(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Activation functions are at the heart of how deep neural networks perform non-linear transformations. The use of an activation function allows a neural network to approximate highly complex functions, train using a gradient-based optimization technique and generalize to new data. However, existing activation functions, such as ReLU, GELU, and Swish, have limitations that restrict their use in practice. Specifically, they can saturate gradients during training due to their inherent structure, cause vanishing gradients on deeply stacked architectures, and are inefficient at learning periodic dependency relationships while performing poorly at modeling highly heterogeneous non-linear interactions. These limitations are of particular importance for scientific, financial, and engineering use cases where data represent polynomial, periodic, saturating, and exponential shapes on the same data manifold. This paper introduces HyperNova++, a smooth, adaptive, parameterized activation function that unifies bounded saturation, periodic oscillation, and unbounded growth into a single learnable formula. HyperNova++ is architectured and designed to overcome the expressive constraints of existing activations which enables dynamic, data-driven modulation of curvature, frequency, and growth behavior using three trainable parameters (α,β,γ). These above mentioned parameters respectively govern contributions from the hyperbolic tangent (tanh) for bounded saturation, sine (sin) for periodic oscillations, and Softplus (log(1+ex)) for getting a smooth monotonic growth all thorughout. The resulting function obtained ensures non-vanishing gradients, smooth transitions, and controlled Lipschitz continuity, along with maintaining computational efficiency comparable to contemporary activations and other counterparts. After doing a rigorous, large-scale evaluation on a meticulously crafted synthetic dataset with a known ground-truth decision boundary that stimulates reall life linear, polynomial, and periodic interactions. This controlled environment enables precise, unbiased comparisons against various functions including ReLU, GELU, and Swish under identical architectural, optimization, and hyperparameter settings. HyperNova++ achieves statistically significant superior performance compared to all , exceeding 99% accuracy (0.9903) compared to 98.34% for ReLU, 98.08% for GELU, and 97.60% for Swish, while also attaining the highest F1-score (0.9906) and ROC-AUC (0.9997). Gradient analyses obtained confirm stable, non-vanishing gradients and accelerated convergence. We supplement empirical results obtained during testing with comprehensive theoretical analysis, thus establishing HyperNova++’s universal approximation guarantee, Lipschitz properties, gradient bounds, and optimization landscape characteristics. Practical implementation guidelines, computational complexity dissections, and prospective applications in scientific machine learning, time-series analysis, and multimodal inference are being discussed. Collectively, this work positions HyperNova++ as a potent, versatile activation function for advanced deep learning architectures confronting intricate nonlinear manifolds in upcoming future.