Inspiration

With the problem of particular endangered species declining in number with each day, we came up with this idea of tracking these species and analyze the threats to them in their natural habitat which may be contributed due to various environmental factors like pollution, so that we can take necessary steps to ensure their safe being.

What it does

It uses blockchain combined with predictive analysis towards building an eco-saving platform.

How we built it

We solve the problem here by using Blockchain technology, where we treat each species entity as a block. A blockchain is a sequential list of blocks which are secured using cryptography. We use the CryptoJS library to provide SHA-256 encryption to the blocks where each block contains information like location, temperature and other attributes related to the species entity so that we can track it for safety purposes. We also analyze the risk factor to the particular species entity using a predictive model which predicts on basis of various external factors whether there exists a risk to the species.

Challenges we ran into

Setting up peer to peer connection using Blockchain was a bit tricky part. Implementing and Data collection for deep learning model and integrate with Blockchain technology was challenging.

Accomplishments that we're proud of

What we learned

We learned about relating blockchain and applying predictive model towards the environmental cause of saving endangered species

What's next for Salvation

Gather more data for all species, create more robust blockchain and deep learning model and making more public involvement to create more awareness.

Built With

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Updates

posted an update

Achieved Deep Learning model accuracy up to 93.50% while training data and 98.63% while testing data.

Reference Data:

Epoch 148/150 292/292 [==============================] - 0s 280us/step - loss: 0.1562 - acc: 0.9281 Epoch 149/150 292/292 [==============================] - 0s 272us/step - loss: 0.1557 - acc: 0.9315 Epoch 150/150 292/292 [==============================] - 0s 282us/step - loss: 0.1541 - acc: 0.9349

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