Topic: Support vector machine

Background: Diagnosing Attention Deficit Hyperactivity Disorder (ADHD) is a challenging process currently focused on questionnaires and clinical interviews, which could be sensitive to subjective biases (Farone et al. 2005). Some studies tried machine learning (ML) algorithms with neurophysiological data such as functional magnetic resonance imaging or electroencephalography to explore objective measures for diagnosing purporses (Mikolas et al. 2022). In this study, ML algorithms are built using cheaper and simpler measures including cognitive and intelligence assessments, reducing time and re

Sarcopenia, a condition characterized by the gradual decline of muscle mass and strength, predominantly affects elderly individuals and those leading sedentary lifestyles. Sarcopenia not only heightens the risk of falls, fractures, and hospitalizations among the elderly but also diminishes their quality of life while imposing a significant financial burden on healthcare systems. To address this challenge, our study aimed to develop an innovative integrated measuring system coupled with intelligent-based analysis to evaluate sarcopenia-related changes in neuromuscular functions and behaviors.We

Spatial frequencies (SF) optimal for word recognition are well established. However, other classes of stimuli demonstrate a rapid temporal evolution of SFs most useful for visual recognition. (Bar,2003,JcogNeurosci) This study aims to assess the time course of SF processing in visual word recognition. Word images were filtered like so: 1.2, 2.4, 4.8, and 9.6 cycles per degree, with 200 ms exposure duration and variyng signal-to-noise ratio (SNR; signal=image; noise=white noise) according to a random sampling function integrating sine waves with 5-55 Hz frequencies in steps of 5 Hz. Classificat