Big Data Analytics for Smart Transport and Healthcare Systems

Saeid Pourroostaei Ardakani; Ali Cheshmehzangi

doi:10.1007/978-981-99-6620-2

Big Data Analytics for Smart Transport and Healthcare Systems

Saeid Pourroostaei Ardakani
, Ali Cheshmehzangi

Research output: Chapter in Book/Conference proceeding › Book Chapter › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Urban Sustainability
Publisher	Springer
Pages	1-184
Number of pages	184
DOIs	https://doi.org/10.1007/978-981-99-6620-2
Publication status	Published - 2023
Externally published	Yes

Publication series

Name	Urban Sustainability
Volume	Part F3693
ISSN (Print)	2731-6483
ISSN (Electronic)	2731-6491

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Free Keywords

Big Data Analytics
Data Interpretation
Data Modeling
Data Science
Healthcare
Healthcare Optimisation
Machine Learning Techniques
Smart Applications
Transport Systems
Transportation Management

ASJC Scopus subject areas

Geography, Planning and Development
Transportation
Waste Management and Disposal
Urban Studies

Access to Document

10.1007/978-981-99-6620-2

Cite this

@inbook{a1fc9795e61b4fb492f1426c9ef8284c,

title = "Big Data Analytics for Smart Transport and Healthcare Systems",

abstract = "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.",

keywords = "Big Data Analytics, Data Interpretation, Data Modeling, Data Science, Healthcare, Healthcare Optimisation, Machine Learning Techniques, Smart Applications, Transport Systems, Transportation Management",

author = "Ardakani, \{Saeid Pourroostaei\} and Ali Cheshmehzangi",

note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.",

year = "2023",

doi = "10.1007/978-981-99-6620-2",

language = "English",

series = "Urban Sustainability",

publisher = "Springer",

pages = "1--184",

booktitle = "Urban Sustainability",

}

TY - CHAP

T1 - Big Data Analytics for Smart Transport and Healthcare Systems

AU - Ardakani, Saeid Pourroostaei

AU - Cheshmehzangi, Ali

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Big Data Analytics

KW - Data Interpretation

KW - Data Modeling

KW - Data Science

KW - Healthcare

KW - Healthcare Optimisation

KW - Machine Learning Techniques

KW - Smart Applications

KW - Transport Systems

KW - Transportation Management

UR - https://www.scopus.com/pages/publications/105002172750

U2 - 10.1007/978-981-99-6620-2

DO - 10.1007/978-981-99-6620-2

M3 - Book Chapter

AN - SCOPUS:105002172750

T3 - Urban Sustainability

SP - 1

EP - 184

BT - Urban Sustainability

PB - Springer

ER -

Big Data Analytics for Smart Transport and Healthcare Systems

Abstract

Publication series

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this