scholarly journals Deep learning classifier based on nefprox neural network

2016 ◽  
Vol 4 (50) ◽  
Author(s):  
O. I. Chumachenko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Classifier

scholarly journals Deep Learning to Distinguish ABCA4-Related Stargardt Disease from PRPH2-Related Pseudo-Stargardt Pattern Dystrophy

2021 ◽  
Vol 10 (24) ◽  
pp. 5742
Author(s):  
Alexandra Miere ◽  
Olivia Zambrowski ◽  
Arthur Kessler ◽  
Carl-Joe Mehanna ◽  
Carlotta Pallone ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Good Accuracy ◽  
Fundus Autofluorescence ◽  
Retinal Diseases ◽  
Proof Of Concept ◽  
Stargardt Disease ◽  
Test Dataset ◽  
Pattern Dystrophy ◽  
Learning Classifier

(1) Background: Recessive Stargardt disease (STGD1) and multifocal pattern dystrophy simulating Stargardt disease (“pseudo-Stargardt pattern dystrophy”, PSPD) share phenotypic similitudes, leading to a difficult clinical diagnosis. Our aim was to assess whether a deep learning classifier pretrained on fundus autofluorescence (FAF) images can assist in distinguishing ABCA4-related STGD1 from the PRPH2/RDS-related PSPD and to compare the performance with that of retinal specialists. (2) Methods: We trained a convolutional neural network (CNN) using 729 FAF images from normal patients or patients with inherited retinal diseases (IRDs). Transfer learning was then used to update the weights of a ResNet50V2 used to classify the 370 FAF images into STGD1 and PSPD. Retina specialists evaluated the same dataset. The performance of the CNN and that of retina specialists were compared in terms of accuracy, sensitivity, and precision. (3) Results: The CNN accuracy on the test dataset of 111 images was 0.882. The AUROC was 0.890, the precision was 0.883 and the sensitivity was 0.883. The accuracy for retina experts averaged 0.816, whereas for retina fellows it averaged 0.724. (4) Conclusions: This proof-of-concept study demonstrates that, even with small databases, a pretrained CNN is able to distinguish between STGD1 and PSPD with good accuracy.

A domain categorisation of vocabularies based on a deep learning classifier

2021 ◽  
pp. 016555152110181
Author(s):  
Alberto Nogales ◽  
Miguel-Angel Sicilia ◽  
Álvaro J García-Tejedor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Linked Data ◽  
Deep Neural Networks ◽  
Open Data ◽  
Data Sets ◽  
Learning Classifier ◽  
Learning Techniques ◽  
The World ◽  
Increasing Trend

The publication of large amounts of open data is an increasing trend. This is a consequence of initiatives like Linked Open Data (LOD) that aims at publishing and linking data sets published in the World Wide Web. Linked Data publishers should follow a set of principles for their task. This information is described in a 2011 document that includes the consideration of reusing vocabularies as key. The Linked Open Vocabularies (LOV) project attempts to collect the vocabularies and ontologies commonly used in LOD. These ontologies have been classified by domain following the criteria of LOV members, thus having the disadvantage of introducing personal biases. This article presents an automatic classifier of ontologies based on the main categories appearing in Wikipedia. For that purpose, word-embedding models are used in combination with deep learning techniques. Results show that with a hybrid model of regular Deep Neural Networks (DNNs), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), classification could be made with an accuracy of 93.57%. A further evaluation of the domain matchings between LOV and the classifier brings possible matchings in 79.8% of the cases.

scholarly journals Identification of Low Population States in Cryo-EM Using Deep Learning

2021 ◽  
Author(s):  
Alec Fraser ◽  
Nikolai S Prokhorov ◽  
John M Miller ◽  
Ekaterina S Knyazhanskaya ◽  
Petr G Leiman
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Three Dimensional ◽  
Atomic Resolution ◽  
Neural Network Classifier ◽  
Automate Data ◽  
Bacterial Host ◽  
Learning Classifier ◽  
Human Effort ◽  
Automate Data Collection

Cryo-EM has made extraordinary headway towards becoming a semi-automated, high-throughput structure determination technique. In the general workflow, high-to-medium population states are grouped into two- and three-dimensional classes, from which structures can be obtained with near-atomic resolution and subsequently analyzed to interpret function. However, low population states, which are also functionally important, are often discarded. Here, we describe a technique whereby low population states can be efficiently identified with minimal human effort via a deep convolutional neural network classifier. We use this deep learning classifier to describe a transient, low population state of bacteriophage A511 in the midst of infecting its bacterial host. This method can be used to further automate data collection and identify other functionally important low population states.

scholarly journals Handwriting Text Recognition using Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 4088-4092
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
The Other ◽  
Text Recognition ◽  
Learning Classifier ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Recognition Systems ◽  
Style Of Writing

Handwritten text recognition is a laborious task because humans can write a similar message in numerous ways or due to huge diversity in individual’s style of writing. The performance of text recognition systems implemented as neural networks has better results and accuracy than normal traditional classifiers. In this paper we explore the methods used to recognize and detect handwritten text or words in different languages. The major method used to recognize text is the Convolutional neural network (CNN) as a deep learning classifier. The other techniques used are Recurrent Neural Network (RNN) and a custom developed model called deep-writer, which is a variant of CNN architecture.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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