Dual approach artificial neural networks-fractional order operator to enhancing in vitro fertilization predictions and success measure

This paper introduces a comprehensive modeling framework for in vitro fertilization (IVF) by combining two distinct yet complementary modeling approaches. First, an artificial neural network (ANN) model is developed to capture intricate relationships within the IVF system, leveraging its ability to learn complex patterns from empirical data. The ANN model is followed by a fractional-order model (FOM) to capture enhanced temporal dynamics for the process under consideration, and it represents a new approach towards systems characterized by a fractional derivative. The proposed ANN model learns nonlinear relationships between different inputs, including sperm morphology and motility, oocyte quality, and other relevant biological variables that affect the outcome of IVF. Furthermore, the FOM expands traditional differential equations into fractional-order derivatives, which provide more accurate representation for the intricate fractional dynamics inherited by reproductive processes. Combining the ANN and FOM frameworks leads to a synergistic model, which improves our understanding in terms of fractional-order dynamics that regulate IVF outcomes and improves predictive capabilities. The experimental results show the effectiveness of the integrated approach in modeling the complexities of IVF dynamics. The integrated ANN and FOM approach gives a more complete view of the underlying biological processes involved in IVF, making better predictions of the success rate of IVF. This integrated modeling paradigm, by incorporating artificial intelligence into the fractional order dynamics, is furthering the science of reproductive biology while enhancing the forecasting capacity of the success of IVF. The results of the simulations suggest that the framework can improve awareness of the complex dynamics in assisted reproductive technologies and is a useful tool not only for academics but also for medical professionals.