  Activation functions (AFs) are crucial components of deep neural networks
(DNNs), having a significant impact on their performance. An activation
function in a DNN is typically a smooth, nonlinear function that transforms an
input signal into an output signal for the subsequent layer. In this paper, we
propose the Parametric Leaky Tanh (PLTanh), a novel hybrid activation function
designed to combine the strengths of both the Tanh and Leaky ReLU (LReLU)
activation functions. PLTanh is differentiable at all points and addresses the
'dying ReLU' problem by ensuring a non-zero gradient for negative inputs,
consistent with the behavior of LReLU. By integrating the unique advantages of
these two diverse activation functions, PLTanh facilitates the learning of more
intricate nonlinear relationships within the network. This paper presents an
empirical evaluation of PLTanh against established activation functions, namely
ReLU, LReLU, and ALReLU utilizing five diverse datasets.
