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Big data analytics for smart transpo...

Ardakani, Saeid Pourroostaei.

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Big data analytics for smart transport and healthcare systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analytics for smart transport and healthcare systems/ by Saeid Pourroostaei Ardakani, Ali Cheshmehzangi.
Author:	Ardakani, Saeid Pourroostaei.
other author:	Cheshmehzangi, Ali.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xvii, 190 p. :illustrations (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-981-99-6620-2
ISBN:	9789819966202

Big data analytics for smart transport and healthcare systems
Ardakani, Saeid Pourroostaei.

Big data analytics for smart transport and healthcare systems[electronic resource] /by Saeid Pourroostaei Ardakani, Ali Cheshmehzangi. - Singapore :Springer Nature Singapore :2023. - xvii, 190 p. :illustrations (chiefly color), digital ;24 cm. - Urban sustainability,2731-6491. - Urban sustainability..

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.

ISBN: 9789819966202

Standard No.: 10.1007/978-981-99-6620-2doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: HE147.6

Dewey Class. No.: 388.072

Big data analytics for smart transport and healthcare systems
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245 1 0 $a Big data analytics for smart transport and healthcare systems $h [electronic resource] / $c by Saeid Pourroostaei Ardakani, Ali Cheshmehzangi.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xvii, 190 p. : $b illustrations (chiefly color), digital ; $c 24 cm.
490 1 $a Urban sustainability, $x 2731-6491
505 0 $a 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.
520 $a 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.
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650 0 $a Medical care $x Data processing. $3 661308
650 1 4 $a Big Data. $3 3134868
650 2 4 $a Transportation Economics. $3 3592376
650 2 4 $a Health Care Management. $3 2054807
700 1 $a Cheshmehzangi, Ali. $3 3299387
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950 $a Mathematics and Statistics (SpringerNature-11649)