Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it’s possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.

Analysis of a Sorter Cascade Applied to Control a Wheelchair | InTechOpen, Published on: 2016-10-19. Authors: Marcos Figueredo, Alexandre Nascimento, Roberto L.S. Monteiro, et

In the evolving landscape of organizational innovation, Co-Creative Cobots (3C-Bots) represent a transformative shift toward human–machine creative partnerships. However, the behavioral dynamics enabling successful creative cooperation in mixed communities remain poorly understood. This study investigates how human behavioral profiles and population ratios influence trust-mediated creative cooperation through an extended simulation-based approach combining trust, creativity, and agent behavior models. We developed a multi-agent simulation integrating Social Value Orientation theory, Asimov’s Three Laws of Robotics, and Guilford’s creativity framework across five population configurations. Our novel Cooperation-Creativity Index (CCI) – a composite metric integrating trust, creativity, and stability revealed significant differences across configurations. Counter-intuitively, robot-majority environments achieved optimal trust-mediated creative outcomes. Results demonstrate that trust serves as the critical mediating foundation for creative cooperation, suggesting innovation-driven organizations should prioritize reliable creative partnerships over balanced human-machine teams. These findings offer important implications for designing next-generation innovation ecosystems.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by the depletion of dopamine-producing neurons in the substantia nigra, leading to motor dysfunction and alterations in speech patterns. Temporal variations in speech signals have been identified as potential biomarkers of PD, emphasizing the importance of early detection for slowing disease progression and allowing timely therapeutic interventions. This study presents an optimized Convolutional Neural Network (CNN) classifier designed for accurate and efficient PD detection. The model leverages sustained vowel phonations (“ah”) from the Parkinson’s Disease Voice Recording Data Set, which includes samples from 147 patients with PD and 48 healthy controls. Sustained vowels are widely used in clinical protocols and research because they minimize articulatory variability and highlight relevant vocal biomarkers such as jitter, shimmer, and the harmonic-to-noise ratio (HNR). The preprocessing pipeline involved denoising, feature normalization, and Bayesian hyperparameter tuning to optimize performance. The CNN achieved a detection accuracy of 96.85%, along with high precision, sensitivity, and a well-balanced F1 score, demonstrating robust predictive capabilities. These findings validate the model’s reliability in detecting PD-specific voice patterns, even in noisy conditions, underscoring its potential for integration into clinical diagnostic workflows.

No presente artigo realizamos uma análise de comportamentos de cooperação em ambiente simulado. O presente estudo tem como finalidade observar os resultados da interação dos caracteres egoísta, vingativo e altruísta em constante interação ao longo de gerações. Durante o processo foram desenvolvidos algoritmos que emulam uma da aplicações da teoria dos jogos, o dilema do prisioneiro e outro que utiliza a forma como os autômatos celulares movimentam-sedentro de uma malha virtual. A contribuição que conseguimos dar foi a de encontrar um valor percentual máximo que uma população aceita do caráter egoísta e verificar que os caracteres vingativo e altruísta tem um tempo de vida bem próximos. Palavras chave: Simulação computacional, algoritmos, comportamento social, cooperação.