This work presents the development of Lucas, a virtual assistant built entirely with open-source tools to support older adults in their daily routines. Built upon the Robot Operating System (ROS 2), the system is composed of modular nodes for wake word detection, speech processing, and natural language interaction. The assistant is triggered by the spoken expression “Opa Lucas” after which it records the user’s voice, transcribes the audio to text using Whisper, and processes the instruction using the LLaMA 3 Large Language Model. Depending on the user’s request, the system may access external tools, such as reminders and weather forecasts, via predefined APIs and SQL queries. The entire interaction is presented through a multimodal interface built with Pygame, combining speech synthesis and animated feedback. The assistant supports speaker identification through voice verification. To evaluate the assistant, a set of instructions was proposed to assess the accuracy, errors made, and the nature of the responses, including aspects such as harmfulness, helpfulness, and coherence. Additionally, the assistant was reviewed and tested by a geriatric care professional. The results demonstrate the assistant’s potential to deliver coherent, accurate, and timely instructions, suggesting its applicability in daily routines. The assistant’s source code is publicly available in a GitHub repository, along with documentation for setup and usage.
Accurate identification of coma etiology is essential for effective treatment in neurocritical care. However, significant challenges persist, especially in settings with limited resources. This study investigates the potential of electroencephalogram (EEG) signals from distinct brain regions for predicting coma etiology using an adapted Convolutional Neural Network combined with Long Short-Term Memory (CNN-LSTM) model. Building on prior hybrid CNN-LSTM architectures that processed all EEG channels collectively, we refined the model to integrate raw EEG data from specific regions (frontal, central, parietal, temporal, and occipital) with patient-specific clinical features (age, gender) and statistical EEG metrics. The model was evaluated on a real-world dataset of 50 patients with coma etiologies including traumatic brain injury, metabolic coma, and stroke, collected at the Clinical Hospital of the Federal University of Uberlândia (HC-UFU). Experimental results demonstrate that the frontal region, when combined with clinical features, yielded the highest predictive performance, achieving an accuracy of 70
Timely disease diagnosis is essential for reducing livestock mortality, as delays caused by reliance on human visual inspection can lead to severe consequences. Early detection of illness hinges on access to detailed, individual animal data capable of capturing subtle behavioral and physiological changes before visible symptoms emerge. A robust classification system is therefore critical for automating behavior analysis, enabling prompt identification of deviations from normal patterns that may indicate illness and facilitating timely intervention. This work reports the development of an innovative livestock behavior classification and health monitoring device. The system incorporates a wireless neck collar equipped with motion and temperature sensors for continuous animal activity monitoring. A prototype collar was tested on two heifers in a controlled field setting under video surveillance. Collected data were manually annotated by human observers, classifying the heifers’ behaviors into five key categories: eating, drinking, walking, lying down, and standing. The study covers the entire process of creating the monitoring system, from the design and construction of the neck collar to data collection and annotation. It also details the complete data mining workflow, including data cleaning, pre-processing, and comparative analysis using six shallow Machine Learning (ML) algorithms. The results indicate that Gradient Tree Boosting is the best-performing model based on classification metrics appropriate for multiclass imbalanced data, such as the F1-score, Weighted F1-score, and Kappa coefficient. The designed device, integrated with a ML model, proved to be an effective and low-cost approach for classifying livestock behavior.
A significant portion of the textual corpus lacks the annotated labels needed for training supervised models. Manual labeling of such data is costly and impractical. In response to this challenge, semi-supervised learning (SSL) has emerged as a pivotal approach to harnessing the potential of both labeled and unlabeled data. This paper explores the integration of adversarial perturbation with SSL, particularly focusing on text classification tasks. We propose an innovative approach called APVAT, which incorporates adversarial perturbation into the Attention Virtual Adversarial Training (VAT) model. We performed experiments in five benchmark datasets, exploring the impact of different embeddings such as fastText, GloVe BERT, and GPT-2. Our contributions are twofold: First, our approach improves classification accuracy in datasets with little training data compared to previous methods, even when labeled data is scarce (e.g., 10
Dermatological diseases represent one of the leading causes of clinical consultations in dogs, affecting over 30
