Since the beginning of the pandemic, high education has been significantly affected, due to the sudden change from in-person to remote mode, which has led to not all teaching methods adapting to this new modality. In this context, the storytelling technique, which in face-to-face mode is effective, in the remote mode presents significant deficiencies since it cannot be interacted with in a shared space and with objects between students and the teacher. In this situation, some applications are poorly adapted for high education to solve this lack, which leads us in this work to present the development of a mobile application as a complement to the teaching of high education with storytelling using augmented reality, which leads to improving how to tell stories between students and teachers in remote teaching using digital storytelling, supported with augmented reality, a more participatory...

This study investigates the application of the Xception architecture for accurate classification of skin lesions, focusing on the early detection of melanoma and other malignant skin conditions. Utilizing deep learning techniques, the research aims to enhance the precision and efficiency of skin lesions diagnosis. The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. Preprocessing steps, including data splitting, augmentation, and image resizing, are applied to the dataset. The Xception architecture, a deep convolutional neural network, serves as the foundational model, supplemented with customized classification layers for specialized features and predictions. The model’s performance is evaluated using diverse metrics. The experimental outcomes revea...