Artificial Intelligence Medical Compendium

This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates ... read more 

Restricted Boltzmann machines (RBM) are generative models capable to learn data with a rich underlying structure. We study the teacher-student setting where a student RBM learns structured data generated by a teacher RBM. The amount of structure in t... read more 

This paper tested the relevance of two machine learning approaches (decision trees, DTs; and random forest models, RFs) applied to meat authentication. DT allow to select and rank potential biomarkers according to their respective discriminatory powe... read more 

The total polar surface area (TPSA) is a crucial parameter in photovoltaic (PV) materials, as it directly influences their solubility, processability, and device performance. This study leverages machine learning-assisted reverse engineering to gener... read more 

While machine learning has gained traction in toxicological assessments, the limited data availability requires the quantification of uncertainty of in silico predictions for reliable decision-making. This study addresses the challenge of predicting ... read more 

For decades, the photosynthetic bacteria (PSB)-based nitrogen treatment and valorization from wastewater have been explored. However, balancing nitrogen removal performance and resource recovery potential in PSB has remained a key unresolved issue fo... read more 

BACKGROUND: Nursing students' acceptance and usage of AI are crucial for embracing and implementing the technology in nursing practice in the future. However, there is a lack of literature to examine the factors affecting AI usage intention among nur... read more 

Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized into low-bit representations, there is often a significant performance drop compared to their full-pre... read more 

Neural networks often excel in short-horizon tasks, but their long-term reliability is less assured. We demonstrate that even a minimal architecture, trained on near-periodic data, can exhibit hyperbolic chaotic behavior after a small parameter pertu... read more 

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network m... read more