This project aims to explore the potential of Twitter data for disaster management and risk reduction. The project includes several Python scripts that can be used for collecting, preprocessing, and analyzing Twitter data related to natural hazards and disasters. 
The project involves the development of a Tableau dashboard to visualize the results of an extensive Twitter data analysis focused on the 2018 California Campfire.
This research presents a novel approach to disaster analysis using Twitter data, focusing on the 2018 California Camp Fire. The study was published in 2022 and implements LSTM-based topic classification to enhance understanding of public discourse during the disaster.
This research presents an innovative approach to extracting valuable information from social media data during natural disasters, focusing on Hurricane Harvey in 2017. The study was published in the journal Sustainable Cities and Society in 2022.
This research presents a cutting-edge approach to analyzing social media data during natural disasters, focusing on Hurricane Ian in 2022. The study was published in the International Journal of Disaster Risk Reduction in 2023.
This research presents a novel approach to fault detection in analog circuits using deep learning techniques. The paper was published in the journal Analog Integrated Circuits and Signal Processing in 2021.
This project showcases a smart contract system for managing crowdfunding campaigns on the blockchain. It includes a factory contract for deploying new campaigns and a campaign contract with functionalities for contributions, funding requests, and approvals.
