AIMC Topic: Algorithms

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Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis.

Methods (San Diego, Calif.)
To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random...

Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms.

Environmental science and pollution research international
This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction va...

Prediction of vancomycin dose on high-dimensional data using machine learning techniques.

Expert review of clinical pharmacology
OBJECTIVES: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of ind...

A biomimetic neural encoder for spiking neural network.

Nature communications
Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-dri...

Improving protein domain classification for third-generation sequencing reads using deep learning.

BMC genomics
BACKGROUND: With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce impor...

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

Frontiers in public health
This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantu...

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.

Computational intelligence and neuroscience
Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could he...

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel ...

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network.

Biomolecules
Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze...

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Sensors (Basel, Switzerland)
Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified ...