DOxy: A Dissolved Oxygen Monitoring System.

Autor: Shaghaghi, Navid¹ (AUTHOR) nshaghaghi@scu.edu, Fazlollahi, Frankie¹ (AUTHOR), Shrivastav, Tushar¹ (AUTHOR), Graham, Adam¹ (AUTHOR), Mayer, Jesse¹ (AUTHOR), Liu, Brian¹ (AUTHOR), Jiang, Gavin¹ (AUTHOR), Govindaraju, Naveen¹ (AUTHOR), Garg, Sparsh¹ (AUTHOR), Dunigan, Katherine¹ (AUTHOR), Ferguson, Peter¹ (AUTHOR)

Publikováno v: Sensors (14248220). May2024, Vol. 24 Issue 10, p3253. 22p.

Data gap/outlier correction and treatment.

Autor: Zuluaga, Juan, Castillo, Michael, Syal, Divya, Calle, Andres, Shaghaghi, Navid

Publikováno v: AIP Conference Proceedings; 2024, Vol. 3034 Issue 1, p1-8, 8p

Lecture, Discussion, Group Work, Repeat: Using Aerial Photography and Machine Learning to Study the Use of Writing-Related Pedagogies in STEM Courses and Their Impact on Different Student Subgroups[1].

Autor: Voss, Julia, Shaghaghi, Navid, Calle, Andres Mauricio, Lee, Kristin, Abbate, Liam

Publikováno v: Across the Disciplines; 2022, Vol. 19 Issue 1/2, p99-121, 23p, 1 Color Photograph, 5 Charts, 2 Graphs

Safe Routes.

Autor: Shaghaghi, Navid, Mackey, Aidan, Mistele, Stephen, Rooney, Kevin, Tallis, Nicholas

Publikováno v: ACM International Conference Proceeding Series; 9/14/2020, p240-243, 4p

Neuroevolution vs Reinforcement Learning for Training Non Player Characters in Games:The Case of a Self Driving Car

Autor: Kovalsky, Kristián, Palamas, George

Publikováno v: Kovalsky, K & Palamas, G 2021, Neuroevolution vs Reinforcement Learning for Training Non Player Characters in Games : The Case of a Self Driving Car . in N Shaghaghi, F Lamberti, B Beams, R Shariatmadari & A Amer (eds), Intelligent Technologies for Interactive Entertainment-12th EAI International Conference, INTETAIN 2020, Proceedings . Springer, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 377, pp. 191-206, 12th EAI International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2020, Virtual, Online, 12/12/2020 . https://doi.org/10.1007/978-3-030-76426-5_13

The aim of this project is to compare two popular machine learning methods, a non-gradient-based algorithm such as neuro-evolution with a gradient-based reinforcement learning on an irregular task of training a car to self-drive around 3D circuits wi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f1c372414a6b719473be7d45c268f2ca
https://vbn.aau.dk/da/publications/0270b7fd-070e-4f8d-a82e-2778afba62d5