most citedDetermining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI20211 cited

Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System

Logan Brown, Reid Pezewski, Jeremy Straub

This paper presents two studies that use a machine learning expert system (MLES). One focuses on a system to advise to United States federal judges for regarding consistent federal…

cs.CR20211 cited

Fake News and Phishing Detection Using a Machine Learning Trained Expert System

Benjamin Fitzpatrick, Xinyu "Sherwin" Liang, Jeremy Straub

Expert systems have been used to enable computers to make recommendations and decisions. This paper presents the use of a machine learning trained expert system (MLES) for phishing…

cs.ET2021

Consideration of the Need for Quantum Grid Computing

Dominic Rosch-Grace, Jeremy Straub

Quantum computing is poised to dramatically change the computational landscape, worldwide. Quantum computers can solve complex problems that are, at least in some cases, beyond the…

cs.CR2021

Defining, Evaluating, Preparing for and Responding to a Cyber Pearl Harbor

Jeremy Straub

Despite not having a clear meaning, public perception and awareness makes the term cyber Pearl Harbor an important part of the public discourse. This paper considers what the term…

cs.LG2021

Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique

Jeremy Straub

Artificial intelligence systems, which are designed with a capability to learn from the data presented to them, are used throughout society. These systems are used to screen loan a…