2 papers
physics.data-an2026
New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution
M. Adriamirado, A. B. Balantekin, C. Bass +40
We propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Pre…
cs.CV2025
Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach
Mohammed Alsakabi, Aidan Erickson, John M. Dolan +1
We present a cost-effective new approach for generating denser depth maps for Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images obtained from deep neu…