Machine Learning Based Fitness Dataset for Fog-based Inclusion Analysis

Published: 9 May 2019| Version 1 | DOI: 10.17632/pgjdr7s9ky.1
Contributor:
Simar Preet Singh

Description

Machine Learning Based Fitness dataset is computed using the matrix <FIW,M,G,C> where FIW is the fitness index, M is the modularity, G is the value of geospatial support score and C is the value of call data support score. HealthRiskData_Ver_3 (http://dx.doi.org/10.17632/8y7628b96z.2) dataset is being referred to perform the analysis. Qualifying criteria depends upon values obtained from these four parameters. Using LDA model, identification of the participant is computed (i.e. qualified (Q) or not-qualified (NQ)). Also, this dataset represents the RegistrationID, SubscriberID, Date, CallEvent and Mobile; which represents the details of the respective participant.

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Institutions

Thapar University

Categories

Informal Economy, Machine Learning, Learning, Computing, Machine, Fog Computing

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