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The team created a novel dataset to fix the issue of the absence of data for detached shock waves for airfoils. The team utilized Computational Fluid Dynamics to generate 1000 shock wave data sets.
A website built with Taipy that uses a recurrent neural network and MATLAB to classify images and process images.
Struggle with schedules? Let ScheduMate do it for you!
The ultimate Pictionary playground where your drawing skills are put to the test against a perceptive AI. Can you out-doodle the machine?
Few research look into the perceptions held by teachers regarding educational games in the classroom, so we collected the data from educators and using confirmatory factor analysis to analyze the data
ML model to predict hospital morality rates.
We tactically sort the data through criteria to produce the best dataset for machine learning prediction!
Our project is training an ai model using a dataset to determine the accuracy of a patient surviving given the specifics of their condition.
A tool that plans the optimal course selections for a major at Texas A&M based on GPA data and professor ratings.
All in one dataset for bouldering!!! Utilize handgathered tabular, video, and pose data to analyze trends in the different styles, physical characteristics, and skills of climbers!
In this project we developed a Deep Neural Network for the classification of images into 15 different clases.
Identifying sketches stroke by stroke.
After some initial exploration, we decided against a Reinforcement Learning approach and instead created a unique PID loop implementation written entirely in native Python!
In this project, we aimed to guide a robot in a simulation environment. We learned about Proportional-Integral-Derivative (PID) control and other dynamic systems.
Algorithmic approach to bot-race problem with PID control.
Answering the future.
This model predicts whether a given social media post will be approved or rejected by a user.
Unraveling the confidence of patient survival.
Zooom
Have a doodle but no one can quite make out what it's supposed to be? No worries, our neural network built with PyTorch can figure out even the messiest of scribbles through the power of ML!
Using sequential model machine learning, we can predict whether or not a social media post will be approved by the caption written in the post.
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Through our machine learning model, we intend to mitigate the process of patient data analysis and provide a more effective way of checking survival rates.
Marky enables brands to efficiently create social media posts, but how can we predict whether a post will be approved? In comes Binarky, a binary classification using machine learning for approval!
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