Big Data Analytics for Smart Transport and Healthcare Systems / / Saeid Pourroostaei Ardakani and Ali Cheshmehzangi

• Record Nr.: UNINA9910768440903321
• Autore: Pourroostaei Ardakani Saeid
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]; ©2023
• ISBN: 981-9966-20-5
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (197 pages)
• Collana: Urban Sustainability Series
• Disciplina: 005.7
• Soggetti: Big data; Medical care - Data processing; Transportation - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.