AIMC Topic: Algorithms

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MeshCut data augmentation for deep learning in computer vision.

PloS one
To solve overfitting in machine learning, we propose a novel data augmentation method called MeshCut, which uses a mesh-like mask to segment the whole image to achieve more partial diversified information. In our experiments, this strategy outperform...

Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition.

International journal of neural systems
In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are desc...

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation.

Journal of chemical information and modeling
In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and th...

Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation.

Sensors (Basel, Switzerland)
Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountabi...

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ...

Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a ch...

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The s...

Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model.

The journal of nursing research : JNR
BACKGROUND: Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learnin...

Ontological representation, classification and data-driven computing of phenotypes.

Journal of biomedical semantics
BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable...