A strategy based on paraconsistent random forest for sEMG gesture recognition systems robust to contaminated data.

Applying machine learning algorithms to physical signals is always challenging since undesirable events can occur when signals are acquired outside a controlled environment. Among several applications, movement recognition through sEMG signals is especially complicated, since they are subject to several types of contaminants that can degrade the signal. These degradations alter the characteristics of myoelectric signals, hindering the ability of pattern recognition algorithms to discriminate movement classes.

B-mode and contrast-enhanced ultrasound versus magnetic resonance imaging in hepatic angiomyolipoma: a case report.

Angiomyolipoma is a solid mesenchymal tumour that usually affects the kidney. Hepatic localization of angiomyolipoma (HAML) is rare and usually asymptomatic however it presents a challenging differential diagnosis. We present the case of a 45-year-old man affected by tuberous sclerosis complex type 2 (TSC2) and an hepatic lesion suspected to be hepatocellular carcinoma on magnetic resonance but whose Bmode ultrasound and contrast-enhanced ultrasound (CEUS) findings were consistent with benignity, as confirmed by histology.

Reliability of different formulas for estimating plasma Apolipoprotein B levels in a large cohort of South European individuals.

Background And Aims: Direct measurement of apolipoprotein B (ApoB) is not always standardized and is relatively expensive, making it unavailable in several low-income settings. To address this issue, several formulas have been developed to estimate ApoB levels. Therefore, our study aims to compare the reliability of 23 formulas for estimating ApoB levels in a large cohort of South-European individuals.

ADHD Diagnosis through Resting-State EEG Frequency Analysis with Random Forest.

Attention-deficit hyperactivity disorder (ADHD) affects about 5% of the population. In order to minimize ADHD effects, it is important to identify its biomarkers. We analyzed electroencephalographic (EEG) signals using a random forest (RF) classifier optimized with a genetic algorithm (GA) to find differences between control and ADHD groups.

FPGA-based Implementation of Random Forest Classifier for sEMG Signal Classification.

This paper proposes a method that combines a hardware description language and Random Forests theory, to develop a motion recognition embedded system. To validate this system, the Ninapro database DB2 was used as training and test data, resulting in an accuracy of 83.1% for the test base in movement prediction.