AIMC Topic: Humans

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Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

Scientific reports
In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional...

Human factors considerations for the context-aware design of adaptive autonomous teammates.

Ergonomics
Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangero...

Estimating the Severity of Oral Lesions Via Analysis of Cone Beam Computed Tomography Reports: A Proposed Deep Learning Model.

International dental journal
OBJECTIVES: Several factors such as unavailability of specialists, dental phobia, and financial difficulties may lead to a delay between receiving an oral radiology report and consulting a dentist. The primary aim of this study was to distinguish bet...

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

Journal of imaging informatics in medicine
The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for ef...

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.

A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints.

Neural networks : the official journal of the International Neural Network Society
Controllable Pareto front learning (CPFL) approximates the Pareto optimal solution set and then locates a non-dominated point with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice...

FPWT: Filter pruning via wavelet transform for CNNs.

Neural networks : the official journal of the International Neural Network Society
The enormous data and computational resources required by Convolutional Neural Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive problem, filter pruning has become one of the practical approaches. At presen...