Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
To increase the trustworthiness of deep neural networks, it is critical to improve the understanding of how they make decisions. This paper introduces a novel unsupervised concept-based model for image classification, named Learnable Concept-Based Model (LCBM) which models concepts as random variables within a Bernoulli latent space. Unlike traditional methods that either require extensive human...
In recent years, the development of specialized edge computing devices has significantly increased, driven by the growing demand for AI models. These devices, such as the NVIDIA Jetson series, must efficiently handle increased data processing and storage requirements. However, despite these advancements, there remains a lack of frameworks that automate the optimal execution of optimal execution ...
Existing deraining models process all rainy images within a single network. However, different rain patterns have significant variations, which make...
Cloud data centres demand adaptive, efficient, and fair resource allocation techniques due to heterogeneous workloads with varying priorities. Howev...
In this study, a shift-invariant optical pattern classification system is proposed. Optical machine learning systems have been widely studied as proce...
Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for mental health (MH) care, offering opportunities to ...
Medical imaging constitutes critical information in the diagnostic and prognostic evaluation of patients, as it serves to uncover a broad spectrum of ...
The design-make-test cycle for drug discovery is highly dependent on the purification of synthesized compounds. Prior to evaluation of suitability, ul...
The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field....
BACKGROUND AND OBJECTIVE: In recent years, deep U-shaped network architectures have been widely applied to medical image segmentation tasks, achieving...
Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decision-making. However, traditional scoring systems re...
Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment ...
We introduce Flashback Learning (FL), a novel method designed to harmonize the stability and plasticity of models in Continual Learning (CL). Unlike...
Distributed and federated learning are important tools for high-dimensional classification of large datasets. To reduce computational costs and over...
Rare and intractable diseases affect an estimated 3.5% to 5.9% of the global population but remain largely underserved in terms of diagnosis and treat...
Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupe...
Mobile GUI agents aim to autonomously complete user-instructed tasks across mobile apps. Recent advances in Multimodal Large Language Models (MLLMs)...
Mobile GUI agents aim to autonomously complete user-instructed tasks across mobile apps. Recent advances in Multimodal Large Language Models (MLLMs)...
Image-based virtual try-on aims to fit a target garment to a specific person image and has attracted extensive research attention because of its hug...
Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a cri...