scholarly journals UTILIZAÇÃO PRÁTICA DE WORD EMBEDDING APLICADA À CLASSIFICAÇÃO DE TEXTO

2020 ◽  
Author(s):  
Luiz Fernando Spillere de Souza ◽  
Alexandre Leopoldo Gonçalves
Keyword(s):  
Text Classification ◽  
Word Embedding ◽  
Practical Application ◽  
Accuracy Rate ◽  
Unstructured Text ◽  
Representation Technique ◽  
Concept Of Word

Text classification aims to extract knowledge from unstructured text patterns. The concept of word incorporation is a representation technique that allows words with similar meanings to have a similar representation, in order to incorporate reasoning characteristics about their use and meaning. The aim of this article is to analyze the work already published on the use of embedded words applied to the classification of texts, to propose a practical application that demonstrates its effectiveness. This study contributes to proving the effectiveness of the use of word incorporation applied to text classification, having reached an accuracy rate of around 73%.

scholarly journals Research on the Relationship between Human Resource Management Activities and Enterprise Performance Based on the Supervised Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chan Sun ◽  
Xiaojuan Li
Keyword(s):  
Text Classification ◽  
Feature Space ◽  
Information Support ◽  
Enterprise Performance ◽  
Job Seekers ◽  
Accuracy Rate ◽  
Job Opportunities ◽  
Rich Information ◽  
The Relationship

HRMS is a very critical tool for companies. The recruitment text contains rich information that can provide strong information support for the company’s recruitment work and also improve the efficiency of job seekers in finding job opportunities. To this end, for the problem of multilabel text classification of recruitment information, this paper provides two algorithms for multilayer classification based on supported SVM. First, the same learning subclass method is used for text sorting subclass acquisition, and then, the class of the text is determined. Second, the hemispherical support SVM is used to find the smallest hypersphere in the feature space that contains the most text of that class and segment the text of that class from other texts. For the text to be classified, the distance from it to the center of each hypersphere is used to determine the class of the text. Experimental results on recruitment data demonstrate that the algorithm in this paper has a high check-all rate, check-accuracy rate, and F1. And, the relationship between HRM activities and corporate performance is discussed.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

FEATURES OF OPHTHALMIC CARE FOR PATIENTS WITH COMPLICATED CATARACTS

2020 ◽  
pp. 84-89
Author(s):  
Inna Ivanovna Lapkina
Keyword(s):  
Health Care ◽  
Surgical Treatment ◽  
Postoperative Complications ◽  
Key Words ◽  
Outpatient Treatment ◽  
Practical Application ◽  
Unified Approach ◽  
Case Histories ◽  
High Prevalence

Today, around 50 million people worldwide suffer from cataracts, more than a half of them need surgical treatment. High prevalence of this pathology in Ukraine, the need to improve the provision of ophthalmic care to patients, and the reform of the health care system have made the research relevant. Concomitant diseases and special conditions of the eye increase the risk of intra− and postoperative complications, worsen the functional parameters of patients after surgery. In order to develop a unified approach to the treatment of complicated cataracts based on diagnostically related groups of patients, a retrospective analysis of case histories of patients with different variants of complications related to the condition of the lens itself, its ligament apparatus and other structures of the eye was conducted. In each case, the surgeon has to choose the appropriate modification of cataract phacoemulsification surgery. The study proposed the classification of cataract phacoemulsification modifications on the basis of the techniques and the sequence of operation stages, taking into account the classification of the degrees of turbidity of the lens, proposed by L. Buratto. It has been noted that in complicated cases, according to the indications of the patient, surgery may be performed on several modifications of cataract phacoemulsification. The developed classification made it possible to generalize the various variants of pathology and greatly facilitate the choice of tactics of surgical treatment in complicated cataracts. It can be used not only for practical application, but also for improving the qualification of trained professionals. The prospect of further research is to identify contraindications for outpatient treatment of the patients with complicated cataracts. Key words: cataract complication, classification of phacoemulsification modifications, diagnostically related groups.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1411-1418
Author(s):  
Ravi Kauthale
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Support Systems ◽  
The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

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