An Interdisciplinary Mathematical Optimization and Neural Networks for facial emotion recognition in pharmaceutical and clinical research

This paper presents a unified framework involving integration of different principles of calculus and transform functions on the deep neural networks and using them for efficient emotion recognition. Facial emotion recognition is the application of significant use in computer vision, the evaluation of pharmaceutical research and mental health related drug development. It's one of the very significant applications in computer science area and also to human-computer interaction, security, and psychological analysis. This research presents an interdisciplinary framework that integrates mathematical optimization techniques with the deep neural networks to optimize learning rate for the ResNet architectures to be better for FER on the benchmark dataset FER2013+ for emotion classification. Trade-offs in the model size with computational efficiency in recognition performance shall be addressed together with feasibility and potential applications to deploy such a ResNet model on resource-limited devices. To measure the models' ability to recognize complex emotional features under resource constraints, experiments were conducted. The findings highlight the potential of optimized deep neural networks as supportive tools in pharmaceutical and healthcare research, particularly for patient-centered studies, real-time emotion monitoring, and data-driven assessment of treatment responses. Higher models, such as ResNet50 and ResNet101, recorded a higher accuracy rate in complicated emotions but relied on more computing resources. ResNet18 and ResNet34 were more efficient and thereby useful in embedded applications. The fit-one-cycle method gave enhanced training efficiency for all the architectures.