Sign language is the primary means of communication for many people who are deaf or hard of hearing. The advancement of automatic sign language recognition systems is essential to enhance accessibility and reduce communication barriers. However, the lack of robust and accessible technological solutions still poses a significant obstacle to the full social inclusion of this population. In this context, video signal recognition systems have gained prominence, especially with the advancement of computer vision techniques based on deep learning. This study presents a multimodal recognition approach that combines RGB and depth video data through an attention-based fusion mechanism. The proposed architecture employs two pre-trained Multiscale Vision Transformers (MViT) to extract spatiotemporal features from each modality. These features are then integrated using an attention module that dynamically adjusts the contribution of each input stream. Experiments were conducted using the LIBRAS-UFOP dataset, which contains 56 signs performed by five different individuals, grouped into four linguistic categories. To evaluate the model’s performance, two evaluation protocols were employed: a random data split and a leave-one-signer-out strategy to assess the model’s ability to generalize to unseen users. The results show that while multimodal fusion provided modest improvements in the random split scenario, it achieved significantly higher accuracy when evaluated on unseen signers. These findings demonstrate the value of combining multiple modalities and leveraging attention mechanisms to address user variability, ultimately contributing to more robust and reliable sign language recognition in real-world applications.

Skin cancer is one of the cancers that most aggravates the problem in public health. Among the types of cancer, melanoma is the most aggressive type. Its early diagnosis is essential to increase the possibility of adequate treatment, aiming to reduce the mortality rate. Dermatologists generally use manual methods to diagnose skin lesions. These methods, in addition to being time-consuming, as they are performed manually, can present different results for the same lesion when analyzed by different specialists. Therefore, an automated diagnosis may be necessary to deal with this issue as well as avoid invasive tests. For this, the task of segmenting the skin lesion in the dermoscopic image can be fundamental, as it is a basic task in the image analysis process. In the present work, a Convolutional Neural Network (CNN) model, based on the U-Net, is used to segment the lesion in dermoscopic images. This proposal achieved an accuracy of 0.949 and Jaccard of 0.833 for the 2017 ISIC base, and an accuracy of 0.954 and Jaccard of 0.850 for the 2018 ISIC base. The proposed model has a simpler architecture, in addition to requiring less computational resources. The experiments made it possible to observe that the proposed model results are promising compared with other CNN models presented in the literature.