AIMC Topic: Humans

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Margin-Based Deep Learning Networks for Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have m...

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module.

Sensors (Basel, Switzerland)
Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessar...

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

International journal of molecular sciences
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection ...

A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory.

Sensors (Basel, Switzerland)
Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behavi...

Analyzing and learning the language for different types of harassment.

PloS one
THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches...

Securing Mesh with Delayed Absorbable Suture Does Not Increase Risk of Prolapse Recurrence After Robotic Sacral Colpopexy.

Journal of endourology
Nonabsorbable monofilament suture is traditionally used to secure vaginal mesh for robot-assisted sacral colpopexy (RASC) but can become exposed postoperatively requiring invasive vaginal removal. Polydioxanone delayed absorbable suture may avoid th...

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML al...

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission sc...

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to ass...