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.
Table of Content
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.
About the author
Dr. Saeid Pourroostaei Ardakani is a Senior Lecturer in Computer Science at the University of Lincoln, UK. His research and teaching expertise centers on smart and adaptive computing and/or communication solutions to build collaborative/federated (sensory/feedback)systems in Internet of Things (Io T) applications and cloud environments. Saeid is also interested in (ML-enabled) Big Data processing and analysis applications. Saeid formerly worked at the University of Nottingham (China campus), 2019-2023, and ATU, 2015-2018 as an Assistant Professor in Computer Science. He received his Ph D in Computer Science from the University of Bath focusing on data aggregation routing in Wireless Sensor Networks. His subject specialisms are: Internet of Things, Big Data Analysis, Distributed and Collaborative Computing, Sensory Systems, and Educational Technology.
Prof. Ali Cheshmehzangi is the World’s 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 Architecture and Urban Planning, Director of the Center for Innovation in Teaching, Learning, and Research, and Advisor to the school’s 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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