Added relevant information

Author: Ttiki

main.tex

@@ -77,11 +77,11 @@ \section{Conceptual implementation}
 
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 \section{Acknowledgment}
-I would like to thanks all the faculty for their welcome, help and support throughout this year. Helping me both on my paper and on my work at the lab. I would like to personally thanks Dr. Ernesto Exposito, Dr. Mamadou Lamine Gueye and Dr. Houssam Kansso as well as PhD. student Nicolas Evain for their help and welcome. A special thanks goes to Dr. Mamadou Lamine Gueye for following me as a tutor for this last year of Master.
+I would like to thank all the faculty for their welcome, help, and support throughout this year. Helping me both on my paper and on my work at the lab. I would like to personally thanks Dr. Ernesto Exposito, Dr. Mamadou Lamine Gueye and Dr. Houssam Kansso as well as PhD. student Nicolas Evain for their help and welcome. A special thanks go to Dr. Mamadou Lamine Gueye for following me as a tutor for this last year of Master.
 
-I would also like to thanks the entirety of the 2023-2024 SIGLIS and Industry 4.0 promotion with whom I've been able to share great times, knowledge and some fun moments during the year. A special thanks goes to the SIGLIS promotion for supporting me for two year, especially my flatmates Dorian Cazabat, Lise Laville and Gabriel Das Neves Rodrigues. 
+I would also like to thank the entirety of the 2023-2024 SIGLIS and Industry 4.0 promotion with whom I've been able to share great times, knowledge and some fun moments during the year. A special thanks go to the SIGLIS promotion for supporting me for two year, especially my flatmates Dorian Cazabat, Lise Laville and Gabriel Das Neves Rodrigues. 
 
-Finlay, I would like to thanks my friends and family for their support, their motivation and for being here in the time of need.
+Finlay, I would like to thank my friends and family for their support, their motivation and for being here in the time of need.
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sections/conceptual_proposal.tex

@@ -68,5 +68,12 @@ \subsection{Data workflow}
 
 \subsection{Validation and Expected Outcomes}
 We anticipate that this workflow will yield a robust model capable of identifying excellent students. We will gauge the efficiency of our model through rigorous validation techniques such as \acrfull{roc}, \acrfull{pca}, etc. to ensure the reliability of our predictions. 
+To do this evaluation process, we will organize our datasets' intro training and testing sets. When training our model and test it for the first few times, we will feed it previous years' candidate profile and will examine the output of our model (i.e. potential excellence student) and compare the list given by it to the result of the entire class. This will help us determine which student could be classified as excellence and see if our model sends us back their profile or missed on some, or  to determine if our model as correctly found or missed on excellence.  
+We will test our models with different year to determine if this model could be implemented and use on \textit{the field} to help institution with the registration problem.
+
+\subsection{Usage on the field}
+We would like to explain our vision on how this could be implemented in universities and institution if the model proves to be viable and efficient. Such a system would pose an ethical problem and would need to be constantly measured, evaluated and should only be administrated by a third party without interest. It would be an impartial entity without any connection with the institution. 
+This model should not be used to exclude students, but should only point out the best result for each formation to reduce dropout rates throughout the country. However, other factors outside this model should be studied by administration for each institution for each formation to correctly select student not only based on the result indicated by our result but also taking into account these other factors. Moreover, any person using this model should not understand which information is used and how by the model itself. People selecting student's should not have anything to do with the registration data, the feeding process and the output of the model. To make this process as imperial as possible, this people should only receive the output without prior knowledge and the resource they usually get to chose student to do their selection.  
+Finally, as said earlier in this paper to protect students, no personal information and their identity (name, surname, country of origin, etc.) should not be involved in the process and the information shared should not allow anyone to trace back the student based on the information given.That is why having compartmentalisation is really important if this system should be put in place in any institution.
 
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