Neurodegenerative diseases (NDDs) cause, among other symptoms, motor impairment. Given the incurable nature of the NDDs, several studies have investigated gait using artificial intelligent models as non-invasive alternative methods to assist in the diagnosis of these diseases. This work proposes a novel method using an Encoder-Only Transformer to detect NDDs, a multi-classification task, through gait signal analysis. The approach comprises data preprocessing, windowing technique, a modified transformer architecture and cross-validation evaluation. The results indicate the transformer-based architecture can be a promising alternative to accomplish this goal.

This study aims to investigate the use of text classification techniques to support the audit of municipal accounts by the Court of Accounts in the context of calculating Total Personnel Expenditure. The study contributes to the discovery of expenses incorrectly classified by municipal managers due to error or fraud, which would be left out of the calculation. It used data obtained from the Court of Accounts of the Municipalities of Goiás, Brazil – TCM. These data were labeled by human experts and then prepared to build expenditure classification models from their description. The TF-IDF algorithm was used for feature engineering, and the Support Vector Machines (SVM), Logistic Regression, and Multinomial Naïve Bayes were used for classification. The results showed that the proposed method is consistent, having reached an F-Score of 0.91 and 0.97 with the SVM algorithm in the binary and multiclass corpus, respectively. Even dealing with a highly unbalanced dataset in the multiclass approach, the performance can be considered very good. As an innovative study, this work seeks to insert, in the context of the analysis of public expenditure on personnel, the use of text mining techniques to solve problems that would require the effort of many human specialists. The authors understand that there is a practical contribution to the process of analysis of public expenditure on personnel carried out by the Court of Accounts.

Huntington's Disease (HD) is a genetic disorder that causes the progressive breakdown of nerve cells in the brain, reducing an individual's ability to reason, walk, and speak. Due to its severity, new approaches are important for the development of methods that contribute to the correct classification of this disease. In this paper, we propose an automatic method for diagnosing Huntington's Disease using gait dynamics information. Our approach is divided into a four-stage pipeline: preprocessing, feature extraction, classification, and diagnosis output. We evaluate the performance of our proposed method through well-known classifiers that are commonly used in machine learning problems. A publicly available database on Gait Dynamics in Neuro-Degenerative Disease is used, and the experimental results show that both Support Vector Machines (SVM) and Decision Tree (DT) were able to achieve an average accuracy of 100:0%, representing an improvement in the field.

Medical X-ray images are an important and valuable source of studies and diagnoses for diseases with low cost besides its high availability. However, radiological images are subject to degradations related to low contrast and presence of noise. Based on this finding, this article presents a simple but efficient enhancement method for these images with the objective of contrast gain and noise removal. The proposed method (MP) consists of a sequence of interactive steps. Start from the step of double precision conversion and end with removing impulsive noises. An evaluation with the PSNR, Entropy, AMBE, and IQR indicators was performed, besides gain check on the thresholding process and the histogram characterization. The evaluation was conducted on three different datasets in a total of 1409 images chest X-rays. The results compared to others known in the literature proved to be promising and put it as an interesting alternative in the process of enhancement medical X-ray images.

Computer vision is an area have proven to play an essential role in urban and rural applications like medical, agriculture, and remote sensing. The use of image processing methods for simulating the visual capability of robots plays a crucial role in the consolidation of smart farming. The understanding of the complexity of outdoor environments, where the robot performs its task, is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination, unpredictable weather conditions, and different color shades. In this study, we present a new method to detect and segment tree trunks from unstructured environments where natural properties such as lighting and terrain shape form a variety of non-controlled conditions. We prepared a dataset with stereo image pairs and ground truth maps to calculate disparities and to evaluate the proposed method in the application of smart farming. The results show that the presented approach can segment trees with high precision, which is an important step in calculating the disparity of external components by systems that use the stereoscopic view.

This work proposes the development of a method for smartwatches that allows to control platform games using continuous recognition of gestures and conducts a case study as the game Super Mario World. Uses a set of gestures based on geometric shapes to send actions to the game. Gesture recognition is performed by the algorithm of continuous gesture recognition, as it is able to recognize a gesture before being finalized, allows an action to be performed quickly, improving feedback. The recognition process was paralleled to improve performance. A technique has been developed that allows the execution of several gestures in sequence, without the need for a signaling that a gesture has been finalized or initiated. It was also created a technique that allows the sending of special commands to the game using the pressure applied on the screen by the player. A prototype for smartwatches was developed that communicates with an emulation platform installed on a Raspberry PI 3. A user experiment was performed as well as usability and experience tests. The results show that the method has the potential to be used effectively and effectively by players.

As a digital image provides such information about a scene, a disparity map can be yielded by means of stereo images. This topic was exhaustively surveyed but it remains one of the most important branches in both computer vision and machine vision. Most algorithms are organized in a pipeline that starts with a matching cost step and ends with a disparity refinement. This paper provides a simple but an effective method to adjust a disparity map in a more appropriate configuration, i.e. it presents a disparity refinement technique. It is based on an assumption that most disparities in a region point to a correct disparity value for this area. To develop the methodology, we use image segmentation and support weighted windows. By performing an evaluation, it shows that this method can increase the robustness of a raw disparity map even with a lot of noisy parts.

Several works in the literature use propositional (“black box”) approaches to generate prediction models. In this work we employ the Inductive Logic Programming technique, whose prediction model is based on first order rules, to the domain of breast cancer. These rules have the advantage of being interpretable and convenient to be used as a common language between the computer scientists and the medical experts. We also explore the relevance of some of variables usually collected to predict breast cancer. We compare our results with a propositional classifier that was considered best for the same dataset studied in this paper.

Mammoclass is a web tool that allows users to enter a small set of variable values that describe a finding in a mammography, and produces a probability of this finding being malignant or benign. The tool requires that the user types in every variable a value in order to perform a prediction. In this work, we present a speech-to-text interface integrated to MammoClass that allows radiologists to speak up a mammography report instead of typing it in. This new MammoClass module can take audio content, transcribe it into written words, and automatically extract the variable values by applying a parser to the recognized text. Results of spoken mammography reports show that the same variables are extracted for both types of input: typed in or dictated text.