Anemia

Unusual Driver Behavior Detection in Videos Using Deep Learning Models.

Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road a...

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity sugges...

Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human-Robot Architecture for Search and Rescue.

The roles of emergency responders are challenging and often physically demanding, so it is essential that their duties are performed safely and effect...

Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training.

Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic...

A CNN-RNN unified framework for intrapartum cardiotocograph classification.

BACKGROUND AND OBJECTIVE: Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is very vital for pregnant women before del...

Efficacy and Safety of the "Trisection Method" Training System for Robot-Assisted Radical Cystectomy at a Single Institution in Japan.

To maintain a surgeon's concentration, reduce fatigue, and train young surgeons, surgical procedures for bladder cancer are divided into the following...

Forecasting medical state transition using machine learning methods.

Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original...

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.

A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. P...

Prediction of anemia using facial images and deep learning technology in the emergency department.

BACKGROUND: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological me...

Diaphragmatic paralysis post COVID-19 treated with robot-assisted plication reinforced with acellular dermal matrix: a report of two cases.

Coronavirus disease 2019 (COVID-19) continues to be a disease of global importance, with an increasing array of sequelae attributed to infection by th...

A real-time driver fatigue identification method based on GA-GRNN.

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning i...

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by S...

Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm.

BACKGROUND: More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells...

Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal.

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label no...

Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists' Gaze Patterns.

Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of which increases at the end of work shifts. In this study,...

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history c...

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors.

Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot ...

Application of an EMG-Rehabilitation Robot in Patients with Post-Coronavirus Fatigue Syndrome (COVID-19)-A Feasibility Study.

This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation robot in patients with Post-Viral Fatigue (PVF) syndrome a...

Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production.

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable ...

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (S...