scholarly journals Filtering big data from social media – Building an early warning system for adverse drug reactions

2015 ◽  
Vol 54 ◽  
pp. 230-240 ◽  
Author(s):  
Ming Yang ◽  
Melody Kiang ◽  
Wei Shang
Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.


2019 ◽  
Vol 53 (2) ◽  
pp. 221-240
Author(s):  
Sean Foley

Abstract“Social media,” Saudi artist Abdullah al-Shehri (known as Shaweesh) observes, is the “best tool we have available to showcase and express our art,” because it allows millions of Saudis to share and comment on a given work of art simultaneously. Building on this insight, this essay argues that Saudi artists, who have among the largest followings on the country's social media, have used the online public sphere to build a new social movement. They have adopted a role akin to Antonio Gramsci's concept of organic intellectuals – namely, men and women who are not part of the traditional intellectual elite, but who, through the language of culture, articulate feelings and experiences the masses cannot easily express. To paraphrase Ezra Pound, Saudi artists are the “antennae” of the kingdom's society, whose work is not “mere self-expression,” but, in the words of Marshall McLuhan, the “distant early warning system that can always be relied upon to tell the old culture what is beginning to happen to it.” As a leading Saudi artist Abdulnasser Gharem observed in June 2019, “people need to listen to the artist.”


2021 ◽  
Author(s):  
Guangxin Zhang ◽  
Liying Zhao ◽  
Dongliang Qiao ◽  
Ziwen Shang ◽  
Rui Huang

2017 ◽  
Vol 17 (10) ◽  
pp. 1713-1723 ◽  
Author(s):  
Emanuele Intrieri ◽  
Federica Bardi ◽  
Riccardo Fanti ◽  
Giovanni Gigli ◽  
Francesco Fidolini ◽  
...  

Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.


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