Partanaz, Damla

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Partanaz, Damla
D.,Partanaz
D. Partanaz
Damla, Partanaz
Partanaz, Damla
D.,Partanaz
D. Partanaz
Damla, Partanaz
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Misafir Öğr. Gör.
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Damla.partanaz@khas.edu.tr
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    Master Thesis
    A system model proposal in which human creativity meets with machine learning coping with smoking cravings
    (Kadir Has Üniversitesi, 2019) Partanaz, Damla; Soysal, Levent; Soysal, Levent
    Quitting smoking is hard, yet preventing relapse can be even harder. During those craving moments, urges to smoke may be the determining factor as to whether a smoking quitter will relapse or not. I researched the question "what could be done at craving moments in order to resist to smoke?" and spotted that the things that can be done/thought, at the craving moments instead of smoking, can vary from person to person and even from one craving moment to another craving moment. So I figured out that some people can come up with "instant creative solutions with some design thinking approach to these craving moments". Actually, people are already doing this even if some of them are doing this unconsciously and do not look at those acts as "solutions". And if they do, those solutions -real world information- are not preserved as computable data. Thereupon I pursued the question: "how can I computerize these experiences and enable the exchange of those solutions between smoking quitters in an optimum way?". Drawing inspiration from this question I designed a system model. The designed system will (1) take solutions from smoking quitters for each craving moment they encounter and pass without smoking, and (2) give the optimum solution from collected solutions to the ones who need a solution at their craving moment. These solutions are on the edge of the smoking quitters' imagination and creativity. And the given instant solution- the recommendation- will be (1) personalized and also (2) suitable with that craving moment's characteristic 'features'. These solutions can vary just as the answers to these questions (a) "where?", (b) "while doing what?", (c) "with whom?", (d) "which emotion state?", and (e)"when?". I researched two topics: smoking cravings, machine learning. Then to better understand the users, I conducted a qualitative exploratory approach and in-depth interviews. In the light of the analysis of these interviews, I designed the conceptual model of the system model and lastly for the system-user interface design and as a data collection solution I designed and developed a chatbot named "Drop-it-at-t0_bot" on Telegram.