CS388- Final pitches – Eliza Vardanyan

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1. Fake news/unreliable source detection

Fake news detection algorithm would be an improved version of detecting unreliable sources. For this, I would need to come up with a database of credible data and identify the trustworthiness of the source based on that database. 

Combination of multiple features will be required to identify to then evaluate the unreliability of the news/source. Some of those features are word combination, attached images, clickbait analysis, and checking whether the content is sponsored.

Machine learning would be used to create this algorithm ( some similar projects identified hybrid approaches that used user characteristics from a particular social media as well as the user’s social graph. The algorithm was based on node2vec that allowed extraction from social media followers/followers. )

My project would allow a user interface for interaction and manual news/source credibility check. This project would be unique with the accuracy of the source reliability and work on more than one medium. 

2. Breast cancer detection

Various ways of cancer detection have been detected in recent years as technology and medicine start moving more hand in hand. Depending on the type of cancer, there are different ways of detecting cancer in medicine. For breast cancer, the most common methods of detecting are through MRI, breast ultrasound, mammograms. 

 Is there a way to get the hints of cancer early, before it has developed into its late stages?

 I will be looking at previous work that has been done in integrating the medical records ( images from the methods mentioned above) into an algorithm of image processing and cancer detection. 

I will be using image processing (from MRI, breast ultrasound, mammogram) to explore the historiography method. I will be looking at thermal body scan results as well and comparing these two approaches. For this, I will also explore the work done previously in this field.

I have searched and found various datasets present on Kaggle on mammogram scans.

3. Crime prediction/prevention

This project involves past crime analysis in a particular city (e.g., Boston or Indianapolis), takes the types of crime that happened in one of these locations, and uses machine learning neural networks to predict the possible crime location/hotspot. This project would rely on data mining through the records of crime. 

This algorithm would also consider other features such as economic changes, news, and their impact on the crime rate. I consider taking into account the level of the crime and putting this into the analysis.

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