Low-Cost Optimized U-Net Model with GMM Automatic Labeling Used in Forest Semantic Segmentation.

Currently, Convolutional Neural Networks (CNN) are widely used for processing and analyzing image or video data, and an essential part of state-of-the-art studies rely on training different CNN architectures. They have broad applications, such as image classification, semantic segmentation, or face recognition. Regardless of the application, one of the important factors influencing network performance is the use of a reliable, well-labeled dataset in the training stage.

CNN Architectures and Feature Extraction Methods for EEG Imaginary Speech Recognition.

Speech is a complex mechanism allowing us to communicate our needs, desires and thoughts. In some cases of neural dysfunctions, this ability is highly affected, which makes everyday life activities that require communication a challenge. This paper studies different parameters of an intelligent imaginary speech recognition system to obtain the best performance according to the developed method that can be applied to a low-cost system with limited resources.

Role of stress in modulation of skin neurogenic inflammation.

There are complex interconnections between the nervous system and the skin highlighted by the impact of stress and neuroendocrine factors on various dermatological conditions. We investigated the influence of stress on skin neurogenic inflammation induced by capsaicin. A total of 31 healthy subjects were randomized into two groups: subjects in the stress group underwent a stress-inducing protocol and those in the control group were exposed to indifferent conditions.