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V.I.P.E.R detects AI-generated images using deep learning + forensic signals like frequency and sensor noise, achieving 96%+ accuracy while explaining every decision.
A data-driven predictive tool for marine tracking. We utilize logistic regression and environmental feature engineering to forecast seal sighting likelihoods, visualized on a custom interactive map.
How can we help students achieve academic success?
As avid enjoyers of good food and gatekept, niche restaurants, we’re tired of being recommended the same restaurants over and over again. That’s why we created Datadash.
Healthy Home Audit maps environmental health risk by ZIP code across LA/OC. Using XGBoost and public data, it reveals asthma, cardio, and pollution inequality to help communities and policymakers act
Can a machine read between the lines of a tweet and pick the right emoji? We trained 5 models to find out and the mistakes were more interesting than the accuracy.
Rethinking Emoji Prediction with Semantic Understanding
Busi uses weighted parameters and SQLite3 to calculate business viability scores. By pairing LLM-driven weights with geographic APIs, we pinpoint the ideal spot for your next shop.
Our project predicts housing instability by analyzing price and movement trends, helping users spot high-risk areas before displacement happens.
LA crime data leads to real decisions, but unknown crime times are often logged as midnight or noon. We flagged these unreliable timestamps and found they disproportionately affect vulnerable victims.
As the threat of cyber attacks and their consequences skyrocket, it's important to evaluate our methods of combatting these breaches. We'll use these metrics to recommend solutions to breach victims.
Using 311 service data and American Community Survey data to geographical overlay how service requests are handled in areas of different financial, racial, and educational demographics.
OMNY-ANALYST: While others hallucinate, we automate. 12 months of compliance in 10 mins with Gemini 2.0. Scalable. Fast.
AquaResponse helps firefighters predict fire spread using historical data, locate nearby water sources, and assess risk levels based on elevation, topography, wind levels, and fuel sources.
Breathe in & breathe out!
For food and retail brands, it can provide real-time insights into customer emotions and cravings which can then help them tailor marketing campaigns.
Analyzing the effect different factors, such as HDI and unemployment rate, on depression across seven countries across the globe.
Our AI uses Melissa’s dataset to connect people with ideal communities in Rancho Santa Margarita—matching them with neighbors who share their values and interests.
Do you ever wonder if gas prices differ locally? So local, that gas prices could be different in another city right next to you?
Because We Just Want A Double Double
A data-driven approach to NBA in-game coaching decisions, identifying the key factors that should influence lineup choices in close games.
Given a tweet with its emoji removed, can a model predict which emoji the author used? This project explores that question by training and comparing three classical machine learning classifiers.
We turn the idea of the California Dream into a data-driven housing analysis, identifying crisis zones across California and highlighting which communities should be prioritized for intervention.
Determining the best amenities for your house depending on certain characteristics.
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