AIMC Topic: Algorithms

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Predicting dengue importation into Europe, using machine learning and model-agnostic methods.

Scientific reports
The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and i...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Psychological medicine
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these...

Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
We study training deep neural network (DNN) frequency-domain beamformers using simulated and phantom anechoic cysts and compare to training with simulated point target responses. Using simulation, physical phantom, and in vivo scans, we find that tra...

Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Shortness of breath is a major reason that patients present to the emergency department (ED) and point-of-care ultrasound (POCUS) has been shown to aid in diagnosis, particularly through evaluation for artifacts known as B-lines. B-line identificatio...

Role of Artificial Intelligence and Machine Learning in Nanosafety.

Small (Weinheim an der Bergstrasse, Germany)
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. N...

Deep learning in interstitial lung disease-how long until daily practice.

European radiology
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung...

Improving blood glucose level predictability using machine learning.

Diabetes/metabolism research and reviews
This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-mo...

Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has t...

Automatic wheat ear counting using machine learning based on RGB UAV imagery.

The Plant journal : for cell and molecular biology
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is ...

A neurodynamic optimization approach for complex-variables programming problem.

Neural networks : the official journal of the International Neural Network Society
A neural network model upon differential inclusion is designed for solving the complex-variables convex programming, and the chain rule for real-valued function with the complex-variables is established in this paper. The model does not need to choos...