Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
The increasing emerging contaminants (ECs) pose significant challenges to non-targeted screening (NTS) and annotation. Machine learning-based retention time (RT) prediction models offer a promising approach to narrow candidate compounds and enhancing identification accuracy. However, existing studies rely on a single machine learning algorithm, which is susceptible to overfitting or underfitting o...
Vast power grid infrastructure generates enormous volumes of inspection data from smart meters, unmanned aerial vehicle (UAV) patrols, and high-definition video monitoring. Meeting the demand for real-time analysis places stringent requirements on latency, energy efficiency, and on-device intelligence at the edge. Here, we present a molecular crystal memristor-based edge artificial intelligence (A...
Tissue expansion, originally developed for super-resolution imaging, has become a foundation for expansion omics (ExO), a growing field that uses phys...
PURPOSE: Transthyretin cardiac amyloidosis (ATTR-CM) is a progressive, underdiagnosed disease with high morbidity and mortality. While disease-modifyi...
The partial nitritation-anammox (PN/A) process offers significant benefits in energy conservation and carbon neutrality, however, its application is l...
The Flexible Job Shop Scheduling Problem (FJSP) is an Non-deterministic Polynomial (NP)-hard combinatorial optimization problem whose large-scale and ...
BACKGROUND: The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, hold...
Human and veterinary healthcare systems face many parallel challenges, yet opportunities for cross-sectoral learning remain underexplored. This scopin...
Conventional representation learning methods have achieved remarkable performance in traffic flow forecasting when data is sufficient, while they stru...
The integration of artificial intelligence (AI) and machine learning (ML) into medical devices has revolutionized healthcare, enhancing diagnostic acc...
BACKGROUND: Implementing Mohs case complexity grading systems is critical due to inherent complexities of Mohs workflow coordination. Precise scheduli...
Self-attention is the cornerstone of transformers, yet its quadratic time and space complexity with respect to the input sequence length leads to high...
Co-creation has emerged as a transformative approach to meaningful collaboration among students, teachers, and other key stakeholders. Although co-cre...
The advent of generative artificial intelligence (GenAI) is already impacting pedagogical strategies and assessment methodologies in higher education,...
The bone unit (BU) is a multicellular functional unit composed of neuromodulatory networks, bone tissue, and functional blood vessels. As a functional...
BACKGROUND: Cerebral palsy (CP), a leading cause of childhood motor disability, severely impacts lower limb function. Conventional gait training (CGT)...
PURPOSE: To compare the performance of a vision transformer-based foundation model (RETFound) and a supervised convolutional neural network (VGG-19) f...
OBJECTIVE: The aim of this study was to investigate the diagnostic performance of the 2.5-dimensional (2.5D) ensemble deep learning (DL) model based o...
With the increasing prevalence of dermatological diseases, skin lesion segmentation has gained significant attention in medical image analysis. Despit...
This comprehensive review focuses on the use of knowledge graphs and Graph Neural Networks (GNNs) to integrate multi-omics data in the field of person...