@inproceedings{21889687f7794b19a81cc4aa6f61d85c,
title = "Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs",
abstract = "The emergence of harmful algal blooms (HABs) along the U.S. East Coast has necessitated the development of an innovative drone-based detection system, a response to the growing challenges posed by climate change and pollution. This multifaceted approach involves the collection of high-resolution images from drones and smartphone cameras, the detection and classification of algae using Transformer Neural Networks and Large Language Models (LLMs), and the application of a data segmentation approach for efficient data analysis. The system enhances traditional monitoring techniques by integrating unique custom data gathered from New Jersey's water bodies with public data collected by the state's Environmental Protection Department. It offers a more precise and efficient method for HAB detection.",
keywords = "data segmentation, drones, HABs classification, HABs detection, LLMs, marine ecosystems, transformers",
author = "Andrea Balcacer and Brendan Hannon and Yulia Kumar and Kuan Huang and Joseph Sarnoski and Shuting Liu and Li, {J. Jenny} and Patricia Morreale",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023 ; Conference date: 06-10-2023 Through 08-10-2023",
year = "2023",
doi = "10.1109/URTC60662.2023.10534955",
language = "English",
series = "IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings",
}