While machine learning methods for named entity recognition (mention-level detection) have become common, machine learning methods have rarely been applied to normalization (concept-level identification). Recent research introduced a machine learning method for normalization based on pairwise learning to rank. This method, DNorm, uses a linear model to score the similarity between mentions and ...

Data normalization techniques are a very important initial stage to be carried out in order obtain good predictive data approach. Many researchers get different prediction and error results each use of these techniques. Thereby, this article discusses the accuracy rate seven at preprocessing Neural Network Backpropagation (NNBP) architecture including decimal scaling, Z-score, min-max (there 6 ...

Multimodal biometric authentication systems are now widely used for providing the utmost security owing to its better recognition performance compared to unimodal systems. Multimodal biometric systems are developed by combining the information of individual biometrics. In this paper, a multimodal biometric system is proposed by combining the scores of iris and palm print traits of a person. Thi...

Objective: The current study was performed to provide real-time bedside ultrasonography for emergency medical technicians (EMTs) and assess the advantages and disadvantages of its application in dealing with trauma patients in pre-hospital setting from their viewpoints.Methods: This semi-experimental study was conducted in Tehran, Iran. Twenty EMTs were selected purposefully and underwent...

We participated in task 2 of the CLEF eHealth 2016 challenge. Two subtasks were addressed: entity recognition and normalization in a corpus of French drug labels and Medline titles, and ICD-10 coding of French death certificates. For both subtasks we used a dictionary-based approach. For entity recognition and normalization, we used Peregrine, our open-source indexing engine, with a dictionary ...

This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet, with purpose alleviate pressure on final classifier. We give design classifiers, which collects features produced between network sets, present constituent layers activation function for calculate classification score each use L2 normalization obt...

We first present our sparse and dense interpretations for the top 100 most confident results, ordered by confidence. Confidence is defined as the mean value of the weight image (i.e., the normalization constant Z) and is roughly equivalent to the number of detections, weighted by cablibrated score. We then present results automatically evenly spaced among the remaining results.

PURPOSE To compare "standardization," "Gaussian normalization," and "Z-score normalization" intensity transformation techniques in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) estimates of cerebral blood volume (CBV) in human gliomas. DSC-MRI is a well-established biomarker for CBV in brain tumors; however, DSC-MRI estimates of CBV are semiquantitative. The use of image ...

We first present the top 50 most confident results per dataset, training on the NYU v2 dataset and testing the other dataset. Confidence is the mean value of the weight image (i.e., the normalization constant Z), roughly equivalent to the number of detections, weighted by cablibrated score. We then present results automatically selected among the remaining results, evenly spaced according to co...