Fake news detection, methods and data processing

Due to its environmental benefits and decreasing costs, the supply of solar energy is growing at an accelerating pace globally. However, the decentralised nature of solar makes it difficult to keep track of the different photovoltaic (PV) systems deployed across a country. There is a critical need for highly accurate, comprehensive national databases of solar systems, which would allow policymakers, researchers, and the government to study socioeconomic trends in solar deployment. Manual surveys have shown to be inaccurate. The 2018 DeepSolar study by Yang et. al developed a deep-learning framework and national solar deployment database for the US using high-quality satellite imagery, which proved to be a much more efficient and accurate approach. However, satellite imagery in developing countries such as Bangladesh is of much lower resolution and quality, and performed poorly with the original DeepSolar model by Yang et. al. Our study highlights the implementation of a novel convolutional neural network (CNN) in detecting solar panels through low resolution Google Static Maps API satellite imagery data.

This project was built with the goal of performing real-time obstacle detection on a drone.

This research evaluates the safety performance of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) by training supervised machine learning models on incident report data to predict injury severity. The best results were achieved using a fine-tuned Random Forest model, offering insights to improve autonomous systems and enhance road safety.