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Implementation of Data Mining and Machine Learning Techniques in the Context of Disaster and Crisis Management

Authors(1) :-Smita Deogade

The explosive growth in social system content proposes that the biggest "sensor organize" yet may be human. Broadening the participatory sensing model, this undertaking investigates the possibility of using social systems as sensor systems, which offers ascend to an intriguing dependable sensing issue. In this issue, people are spoken to by sensors (data sources) who every so often mention objective facts about the physical world. These perceptions might be valid or false, and thus are seen as twofold claims. The dependable sensing issue is to decide the accuracy of detailed perceptions. From an organized sensing point of view, what makes this sensing issue detailing diverse is that, on account of human members, not exclusively is the reliability of sources normally obscure yet in addition the first data provenance might be uncertain. People may report perceptions made by others as their own. The commitment of this paper lies in building up a model that considers the effect of such data sharing on the diagnostic establishments of dependable sensing, and insert it into an instrument called Apollo that utilizations Twitter as a "sensor organize" for watching events in the physical world. Assessment, utilizing Twitter-based contextual analyses, demonstrates great correspondence between perceptions regarded adjust by Apollo and ground truth.
Smita Deogade
Humans as Sensors, Social Sensing, Data Reliability, Uncertain Data Provenance, Maximum Likelihood Estimation, Expectation Maximization
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Publication Details
  Published in : Volume 4 | Issue 8 | May-June 2018
  Date of Publication : 2018-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 46-49
Manuscript Number : IJSRST1845152
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Smita Deogade, "Implementation of Data Mining and Machine Learning Techniques in the Context of Disaster and Crisis Management", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.46-49, May-June-2018
URL : http://ijsrst.com/IJSRST1845152