SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification

TL;DR

This paper introduces the Saha-Bora Activation Function (SBAF), a novel activation function for neural networks, demonstrating its analytical advantages and application in classifying exoplanets' habitability.

Contribution

The paper proposes and analyzes a new activation function, SBAF, tailored for neural networks in exoplanet habitability classification, highlighting its analytical properties and benefits.

Findings

SBAF has favorable analytical properties.

SBAF does not suffer from local oscillation problems.

Effective in classifying exoplanets based on habitability.

Abstract

We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes. We call this Saha-Bora Activation Function (SBAF) as the motivation is derived from long standing understanding of using advanced calculus in modeling habitability score of Exoplanets. The function is demonstrated to possess nice analytical properties and doesn't seem to suffer from local oscillation problems. The manuscript presents the analytical properties of the activation function and the architecture implemented on the function. Keywords: Astroinformatics, Machine Learning, Exoplanets, ANN, Activation Function.