Big Data Analytics for Smart Urban Systems aims to introduce Big data solutions for urban sustainability smart applications, particularly for smart urban systems. It focuses on intelligent big data which takes the benefits of machine learning to analyse large and rapidly changing datasets in smart urban systems. The state-of-the-art Big data analytics applications are presented and discussed to highlight the feasibility of big data and machine learning solutions to enhance smart urban systems, smart operations, urban management, and urban governance. The key benefits of this book are, (1) to introduce the principles of machine learning-enabled big data analysis in smart urban systems, (2) to present the state-of-the-art data analysis solutions in smart management and operations, and (3) to understand the principles of big data analytics for smart cities and communities.
Endorsements
Over the many years of collaboration between academia and industry, we noticed the common language is big data; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science.
- Tian Tang, Independent Researcher, China
Big Data Analytics is a fascinating research area, particularly for cities and city transformations. This book is valuable to those who think vigorously and aim to act ahead.
- Li Xie, Independent Researcher, China
For urban critiques, knowledge trains aspiring opportunities toward outstanding manifestations. Smartness has evolved or/ advanced rambunctious & embracing realities along (with) novel directions and nurturing integrated city knowledge.
- Aaron Golden, SELECT Consultants, UK
Chapter
1. Introduction and editorial to the book.- Part
1. Big Data and
Healthcare applications.
Chapter
2. The Correlation of Vaccination Progress
and World-Wide immunity against COVID-19.
Chapter
3. A Big Data Solution for
Healthcare Infrastructure Development and the COVID-19 Prevention.
Chapter
4. A Federated Regression Analysis on Worldwide Mobility Trends During
COVID-19 Early Stage.- Part
2. Big Data Solutions for Transportation
Management.
Chapter
5. A Big Data approach for predicting Urban
Transportation Cost.
Chapter
6. An Optimised Big Data Enabled Classification
for Flight Status.
Chapter
7. A Time-series Classification of Freight
Transport Data.- Part
3. Big Data Analysis Applications in Economy.
Chapter
8. A Multi-level Classification for Electronic Device Price.
Chapter
9. An
Adaptive Feature Selection for Google App rating using Big Data solutions.-
Chapter
10. A Time-series Pattern for Stock Market Prediction.- Part
4. Big
Data Applications forSocial Networks.
Chapter
11. An Optimized Clustering
Model for Healthcare Sentiments on Twitter.
Chapter
12. How COVID-19
Pandemic Influences Social Networks?.
Chapter
13. Google Stores User
Feedback Mining.- Part 5.- Big Data Applications for Urban Environment
Planning.
Chapter
14. Analysis of the Impact of Green Infrastructure on
Carbon Monoxide Reduction.
Chapter
15. Big Data Analysis for UAV itinerary
planning in Multi-Dimension Environments.
Chapter
16. Big Data Enabled Smart
Home Applications a Carbon-zero World.
Chapter
17. Conclusions on Big Data
Applications for contemporary and future urban sustainability research and
practice.
Saeid POURROOSTAEI ARDAKANI currently works as a Senior Lecturer in Computer Science at the University of Lincoln, UK. He is also an associated academic member of Lincoln Centre for Autonomous Systems (L-CAS), and has formerly worked at the University of Nottingham (UNNC) and Allameh Tabatabai University (ATU) as an Assistant Professor in Computer Science, member of the Next Generation Internet of Everything Laboratory (NGIoE) and Artificial Intelligent Optimisation Research group, and head of ATU-ICT center. He received his PhD in Computer Science from the University of Bath focusing on data aggregation routing in Wireless Sensor Networks. Saeids research and teaching expertise centres on smart and adaptive computing and/or communication solutions to build collaborative/federated (sensory/feedback)systems in Internet of Things (IoT) applications and cloud environments. He is also interested in (ML-enabled) Big Data processing and analysis applications. Saeid has published more than 60 scholarly articles in reputed international journals and peer-reviewed conferences. Ali CHESHMEHZANGI is the Worlds top 2% field leader, recognised by Stanford University. He has recently taken a senior leadership and management role at Qingdao City University (QCU), where he is a Professor in Urban Planning, Director of the Center for Innovation in Teaching, Learning, and Research, and Advisor to the schools international communications. Over 11 years at his previous institute, Ali was a Full Professor in Architecture and Urban Design, Head of the Department of Architecture and Built Environment, Founding Director of the Urban Innovation Lab, Director of Center for Sustainable Energy Technologies, and Director of Digital Design Lab. He was Visiting Professor and now Research Associate of the Network for Education and Research on Peace and Sustainability (NERPS) at Hiroshima University, Japan. Ali is globally known for his research on urban sustainability. So far, Ali has published over 300 journal papers, articles, conference papers, book chapters, and reports. To date, he has 15 other published books.
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