Artificial Intelligence Medical Compendium

Multi-view clustering remains a challenging task due to the heterogeneity and inconsistency across multiple views. Most esisting multi-view spectral clustering methods adopt a two-stage approch-constructing fused spectral embeddings matrix followed b... read more 

Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coherence Tomography (OCT) has become a standard imaging modality for retinal disease diagnosis, but OCT im... read more 

Deep neural networks are notoriously vulnerable to adversarial perturbations, largely due to the presence of non-robust features that destabilize model performance. Traditional Adversarial Training (AT) methods on feature space typically operate on o... read more 

Despite the hinge loss function being non-strongly-convex and non-strongly smooth, we establish the linear rate of convergence for sparse linear support vector machines (SVM) up to its statistical accuracy. The algorithm we use is the proximal gradie... read more 

Forest fire smoke detection is crucial for early warning and emergency management, especially under complex environmental conditions such as low contrast, high transparency, background interference, low illumination, occlusion, and overlapping smoke ... read more 

This paper is dedicated to demonstrating the influence of leaders on followers in signed networks with antagonistic interactions, where leaders have their own dynamics and interact with other leaders in a strongly connected closed subgraph (SCCC). An... read more 

Existing bipartite graph based methods commonly learn a consistent anchor graph across multiple views utilizing various optimization techniques to determine clustering assignments, maintaining linear complexity w.r.t. the number of samples. Owing to ... read more 

In industrial control processes, system dynamics and parameters often change over time. As a result, traditional control schemes designed for fixed environments struggle to handle complex, dynamic operating conditions. This paper focuses on pattern-b... read more 

AIMS: To predict nurses' turnover intention using machine learning techniques and identify the most influential psychosocial, organisational and demographic predictors across three countries. DESIGN: A cross-sectional, multinational survey design. ME... read more 

AIM: To synthesise the available evidence on the effect of artificial intelligence in promoting positive nursing practice environments, exploring outcomes for professionals, clients, and institutions. BACKGROUND: Artificial intelligence has undergone... read more