AIMC Topic: Humans

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Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.

Journal of medical systems
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive com...

Cocreative Development of Robotic Interaction Systems for Health Care: Scoping Review.

JMIR human factors
BACKGROUND: Robotic technologies present challenges to health care professionals and are therefore rarely used. Barriers such as lack of controllability and adaptability and complex control functions affect the human-robot relationship. In addition t...

Comparison of feed-forward control strategies for simplified vertical hopping model with intrinsic muscle properties.

Bioinspiration & biomimetics
To analyse walking, running or hopping motions, models with high degrees of freedom are usually used. However simple reductionist models are advantageous within certain limits. In a simple manner, the hopping motion is generally modelled by a spring-...

Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

Nanoscale
How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic ...

Comparison and benchmark of deep learning methods for non-coding RNA classification.

PLoS computational biology
The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding...

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

Frontiers in public health
This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2,517 patients treated in the ICU of a tertiary hospital in Ch...

STransU2Net: Transformer based hybrid model for building segmentation in detailed satellite imagery.

PloS one
As essential components of human society, buildings serve a multitude of functions and significance. Convolutional Neural Network (CNN) has made remarkable progress in the task of building extraction from detailed satellite imagery, owing to the pote...

Hybrid evolutionary machine learning model for advanced intrusion detection architecture for cyber threat identification.

PloS one
In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats a...

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

PloS one
BACKGROUND: Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echoca...