AIMC Topic: Humans

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Predicting User Susceptibility to Phishing Based on Multidimensional Features.

Computational intelligence and neuroscience
While antiphishing techniques have evolved over the years, phishing remains one of the most threatening attacks on current network security. This is because phishing exploits one of the weakest links in a network system-people. The purpose of this re...

Detection of COVID-19 With CT Images Using Hybrid Complex Shearlet Scattering Networks.

IEEE journal of biomedical and health informatics
With the ongoing worldwide coronavirus disease 2019 (COVID-19) pandemic, it is desirable to develop effective algorithms to automatically detect COVID-19 with chest computed tomography (CT) images. Recently, a considerable number of methods based on ...

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.

IEEE journal of biomedical and health informatics
Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as...

Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification.

IEEE journal of biomedical and health informatics
The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because o...

Cell Localization and Counting Using Direction Field Map.

IEEE journal of biomedical and health informatics
Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then ...

Rubik-Net: Learning Spatial Information via Rotation-Driven Convolutions for Brain Segmentation.

IEEE journal of biomedical and health informatics
The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and brain diseases. However, it is challenging to segment MRI slices because of the low contrast between different b...

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

IEEE journal of biomedical and health informatics
Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. ...

Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.

IEEE journal of biomedical and health informatics
This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

IEEE journal of biomedical and health informatics
Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time-consuming task due to a large number of influencing factors. Healthcare p...

WeDea: A New EEG-Based Framework for Emotion Recognition.

IEEE journal of biomedical and health informatics
With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiological signals, known as electroencephalography (EEG), have been actively developed and applied to var...