Completed

CMPE 491/492 Project

Twitter is a medium that allows information to spread very fast. It is hard to find the source of the informationg and confirm its truth. I have developed a tweet analysis web tool, TweeTrust, to find the first instance of a URL, hashtag or an image to help find the source of the information that is being spread on Twitter. The users can then comment on the validity of the information and share their thoughts. A list of fast spreading local trends also can help find the news on the home page.

The aim of our project is to predict home country of social media users who do not specify it explicitly. To achieve the goal, we used location information of users who already specified it and approach the problem in a probabilistic manner. In this project, we focused on Twitter users, however the scope may be widened to any other social media platform. The problem can be easily extended to other prediction problems with similar missing information structure.

Ivy is a programming language designed for Ethereum Virtual Machine. Ivy enables us to write smart contracts and deploy them directly to an Ethereum blockchain.

The human genome contains valuable information about the person and its hand analysis is cumbersome since it consists of billions of nucleotides. This work computationally analyses the mutations in the human genome which are the main source of differences and diseases. It utilizes ensembling of various supervised machine learning models on a cancer dataset that is obtained from TCGA and labels the genomes with their cancer types at 74% test accuracy. The obtained accuracies outperform the works in the literature such as DeepGene and create promising results for the future study.