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Titlebook: Big Data Analytics for Smart Transport and Healthcare Systems; Saeid Pourroostaei Ardakani,Ali Cheshmehzangi Book 2023 The Editor(s) (if a

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發(fā)表于 2025-3-21 17:03:56 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Big Data Analytics for Smart Transport and Healthcare Systems
影響因子2023Saeid Pourroostaei Ardakani,Ali Cheshmehzangi
視頻videohttp://file.papertrans.cn/186/185615/185615.mp4
發(fā)行地址Covers big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems.Remains as the only book dealing with data analytic problems in urban sustainability a
學科分類Urban Sustainability
圖書封面Titlebook: Big Data Analytics for Smart Transport and Healthcare Systems;  Saeid Pourroostaei Ardakani,Ali Cheshmehzangi Book 2023 The Editor(s) (if a
影響因子.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..
Pindex Book 2023
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Big Data for Social Media Analysis During the COVID-19 Pandemic: An Emotion Analysis ?Based on Influhe COVID-19?pandemic. The study also investigates the relationship between COVID-19?trends or topics and public sentiments?on social networks. Machine learning?is used to verify the correlation between emotion on social media and the COVID-19?pandemic trends. The study concludes with a prediction mo
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Robert Fantina,Andriy Storozhuk,Kamal Goyal, reaching an accuracy?of 99.89%. The preprocessing stage is proven to be vital for the performance of the LSTM?model. As a result, models using our preprocessed nine features are much more accurate on this freight transportation prediction task. Discussions are also raised to understand better frei
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SEO Hub: Utilities and Toolsets,red with four classifiers, including Multilayer Perceptron, Decision Tree, Multinomial Logistics Regression, and LR one versus rest. According to the results, the Random Forest?model outperforms the benchmarks?regarding prediction accuracy. The findings of this chapter help optimise prediction accur
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https://doi.org/10.1007/978-1-4842-1766-5he COVID-19?pandemic. The study also investigates the relationship between COVID-19?trends or topics and public sentiments?on social networks. Machine learning?is used to verify the correlation between emotion on social media and the COVID-19?pandemic trends. The study concludes with a prediction mo
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Big Data-Enabled Time Series Analysis for Climate Change Analysis in Brazil : An Artificial Neural Nate data and analyze the changing climate trends in the same province. An artificial neural network is established as the model in this project to implement this objective. The performance shows that this model can complete this classification task.
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Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approache experiment results indicate that self-organized map model with the TF-IDF extraction method can achieve the best clustering accuracy. Moreover, the optimized model can have great potential to handle large-scale data in real practice.
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