AIMC Topic: Algorithms

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Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Environmental science and pollution research international
Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector mac...

A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.

Environmental science and pollution research international
Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA)...

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.

Analytical chemistry
Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assi...

Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.

Scientific reports
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4...

Unsupervised feature extraction by low-rank and sparsity preserving embedding.

Neural networks : the official journal of the International Neural Network Society
Manifold based feature extraction has been proved to be an effective technique in dealing with the unsupervised classification tasks. However, most of the existing works cannot guarantee the global optimum of the learned projection, and they are sens...

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.

Radiology
Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images...

Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Physics in medicine and biology
Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy ...

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.

Journal of healthcare engineering
INTRODUCTION: Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement ...

A data-driven artificial intelligence model for remote triage in the prehospital environment.

PloS one
In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical pe...

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

IEEE transactions on medical imaging
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o...