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== **AI For Behavioral Discovery**\\

**Team**: Adarsh Narayanan^UG^, Benjamin Yu^UG^, Elias Xu^HS^, Shreyas Musuku^HS^

**Advisors**:  Dr. Richard Martin and Dr. Richard Howard


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**Project Description & Goals**:\\

The past 40 years has seen enormous increases in man-made Radio Frequency (RF) radiation. However, the possible small and long term impacts of RF radiation are not well understood. This project seeks to discover if RF exposure impacts animal behaviors. In this experimental paradigm, animals are subject to RF exposure while their behaviors are video recorded. Deep Neural Networks (DNNs) are then tasked to correctly classify if the video contains exposure to RF or not. This uses DNNs as powerful pattern discovery tools, in contrast to their traditional uses of classification and generation. The project involves evaluating the accuracies of a number of DNN architectures on pre-recorded videos, as well as describe any behavioral patterns found by the DNNs.


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**Weekly Progress**:\\

**Week 1** - https://docs.google.com/presentation/d/1oZaaNaLMyTjMO3_yzCU-_ruVrwQr0rTuVrc27HG6WAo/edit?usp=share_link
   - Created synthetic data to train a model to perform binary classification based on linear vs curved path (due to presence of distortion field) of a single bee flight to home.
       - Gathered further insight to how data preparation and model training can work for the real dataset.

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**Week 2** - https://docs.google.com/presentation/d/1BebSXbCDB7Z3yCCVYtAP1WkcEWYFKNNOK1TcxBEtSrs/edit?usp=share_link

[[Image(Untitled drawing.png​, 20%)]] [[Image(dummy.0.0.gif​​, 20%)]] [[Image(dummy.8.0.gif​​, 20%)]]

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**Week 3** - https://docs.google.com/presentation/d/1gv2Mb9vWc3VottF-gdk-rGUH5jPb285WYxMsCsw6OnI/edit?usp=share_link

1 frame per sample:

[[Image(Figure_1.png, 20%)]] [[Image(Figure_2.png, 20%)]]

4 frames per sample:

[[Image(Figure_1 (1).png​, 20%)]] [[Image(Figure_2 (1).png​, 20%)]]

Confusion matrices (bias towards class 1, where field is on); Overfitted (expected because the scenario the data is trying to emulate is oversimplified for the complex model): 

[[Image(Screenshot 2024-06-13 at 2.18.28 PM.png, 20%)]] [[Image(Screenshot 2024-06-13 at 2.18.47 PM.png, 20%)]]

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**Week 4** - https://docs.google.com/presentation/d/1v5lVYUB6YdxdCAED8_bN5KRCGSDFeQI9UTT7IYCR_as/edit?usp=sharing                                                    

Calculated a hypothetical decision boundary

If our hypothetical recall ~= actual recall => hypothetical decision \\
boundary might actually represent the model's decision boundary

Class 0 hypothetical recall (between curves / total actual class 0): 0.6070519810977826\\
Actual Class 0 Recall (tested among 500 samples): ~0.896\\
                                                                                                 
[[Image(unnamed.png​, 20%)]]​ 
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