Activation functions are pivotal in neural networks, determining the output of each neuron.
Traditionally, functions like sigmoid and ReLU have been static and deterministic. However …

We present a deep learning model for finding human-understandable connections between
input features. Our approach uses a parameterized, differentiable activation function, based …

We describe a novel approach to explainable prediction of a continuous variable based on
learning fuzzy weighted rules. Our model trains a set of weighted rules to maximise …

This study focuses on enhancing real-time irrigation decisions and stream flow forecasts
using short-term daily forecasts of reference evapotranspiration (ETo). While conventional …

A new approach for Fuzzification and Defuzzification processes of a high degree of
overlapping between the linguistic variables through proportional and relative-dynamic …

Automated machine learning (AutoML) methods improve upon existing models by
optimizing various aspects of their design. While present methods focus on hyperparameters …

The nonlinear activation functions applied by each neuron in a neural network are essential
for making neural networks powerful representational models. If these are omitted, even …

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-
readable relationships between input features with the help of nilpotent fuzzy logic and multi …

Neuronal representations within artificial neural networks are commonly understood as
logits, representing the log-odds score of presence (versus absence) of features within the …

The choice of activation functions and their motivation is a long-standing issue within the
neural network community. Neuronal representations within artificial neural networks are …