Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained ${\sigma }_3=0.95$, which is an improvement of 0.14 points (compared with the state of the art of ${\sigma }_3=0.81$) by using manual segmentation, and ${\sigma }_3=0.89$ using automatic semantic segmentation, proving that depth estimation is improved when the shape and position of objects in a scene are known.

Glaucoma detection is an important task, as this disease can affect the optic nerve, and this could lead to blindness. This can be prevented with early diagnosis, periodic controls, and treatment so that it can be stopped and prevent visual loss. Usually, the detection of glaucoma is carried out through various examinations such as tonometry, gonioscopy, pachymetry, etc. In this work, we carry out this detection by using images obtained through retinal cameras, in which we can observe the state of the optic nerve. This work addresses an accurate diagnostic methodology based on Convolutional Neural Networks (CNNs) to classify these optical images. Most works require a large number of images to train their CNN architectures, and most of them use the whole image to perform the classification. We will use a small dataset containing 366 examples to train the proposed CNN architecture and we will only focus on the analysis of the optic disc by extracting it from the full image, as this is the element that provides the most information about glaucoma. We experiment with different RGB channels and their combinations from the optic disc, and additionally, we extract depth information. We obtain accuracy values of 0.945, by using the GB and the full RGB combination, and 0.934 for the grayscale transformation. Depth information did not help, as it limited the best accuracy value to 0.934.

Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks.

Comparison of Transfer Style Using a CycleGAN Model with Data Augmentation

Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to $256\times 256$ pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy.

A straightforward methodology for identifying certain classes of chaotic systems based on a novel version of the least-squares method, assuming they are algebraically observable and identifiable with respect to a measurable output, is introduced. This output allows us to express the original system as a chain of integrators, where the last term, which depends on the output and its corresponding time derivatives, lumps the system’s non-linearities. We can factorize this term into a regressor function multiplied by an unknown-parameter vector, suggesting that a high-gain observer can be used to simultaneously and approximately estimate the states of the pure integrator and the evolution of the lumped nonlinear term. This allows us to rewrite the original system as a linear regression equation. This configuration enables the above-mentioned least-squares method to recover the chaotic-system parameters.