Difference between revisions of "Team:NYMU-Taipei/HP/Gold-Forecasting Website"
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<h2 style="margin-top:30px; margin-bottom:10px; line-height: 24px;">How does it work?</h2> | <h2 style="margin-top:30px; margin-bottom:10px; line-height: 24px;">How does it work?</h2> | ||
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| + | <p style="font-size:16px; white-space:pre-wrap;">Step 1: Update climatological data | ||
| + | Connect to Agriculture weather website. Crawl and store the newest climatological data. | ||
| + | Step 2: Data process and making prediction | ||
| + | Grasp necessary information from html, and build up several SVM and RandomForest classifiers for prediction. Merge the outcome with the web frame. | ||
| + | Step 3: upload to FTP | ||
| + | Upload the html files to FTP. | ||
| + | <i>----icon credit: Freepik, Wissawa Khamsriwath, Madebyoliver, Gregor Cresnar</i> | ||
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Revision as of 21:10, 18 October 2016
We have a hardware, a software for Ios-i-GEM, yet …
Besides the fungal killing switch and the functional prototype that help reduce concerns over GMO, we wonder what else in iGEM we can do as social practice to really engage in growers’ life and help them diminish threats posed by those pests. So far in our project, the entomogenous fungus provides a biological, no-harm attempt to eradicate the pests, which is one of the most important components of our idea. The prototype makes applying these genetically-engineered fungi practical and perhaps better. Now that we have a software and a hardware, what can we do more for the growers?
The answer came to the app, a well-design, thoughtful and realistic app. Growers, as well as government officials can simply check the “Taiwan Pest Prediction web”, where we provide 4 common pests in Taiwan with time scale from 1 day to 3 days. We offer the predicted pest scale from 0~16, 16~64, and above 64, 4 ranges in total. We convert prediction question into classification question, by building numerous classifiers and perform voting, we can get the prediction that most classifiers agree to. More important, we put all the code on github as open source, everyone and from every country can take advantage of our efforts to establish a better and powerful prediction program.
Collaboration
This app is the outcome of our collaboration with NCTU. We first developed our idea of construct such a web app for social practice. In August, when we have done the web crawling part, we participated in Asia-Pacific iGEM conference hosted by NCKU, and met our friends from NCKU. It was incredible to meet friends with similar ideas and, most importantly, we decided to collaborate at that time. From then, we had several face-to-face talks in FB Messenger and frequent calls.
The attribution is listed as following:
NCTU
Provide us with expertise in FTP, web crawling, and the concept in pipeline
Streamline code, making it more readable and understandable.
Provide the idea of open source, and we did put our code on github.
NYMU
First come up with this idea
Write program for web-crawling, date processing, machine learning and FTP uploading.
Establish FTP host and write UI website.
How does it work?
Step 1: Update climatological data Connect to Agriculture weather website. Crawl and store the newest climatological data. Step 2: Data process and making prediction Grasp necessary information from html, and build up several SVM and RandomForest classifiers for prediction. Merge the outcome with the web frame. Step 3: upload to FTP Upload the html files to FTP. ----icon credit: Freepik, Wissawa Khamsriwath, Madebyoliver, Gregor Cresnar
