BACKGROUND: Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other ha...
To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) v...
Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within...
In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to ...
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some n...
Journal of chemical information and modeling
Nov 30, 2020
Following identification of a target protein, hit identification, which finds small organic molecules that bind to the target, is an important first step of a structure-based drug design project. In this study, we demonstrate a target-specific drug d...
MALDI-TOF MS is an effective potential tool to distinguish between MSSA and MRSA. By combining the ClinProTools3.0 software and manual grouping intervention, we proposed a model optimization method for the first time. The cross validation of the mode...
We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalitie...
Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and related cell subtypes has assumed importance since it aids the laborious manual process of review and diagnosis. Several State-Of-The-Art (SOTA) metho...
In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes...
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