Combating Climate Fake News Using NLP

This project presents a Convolutional Neural Network (CNN) model that identifies landfills in the U.S. using image data, achieving a 97.1% test accuracy. By supporting the creation of a national landfill database, the model can help detect illegal sites and guide the expansion of methane capture systems to reduce greenhouse gas emissions.

The task of stellar classification can be tedious and lengthy when done manually. One can expedite stellar classification by creating an artificial intelligence model to automate the process. The current stellar classification model serves to effectively categorize stars for research purposes regarding their distribution around the universe, so automating the development of this resource would allow professionals to allocate more time to explore the bounds of our current understanding of space and the universe. After finding and analyzing a dataset containing numerical and categorical features, a supervised learning approach was then used to train and test different models on their ability to classify the stars in the given test set. A Decision Tree Classifier, Random Forest Classifier, Ridge Classifier, and Support Vector Classifier were trained and tested using the data.

The goal of this study is to predict Solar Cycle 25 through the deep learning approach,and determine what parameters affect prediction accuracy and what the optimal number of historical solar cycles are used to reliably and accurately predict the upcoming solar cycle. The solar cycle predictions will help us prepare ahead of time for future solar activity.