Behavior Analysis-Based IoT Services For Crowd Management

With the world population growing exponentially reaching 7.8 billion people in 2020, the issue of crowd management has become more difficult especially when the situation requires social distancing (e.g. due to COVID-19). The Internet of Things (IoT) technology can help in tackling such issues. In this article, we propose a behavior analysis-based IoT services architecture for crowd management. We propose to use a behavior analysis approach based on using generative model as Hidden Markov Model to help crowd managers to make good decisions in invoking IoT services. The proposed approach is based on sectioning video segments captured from surveillance cameras of locations that require crowd management into spatio-temporal flow-blocks for marginalization of arbitrarily dense flow field. Then, each flow-block is classified as normal and abnormal. To demonstrate our approach, we used a real case study where crowd management is required namely, Muslim's pilgrimage (i.e. Hajj and Umrah), where real dataset is used for experimenting. The results of the experiments we have conducted are promising in real-time performance. Such results are expected to compare favorably to those found in the literature by other researchers.

Users opinion and emotion understanding in social media regarding COVID-19 vaccine

Online social platforms or social platforms such as Twitter, Facebook and Instagram have become popular platforms for a public discussion about social topics. Recent studies show that there is a growing tendency for people to talk about COVID-19 pandemic in these online channels. The rapid growth of the infected cases by COVID-19 pandemic makes a lots of anxiety and fear among people. With the recent released of Pfizer vaccine, people start posting a lot of rumors regarding the safety concerns of the vaccine, especially among the elderly people. The aim of this study is to bring out the fact that tweets containing all pertinent details about the COVID-19 vaccine and provides an analysis and understanding of users emotions regarding the recent release of COVID-19 vaccine. This study starts with the collection of tweets related to COVID-19 vaccine and then cleaning the dataset from redundant tweets. In this study, we use Twitter API and Web Scraping techniques to obtain a sample of 50,000 tweets talking about COVID-19 vaccine.Further, The analysis of users emotions is achieved by manually labeling and classifying the tweets to either positive or negative. Then, a deep learning based model is used to train the data and classify the people opinion about COVID-19 vaccine. The experimental results illustrate that high percentage of people have shown a positive attitude towards COVID1-19 vaccine. The proposed method is validated over Twitter datasets and the results also demonstrate that use of deep learning classifier can successfully improve the accuracy of people emotions analysis with an accuracy up to 98% for training set and the accuracy for testing set is 73%.

Spatial Impressions Monitoring during COVID-19 Pandemic Using Machine Learning Techniques

During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. Based on this study, various public service messages can be disseminated, and necessary steps can be taken to manage the scourge. There has already been a lot of work conducted in several languages, but little has been conducted on Arabic tweets. The primary goal of this study is to analyze Arabic tweets about COVID-19 and extract people's impressions of different locations. This analysis will provide some insights into understanding public mood variation on Twitter, which could be useful for governments to identify the effect of COVID-19 over space and make decisions based on that understanding. To achieve that, two strategies are used to analyze people's impressions from Twitter: machine learning approach and the deep learning approach. To conduct this study, we scraped Arabic tweets up with 12,000 tweets that were manually labeled and classify them as positive, neutral or negative feelings. Specialising in Saudi Arabia, the collected dataset consists of 2174 positive tweets and 2879 negative tweets. First, TF-IDF feature vectors are used for feature representation. Then, several models are implemented to identify people's impression over time using Twitter Geo-tag information. Finally, Geographic Information Systems (GIS) are used to map the spatial distribution of people's emotions and impressions. Experimental results show that SVC outperforms other methods in terms of performance and accuracy.