This paper introduces a novel method for early skin cancer classification that leverages a soft voting ensemble of Convolutional Neural Networks. The approach combines data preprocessing, image segmentation, and an ensemble of MobileNetV2, VGG19, and InceptionV3 to achieve high accuracy and balanced performance for real-world dermatological applications.

This paper introduces a Multi-LLM Collaboration approach, guided by an "LLM Chemistry" framework, to enhance the reliability and trustworthiness of medication recommendations from clinical vignettes. The method aims to create effective, stable, and calibrated LLM ensembles by explicitly modeling interaction dynamics.