An important part of training simple neural networks for students and researchers alike is choosing an adequate activation function. Activation functions serve a number of important purposes, including but not limited to: preventing dead neurons, eliminating the vanishing gradient problem, and allowing networks to learn complex patterns. Currently, students are taught to use the "guess-and-check" method in choosing the best activation functions for their neural networks. Although literature and knowns exist for choosing activation functions for specific tasks, not all literature applies. The purpose of this presentation is to report an algorithm that can help choose the best activation function by eliminating human-handed checking and increasing time efficiency.

Single sign-on authentications ease the challenges of creating new pathways into different software and databases for users. However, with the growing use of web bots and malicious actors looking to take advantage of sensitive data, it becomes imperative that a well-meaning authentication method of filtering thoughtful users be utilized. This article presents a large-language model powered mechanism and structure to filter users to be human, non-malicious, and conscious of their membership of accounts, databases, and other software.