AIMC Topic: Algorithms

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Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

Computational intelligence and neuroscience
The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (R...

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

PloS one
Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapt...

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

PloS one
BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentiall...

A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models.

Journal of advanced research
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients a...

In vitro and in vivo performance modelling and optimisation of different dry powder inhalers: A complementary study of neural networks, genetic algorithms and decision trees.

International journal of clinical practice
INTRODUCTION: Aerosol delivery from DPIs could be affected by different factors. This study aimed to evaluate and predict the effects of different factors on drug delivery from DPIs.

Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, a local tracking control (LTC) scheme is developed via particle swarm optimized neural networks (PSONN) for unknown nonlinear interconnected systems. With the local input-output data, a local neural network identifier is constructed to...

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
INTRODUCTION: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (D...

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

Scientific reports
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506...

CT-ORG, a new dataset for multiple organ segmentation in computed tomography.

Scientific data
Despite the relative ease of locating organs in the human body, automated organ segmentation has been hindered by the scarcity of labeled training data. Due to the tedium of labeling organ boundaries, most datasets are limited to either a small numbe...

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified.