Cultural Competence

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data a...

Machine-learning media bias.

We present an automated method for measuring media bias. Inferring which newspaper published a given article, based only on the frequencies with which it uses different phrases, leads to a conditional probability distribution whose analysis lets us automatically map newspapers and phrases into a bias space. By analyzing roughly a million articles from roughly a hundred newspapers for bias in dozen...

Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models.

Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within th...

Deep learning in periodontology and oral implantology: A scoping review.

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal an...

Adaptive Dense Ensemble Model for Text Classification.

Text classification has been widely explored in natural language processing. In this article, we propose a novel adaptive dense ensemble model (AdaDEM...

Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.

Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases...

Revealing the real-time diversity and abundance of small mammals by using an Intelligent Animal Monitoring System (IAMS).

It is challenging to reveal the real-time spatio-temporal change of diversity and abundance of animals in natural systems by using traditional methods...

Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning.

Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innova...

Oblique and rotation double random forest.

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable le...

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.

Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still...

Generic Mechanism for Waveform Regulation and Synchronization of Oscillators: An Application for Robot Behavior Diversity Generation.

While nonlinear oscillators have been widely used for central pattern generators to produce basic rhythmic signals for robot locomotion control, metho...

Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging.

This study aimed to discuss the application value of the bias field correction algorithm in magnetic resonance imaging (MRI) images of patients with p...

Accuracy and Efficiency of Machine Learning-Assisted Risk-of-Bias Assessments in "Real-World" Systematic Reviews : A Noninferiority Randomized Controlled Trial.

BACKGROUND: Automation is a proposed solution for the increasing difficulty of maintaining up-to-date, high-quality health evidence. Evidence assessin...

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.

OBJECTIVE: As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing...

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases.

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on...

Feature blindness: A challenge for understanding and modelling visual object recognition.

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a ...

Using Convolutional Neural Networks for the Assessment Research of Mental Health.

Existing mental health assessment methods mainly rely on experts' experience, which has subjective bias, so convolutional neural networks are applied ...

Evolving neural networks through bio-inspired parent selection in dynamic environments.

Environmental variability often degrades the performance of algorithms designed to capture the global convergence of a given search space. Several app...

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace...

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning.

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop p...