core VS OpenCV

Website: terra.money

TerraUSD (UST) was launched in 2020. Like NuBits and other seigniorage algorithmic stablecoins, UST sought to peg one token by moving volatility to a separate token, in this case LUNA, which was also the native token of the Terra blockchain. Users could swap 1 UST for $1 of LUNA and vice versa. When the price of UST rose above $1, holders could profit by swapping $1 of LUNA for 1 UST (which would be worth more than $1), increasing supply and pushing down the price of UST. When UST was below $1, users could profit by swapping 1 UST for $1 of LUNA, reducing the supply of UST. This worked relatively well until a series of events led to confidence in the system collapsing, meaning there was no longer adequate demand for LUNA to stabilize UST. Its failure in May 2022 wiped out tens of billions of dollars of value from the Terra ecosystem, catalyzed the wider crypto bear market, and brought down several large organizations with exposure to it, including crypto hedge fund 3AC and lending company Celsius in the process.

Terra's Official Twitter account post, a newly created account, and a bio that reads "Building a new standard for DAO on Terra"

A final example of a token is a "stablecoin". A stablecoin is a token which is kept by the issuer at a fixed price in terms of a government currency, generally the US dollar. Tether maintains a popular stablecoin known as USDT by keeping reserves matching the supply of the token. Other approaches include that used by MakerDao for its stablecoin Dai, where reserves are decentralized and take the form of tokens or Ethereum. These decentralized stablecoins are referred to as "algorithmic stablecoins". A more unfortunate example is Terra, who's algorithmic stablecoin collapsed to the tune of 60 billion dollars.

Terra

https://www.terra.money/ (this looks exactly like the above)

It’s also on their website https://www.terra.money/

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.