AIMC Topic: Machine Learning

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Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...

Using Machine Learning to Evaluate the Role of Microinflammation in Cardiovascular Events in Patients With Chronic Kidney Disease.

Frontiers in immunology
BACKGROUND: Lipid metabolism disorder, as one major complication in patients with chronic kidney disease (CKD), is tied to an increased risk for cardiovascular disease (CVD). Traditional lipid-lowering statins have been found to have limited benefit ...

Deep learning applied to breast imaging classification and segmentation with human expert intervention.

Journal of ultrasound
PURPOSE: Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.

Evaluation of shape factor impact on discharge coefficient of side orifices using boost simulation model with extreme learning machine data-driven.

Network (Bristol, England)
In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme Learning Machine (ELM) model. In addition, the Monte Carlo simulations (MCs) are applied to assess th...

Mandibular shape prediction model using machine learning techniques.

Clinical oral investigations
OBJECTIVE: To create a mandibular shape prediction model using machine learning techniques and geometric morphometrics.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.

Sensors (Basel, Switzerland)
Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs...

On Inductive-Transductive Learning With Graph Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Many real-world domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The graph neural network (GNN) is a machine learning model capable of directly managing gra...

Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition.

IEEE transactions on pattern analysis and machine intelligence
The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are u...

Machine Learning Assisted Simultaneous Structural Profiling of Differently Charged Proteins in a Porin A (MspA) Electroosmotic Trap.

Journal of the American Chemical Society
The nanopore is emerging as a means of single-molecule protein sensing. However, proteins demonstrate different charge properties, which complicates the design of a sensor that can achieve simultaneous sensing of differently charged proteins. In this...

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.

Sensors (Basel, Switzerland)
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-ri...