Model Predicts COVID Outbreak

No idea how well this actually works in practice, but interesting.   Could be useful for future needs.  Examining the stated methods.

Model Predicts COVID-19 Outbreak Two Weeks Ahead of Time

Florida Atlantic University, Gisele Galoustian, August 6, 2021  via CACM

Researchers at Florida Atlantic University (FAU) and Lexis-Nexis Risk Solutions have crafted a long short-term memory (LSTM) deep-learning model that could potentially predict a COVID-19 outbreak two weeks in advance. The team blended driving-mobility data compiled by the Apple Maps application, COVID-19 statistics, and county-level demographics from 531 U.S. counties. Researchers trained the model to record the impact of government responses and age on COVID-19 cases and viral spread, respectively. Results indicated that average daily cases declined as the retiree percentage expanded and increased as the youth percentage grew. FAU's Stella Batalama said the research "has significant applications for effective management of the pandemic and future outbreaks, which has the potential to save lives and keep our economies thriving."  .... 

Emotions to Drive Autonomous Vehicles?

Some interesting things have come out of FAU, we participated.   Here another.  But can human emotions be use to autonomously drive?  Even in part? Test it well.

Invention Uses Machine-Learned Human Emotions to 'Drive' Autonomous Vehicles  By Florida Atlantic University

Florida Atlantic University (FAU)'s Mehrdad Nojoumian has designed and patented new technology for autonomous systems that uses machine-learned human moods to respond to human emotions. Nojoumian's adaptive mood control system employs non-intrusive sensory solutions in semi- or fully autonomous vehicles to read the mood of drivers and passengers.

In-vehicle sensors collect data based on facial expressions and other emotional cues of the vehicle's occupants, then use real-time machine learning mechanisms to identify occupants' moods over time. The vehicle responds to perceived emotions by selecting a suitable driving mode (normal, cautious, or alert).

FAU's Stella Batalama said Nojoumian's system overcomes self-driving vehicles' inability to accurately forecast the behavior of other self-driving and human-driven vehicles.

From Florida Atlantic University