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

infarct

Obituary

想象

https://doi.org/10.1007/978-1-4302-2992-6ate 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.

Costume

WCF RIA Services and Silverlight for Mobilee 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.

misanthrope

干涉

The Role of Big Data Analytics in Urban Systems: Review and Prospect for Smart Transport and Healthe current information age. The chapter starts with an overview of Big Data analytics for urban systems and follows the discussions from the sector-based perspectives.It then explores Big Data Applications (BDA) in two key areas of smart transportation and healthcare, particularly in cities and as pa

Geyser

Big Data Analysis for an Optimised Classification for Flight Status : Prediction Analysis Using Machtive impacts of such delays. Machine learning-enabled Big data solutions have been widely utilized in recent studies to anticipate aircraft delays. They need a data pre-processing to understand and grasp the relevance of each data attribute. The results of data attribute relevance are used to filter

mercenary

On-Board Unit Freight Transport Data Analysis and Prediction: Big Data Analysis for Data Pre-processontribute to society’s efficient production and development, particularly in optimising urban systems. This chapter aims to build a precise model to predict the number of freight vehicles in future timestamps based on historical data provided On-Board Unit (OBU) datasets in Belgium’s road networks.

MORT

Data-Driven Multi-target Prediction Analysis for Driving Pattern Recognition: A Machine Learning Appers, such as the car’s speed, traffic, weather, and road status. This article investigates the correlations between driving attributes and proposes a multi-target prediction model to recognise driving patterns. For this, a pre-processing data approach, including Pearson’s Correlation Coefficient, Qu

Restenosis