimal Model for Healthcare Investment using Big Data -- Big Data for Social Media Analysis during the COVID-19 Pandemic: An Emotion Analysis based on Influences from Social Networks -- Big Data-enabled Time Series analysis for Climate Change Analysis in Brazil: An Artificial Neural Network Machine Learning Model -- Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approach -- Big Data Analytics and the Future of Smart Transport and Healthcare Systems This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured HTTP:URL=https://doi.org/10.1007/978-981-99-6620-2

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