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Identifying and Reporting Child abuse

Latest AI and machine learning research in identifying and reporting child abuse for healthcare professionals.

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Showing 148-168 of 2,668 articles
Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. E...

Low-rank representation with adaptive graph regularization.

Low-rank representation (LRR) has aroused much attention in the community of data mining. However, i...

Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.

Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often n...

Differential effects of childhood neglect and abuse during sensitive exposure periods on male and female hippocampus.

The hippocampus is a highly stress susceptible structure and hippocampal abnormalities have been rep...

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

Air pollutant concentration forecasting is an effective method of protecting public health by provid...

Medical image classification based on multi-scale non-negative sparse coding.

With the rapid development of modern medical imaging technology, medical image classification has be...

A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine.

The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of th...

Lateral specialization in unilateral spatial neglect: a cognitive robotics model.

In this paper, we present the experimental results of an embodied cognitive robotic approach for mod...

Small-World Propensity and Weighted Brain Networks.

Quantitative descriptions of network structure can provide fundamental insights into the function of...

Eigenspectrum bounds for semirandom matrices with modular and spatial structure for neural networks.

The eigenvalue spectrum of the matrix of directed weights defining a neural network model is informa...

Total nitrogen levels as a key constraint on soil organic carbon stocks across Australian agricultural soils.

Understanding how pedoclimatic drivers regulate soil organic carbon (SOC) stock is crucial for gaini...

Memory flow-controlled knowledge tracing with three stages.

Knowledge Tracing (KT), as a pivotal technology in intelligent education systems, analyzes students'...

InclusiViz : Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation.

Urban segregation refers to the physical and social division of people, often driving inequalities w...

Improved surface NO Retrieval: Double-layer machine learning model construction and spatio-temporal characterization analysis in China (2018-2023).

As an important atmospheric pollutant causing serious harm to human health and the natural environme...

Biology-Informed Matrix Factorization: An AI-Driven Framework for Enhanced Drug Repositioning.

Advances in artificial intelligence (AI) and intelligent computing have significantly accelerated dr...

Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.

BACKGROUND: Understanding protein-molecular interaction is crucial for unraveling the mechanisms und...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

The accurate assessment of the brain's functional network is seen as crucial for the understanding o...

Machine learning-enabled risk prediction of self-neglect among community-dwelling older adults in China.

BACKGROUND: Elder self-neglect (ESN) is usually ignored as a private problem and impairs the health ...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing a...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synth...

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