Difference between revisions of "Machine Learning"

From Living Building Science

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Currently, we believe the only functionality necessary for the API is a request which reveals a prediction and perhaps number which indicates confidence of prediction based on footage pulled from the S3 instance.
 
Currently, we believe the only functionality necessary for the API is a request which reveals a prediction and perhaps number which indicates confidence of prediction based on footage pulled from the S3 instance.
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== Results ==
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Link to GitHub Repos: [https://github.com/SukheshNuthalapati/BeeSnap-ML Swarms] [https://github.com/quynh-trinh/varroa-detection Varroa] [https://github.com/jwu6492/bird-species-sounds-classification Birds]
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[https://docs.google.com/presentation/d/1ProCz75CExdtK0vIP--B4QrEkz9Q-1j_kdXF_rh8j7o/edit#slide=id.p Final Presentation]
  
 
== Past Semester Projects ==
 
== Past Semester Projects ==

Revision as of 14:47, 29 April 2021

Welcome to the Machine Learning Team Page! One of our past goals last semester was to create a bee recognition and classification project to complement our finished flower recognition model and eventually integrate both of these models with the bee classification project of the previous ML team as well as the BeeKeeper GO app. Once successfully integrated, our models could potentially be used to enhance the experience of the app and make it more educational for users, as the classification/identification of flowers will allow users to learn while taking pictures. In addition, another goal of last semester was to use data analysis and computer vision to detect the occurrence of swarms in bee hives and the contributing factors/warning signs that will lead to a swarm occurring. Our current goals include improving and expanding our past projects as well as potentially creating machine learning and analysis tools to assist the other sub-teams in Living Building Science with their goals.

Spring 2021 Semester Overview

We started off this semester by doing the LBS vip. As we were originally in Beesnap, our projects have been geared toward machine learning applications with the goal of integrating with the Beekeeper GO application. However since that is no longer the case, we have shifted our focus to more relevant issues for this VIP. With the biodiversity team's focus on birds and Dr. Weigel's bird sound data that she has, we decided that analyzing and classifying birds would be helpful. We initially reached our to the biodiversity team but they said that there would probably not be anything for us to collaborate on, so we decided to do the bird classifications on our own. Additionally, we are expanding the bee hive analysis to include varroa mite detection with the intention to monitor the health of the hives on campus.


With all these mini projects, our main focus shifts to accessibility so that users can use our tools for whatever needs they have from wherever they are. This is why for the time-series and varroa detection model we were so keen on creating a prototype API and hosting them on AWS. The APIs were created using Flask and will be hosted on an AWS EC2 instance: https://www.twilio.com/blog/deploy-flask-python-app-aws. In the future, we'd want to find a way to capture video footage of the beehives on campus and store them in an S3 instance, which the EC2 instance can pull from. For our frontend, we would create a dashboard which depicts the predictions of our varroa detection on beehive footage over time.


Currently, we believe the only functionality necessary for the API is a request which reveals a prediction and perhaps number which indicates confidence of prediction based on footage pulled from the S3 instance.

Results

Link to GitHub Repos: Swarms Varroa Birds

Final Presentation

Past Semester Projects

Fall 2020 Semester Poster

Spring 2020 Semester Poster


Team Members

Name Major Years Active
Sukhesh Nuthalapati Computer Science Spring 2020 - Present
Rishab Solanki Computer Science Spring 2020 - Present
Sneh Shah Computer Science Spring 2020 - Present
Daniel Tan Computer Science Fall 2020 - Present
Quynh Trinh Computer Science Fall 2020 - Present
Jonathan Wu Computer Science Spring 2020 - Present