This study evaluates several machine-learning based approaches for identifying diagnostic test accuracy studies within a large set of scientific articles for the purpose of reducing the workload of researchers when manually screening abstracts to be utilized in systematic reviews. The relative performance was measured between three documents representation models, with different degrees of rand...

Prospective right hemidiaphragm navigator (NAV) is commonly used in free-breathing coronary MRI. The NAV results in an increase in acquisition time to allow for resampling of the motion-corrupted k-space data. In this study, we are presenting a joint prospective-retrospective NAV motion compensation algorithm called compressed-sensing motion compensation (CosMo). The inner k-space region is acq...

Most practical text classification tasks in natural language processing involve training sets where the number of training instances belonging to each of the classes are not equal. The performance of the classifier in such a case can be affected by the sampling strategies used in training. In this work, we describe a cost sensitive and random undersampling variants of convolutional neural netwo...

Introduction: Parallel imaging has been widely used in dynamic MRI for improved spatial or temporal resolution. In this study, we propose a new k-t space sampling trajectory for dynamic parallel imaging techniques. This method applies a random phase modulation to the spatial aliasing of images in temporal domain. As a result, the spatial aliasing induced by k-space undersampling at every time f...

In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as the constituents. The dataset was taken from the Business Intelligenc...

INTRODUCTION 4D cardiovascular MRI applications typically require prolonged scan times and suffer from artifacts due to physiological motion. Compressed sensing (CS) and its combination with parallel imaging (PI) have recently emerged as valuable tools for accelerating MRI acquisitions by reconstructing images from undersampled k-space data. The required incoherent artifact in CS is usually ach...

The performance of compressed sensing (CS) algorithms is dependent on the sparsity level of the underlying signal, the type of sampling pattern used and the reconstruction method applied. The higher the incoherence of the sampling pattern used for undersampling, less aliasing will be noticeable in the aliased signal space, resulting in better CS reconstruction. In this work, based on point spre...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

Bidikmisi is tuition assistance from the government for high school graduates (SMA) or equivalent who have good academic potential but economic limitations. Different scholarships that focus on providing awards financial support to those excel. The achievement requirements are aimed at ensuring recipients selected truly and willingness complete higher education. Given of this bidikmisi must rea...