A Novel Classification Model SA-MPCNN for Power Equipment Defect Text

2021 ◽  
Vol 20 (6) ◽  
pp. 1-21
Author(s):  
Xiuxia Tian ◽  
Can Li ◽  
Bo Zhao
Keyword(s):  
Text Classification ◽  
Health Condition ◽  
Classification Model ◽  
Semantic Features ◽  
Power Equipment ◽  
Condition Evaluation ◽  
Maintenance Decisions ◽  
Text Features ◽  
Gated Recurrent Unit

The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

Research on Automatic Text Classification Algorithm Based on ITF-IDF and KNN

2015 ◽  
Vol 713-715 ◽  
pp. 1830-1834
Author(s):  
Rong Chen ◽  
Feng Chen ◽  
Yi Sun
Keyword(s):  
Text Classification ◽  
Information Filtering ◽  
Classification Model ◽  
Related Information ◽  
Text Feature ◽  
Extraction Algorithm ◽  
Automatic Text Classification ◽  
Text Features ◽  
Automatic Text ◽  
Better Than

We consider how to efficiently text classification on all pairs of documents. This information can be used to information retrieval, digital library, information filtering, and search engine, among others. This paper describes text classification model which based on KNN algorithm. The text feature extraction algorithm, TF-IDF, can loss related information between text features, an improved ITF-IDF algorithm has been presented in order to overcome it. Our experiments show that our algorithm is better than others.

scholarly journals Text classification based on gated recurrent unit combines with support vector machine

2020 ◽  
Vol 10 (4) ◽  
pp. 3734
Author(s):  
Muhammad Zulqarnain ◽  
Rozaida Ghazali ◽  
Yana Mazwin Mohmad Hassim ◽  
Muhammad Rehan
Keyword(s):  
Support Vector Machine ◽  
Language Processing ◽  
Text Classification ◽  
Classification Model ◽  
Support Vector ◽  
Text Data ◽  
Intelligent Technique ◽  
Learning Tasks ◽  
The One ◽  
Gated Recurrent Unit

As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.

scholarly journals A Classification Model of Legal Consulting Questions Based on Multi-Attention Prototypical Networks

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jianzhou Feng ◽  
Jinman Cui ◽  
Qikai Wei ◽  
Zhengji Zhou ◽  
Yuxiong Wang
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Text Classification ◽  
Question Answering ◽  
Training Data ◽  
Classification Model ◽  
Great Progress ◽  
Public Datasets ◽  
The Cost

AbstractText classification is a research hotspot in the field of natural language processing. Existing text classification models based on supervised learning, especially deep learning models, have made great progress on public datasets. But most of these methods rely on a large amount of training data, and these datasets coverage is limited. In the legal intelligent question-answering system, accurate classification of legal consulting questions is a necessary prerequisite for the realization of intelligent question answering. However, due to lack of sufficient annotation data and the cost of labeling is high, which lead to the poor effect of traditional supervised learning methods under sparse labeling. In response to the above problems, we construct a few-shot legal consulting questions dataset, and propose a prototypical networks model based on multi-attention. For the same category of instances, this model first highlights the key features in the instances as much as possible through instance-dimension level attention. Then it realizes the classification of legal consulting questions by prototypical networks. Experimental results show that our model achieves state-of-the-art results compared with baseline models. The code and dataset are released on https://github.com/cjm0824/MAPN.

scholarly journals A Deep Learning-Based Text Classification of Adverse Nursing Events

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenjing Lu ◽  
Wei Jiang ◽  
Na Zhang ◽  
Feng Xue
Keyword(s):  
Deep Learning ◽  
Health System ◽  
Medical Care ◽  
Text Classification ◽  
Rapid Development ◽  
Classification Model ◽  
Diagnosis And Treatment ◽  
Event Analysis ◽  
Classification Methods

Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient’s diagnosis and treatment results and even increase the patient’s pain and burden. Additionally, It is high likely to cause accidents and disputes and affect normal medical work and personnel safety and is not conducive to the development of the health system. Due to the rapid development of modern medicine, health and safety of patients have become the most concerned issue in society and patient safety is an important part of medical care management. Research and events have shown that classified management of adverse nursing events, event analysis, and improvement measures are beneficial, specifically to the health system, to continuously improve the quality of medical care and reduce the occurrence of adverse nursing events. In the management of adverse nursing events, it is very important to categorize the text reports of adverse nursing events and divide these into different categories and levels. Traditional reports of adverse nursing events are mostly unstructured and simple data, often relying on manual classification, which is difficult to analyze. Furthermore, data is relatively inaccurate and practical reference significance is not obvious. In this paper, we have extensively evaluated various deep learning-based classification methods which are specifically designed for the healthcare systems. It becomes possible with the development of science and technology; text classification methods based on deep learning are gradually entering people’s field of vision. Additionally, we have proposed a text classification model for adverse nursing events in the health system. Experiments and data comparison test of both the proposed deep learning-based method and existing methods in the text classification of nursing adverse events effect are performed. These results show the exceptional performance of the proposed mechanism in terms of various evaluation metrics.

scholarly journals Convolutional neural network-based model for web-based text classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5785
Author(s):  
Satyabrata Aich ◽  
Sabyasachi Chakraborty ◽  
Hee-Cheol Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Learning Models ◽  
Real World Problem ◽  
Traditional Approaches ◽  
The Web

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different topics at different levels, which may adversely affect the accuracy of traditional approaches. With the invention of deep learning models, which already have the capability of providing good accuracy in the field of image processing and speech recognition, the problems inherent in the traditional text classification model can be overcome. Currently, there are several deep learning models such as a convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory that are widely used for various text-related tasks; however, among them, the CNN model is popular because it is simple to use and has high accuracy for text classification. In this study, classification of random texts on the web into categories is attempted using a CNN-based model by changing the hyperparameters and sequence of text vectors. We attempt to tune every hyperparameter that is unique for the classification task along with the sequences of word vectors to obtain the desired accuracy; the accuracy is found to be in the range of 85–92%. This model can be considered as a reliable model and applied to solve real-world problem or extract useful information for various text mining applications.</p></td></tr></tbody></table>

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.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

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