AIMC Topic: Humans

Clear Filters Showing 24371 to 24380 of 95995 articles

Shaping dynamical neural computations using spatiotemporal constraints.

Biochemical and biophysical research communications
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinar...

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke.

Medical image analysis
Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decis...

Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued i...

Performance of large language models in oral and maxillofacial surgery examinations.

International journal of oral and maxillofacial surgery
This study aimed to determine the accuracy of large language models (LLMs) in answering oral and maxillofacial surgery (OMS) multiple choice questions. A total of 259 questions from the university's question bank were answered by the LLMs (GPT-3.5, G...

Adapting to the human: A systematic review of a decade of human factors research on adaptive autonomy.

Applied ergonomics
This systematic review provides an understanding of existing human factors research on adaptive autonomy, its design, its impacts, and its definition. We conducted a search on adaptive autonomy and additional relevant search terms in four databases, ...

Predicting renal damage in children with IgA vasculitis by machine learning.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Children with IgA Vasculitis (IgAV) may develop renal complications, which can impact their long-term prognosis. This study aimed to build a machine learning model to predict renal damage in children with IgAV and analyze risk factors for...

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

European journal of medicinal chemistry
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.

Journal of molecular biology
Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the pa...

Abnormal Behavior Recognition Based on 3D Dense Connections.

International journal of neural systems
Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and ...