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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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Big Data for Social Media Analysis During the COVID-19 Pandemic: An Emotion Analysis  Based on Influyears, news and social media were covered with information, updates, and daily/regular reports on the COVID-19 pandemic. When face-to-face activities were restricted, people started to use social networks more than before. The situation led to the empowerment of digital media, such as social media a
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Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approache social events, e-commerce products, healthcare, etc. This chapter proposes a best-fitted clustering method to classify sentiment samples related to healthcare topics. Thus, we examine other clustering models with keyword extraction methods on the real healthcare datasets collected from Twitter. Th
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Saeid Pourroostaei Ardakani,Ali CheshmehzangiCovers 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
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Urban Sustainabilityhttp://image.papertrans.cn/b/image/185615.jpg
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Introducing Relational Political Analysise 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
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Robert Fantina,Andriy Storozhuk,Kamal Goyaltive 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
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Robert Fantina,Andriy Storozhuk,Kamal Goyalontribute 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.
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