{"id":61,"date":"2016-08-29T11:40:44","date_gmt":"2016-08-29T15:40:44","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=61"},"modified":"2025-04-15T15:46:23","modified_gmt":"2025-04-15T19:46:23","slug":"project-ideas","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2016\/08\/29\/project-ideas\/","title":{"rendered":"Project Ideas"},"content":{"rendered":"

<!–Idea 1:<\/strong><\/em><\/p>\n

Developing some sort of hardware\/software combination that would allow for monitoring of washers and dryers on Earlham’s campus. I would then create an app of some sort so that students could go on to the app and be able to 1) get notifications when a machine is done 2) look to see which machines are available so that they do not have to make the trek to their closest washing machine only to find out that that the machines are all taken. \u00a0Right now, my idea for the hardware is just a machine that is plugged into the outlet at the same spot as the machine, kind of like an adaptor, and will broadcast a signal telling whether the machine is running or not. \u00a0The software will then simply read the broadcast to determine if a machine is running or not.
\n–>
\nIdea 1:<\/strong><\/em><\/p>\n

 <\/p>\n

Developing a piece of software that is able to perform population estimation by scraping information from popular sites. I would most likely scrape instagram posts with tags, twitter posts that mention a location, facebook photos with location tags, and also if possible recent google searches regarding the location. \u00a0For instance, if 1000 people in the last day had searched on google, food locations on Miami beach, it is a good predictor that a high proportion of those 1000 people are visiting Miami beach in the near future. \u00a0Then a person would use my piece of software to say, search how busy Miami beach on that day, and a predictor of how busy it will be in the near future. This approach would require a lot of probability into the calculations.<\/p>\n

 <\/p>\n

Idea 3:<\/strong><\/em><\/p>\n

 <\/p>\n

This idea would be the same concept as idea 2, but instead of scraping information, i would obtain the location information (or create fake data as charlie suggested) and process that data to then provide a more accurate depiction of the population at a location at any given moment. \u00a0The predictive capability will then be based on past data that i had collected.<\/p>\n","protected":false},"excerpt":{"rendered":"

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