Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay atten...
This paper proposes a neural network based on the Markov probability transition matrix to predict the training performance of football athletes. Firstly, seven training indicators affecting the training performance are designed by the Event-group training theory. Then, a discrete Hopfield neural network is employed according to the seven training indicators. To improve the forecast ability of the ...
INTRODUCTION: This study proposes a novel Transformer-based approach to enhance talent attraction and retention strategies in rural public health syst...
Air traffic congestion-induced flight accidents pose a significant challenge in the aviation sector. Currently, aviation navigation systems primarily ...
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine le...
Drone fleets with onboard cameras coupled with computer vision and DNN inferencing models can support diverse applications. One such novel domain is...
Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models;...
Calibration to experimental data is vital when developing subject-specific models towards developing digital twins. Yet, to date, subject-specific m...
The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations i...
Balancing content fidelity and artistic style is a pivotal challenge in image generation. While traditional style transfer methods and modern Denois...
How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two ...
The collection of updated data on social contact patterns following the COVID-19 pandemic disruptions is crucial for future epidemiological assessme...
This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First...
ImageNet, an influential dataset in computer vision, is traditionally evaluated using single-label classification, which assumes that an image can b...
The emergence of Vision-Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to enha...
Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PK...
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with p...
Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, of...
Recent deep learning workloads exhibit dynamic characteristics, leading to the rising adoption of dynamic shape compilers. These compilers can gener...
This study introduces an adaptive user interface generation technology, emphasizing the role of Human-Computer Interaction (HCI) in optimizing user ...