deletions | additions       diff --git a/section_Ongoing_work_The_CDR__1.tex b/section_Ongoing_work_The_CDR__1.tex  index 81c53c3..1753b40 100644  --- a/section_Ongoing_work_The_CDR__1.tex  +++ b/section_Ongoing_work_The_CDR__1.tex 
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\]    \subsection{Model Attributes}  Setting the five month timespan limitation, CDRs are being processed to extract features at the user level.   For every individual, Setting  the top ten most used antennas five month timespan limitation, CDRs  are logged along with being processed to extract features at  the amount user level. The quality  ofuse in each one. Users are tagged as 'epidemic' if their home antenna is in  the classification will rely heavily on  the risk area and 'exposed' if any of ability to characterize  the antennas used are in user and his communication pattern as differentiating as possible. In general,  the epidemic zone. A features constructed reflect calling and  mobility diameter is also processed from the radius of the convex hull defined patterns. Differentiating  by the user's logged antennas. This length is representative of time they were done during  the radius of influence of that individual week  and we are expecting to see a high correlation between a high mobility-diameter and high long-term movements of people. tagging the action or object if it is epidemic.  Calling information is aggregated and binned according to the hour The model's first version consists  of the day and during the weekends. For every user, the duration and individual count of these calls are processed, differentiating between calls made to vulnerable and non-vulnerable users. following features:  Finally, \begin{itemize}  \item Antennas: The top ten most used antennas with the number of uses. From this, users were tagged as 'epidemic' if their home antenna is in the epidemic area and 'exposed' if any of the ten antennas logged is in the risk area.  \item Mobility diameter: The user's logged antennas define a convex hull in space and the radius of the hull is taken to be as the mobility diameter. This length is representative of the area of influence of that individual. We are expecting that these feature be correlated with long-term migrations.  \item Neighbours: From the  social information processed graph built  from the data include CDRs we extracted  the total amount count  of epidemic, exposed neighbours in the communicaction graph  and the  total neighbours count of epidemic neighbors.   \item Calls: The total time and count of calls made during the five month period is aggregated per user. This information is also segmented according to the hour of the day  thatany given user interacts with over  the timeperiod. calls were made and whether they were made during the weekends. Special care was taken with calls placed to and from vulnerable users and aggregated accordingly.  \end{itemiize}  \subsection{Supervised Algorithms}  Based on