Attention Mechanism with Gated Recurrent Unit Using Convolutional Neural Network for Aspect Level Opinion Mining

As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy.

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Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

Shock and Vibration ◽

10.1155/2021/6660243 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiaochen Zhang ◽

Yiwen Cong ◽

Zhe Yuan ◽

Tian Zhang ◽

Xiaotian Bai

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Fault Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

Rolling Bearing ◽

Attention Mechanism ◽

Early Fault Detection ◽

Gated Recurrent Unit ◽

Unit Network

Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN-AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.

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Convolutional Neural Network for Customer’s Opinion on Amazon Products

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5670.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6634-6643 ◽

Cited By ~ 1

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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Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

IEEE Access ◽

10.1109/access.2021.3052937 ◽

2021 ◽

Vol 9 ◽

pp. 15561-15569

Author(s):

Narisa Zhao ◽

Huan Gao ◽

Xin Wen ◽

Hui Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

Gated Recurrent Unit

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Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remote Sensing ◽

10.3390/rs13040554 ◽

2021 ◽

Vol 13 (4) ◽

pp. 554

Author(s):

A. A. Masrur Ahmed ◽

Ravinesh C Deo ◽

Nawin Raj ◽

Afshin Ghahramani ◽

Qi Feng ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Deep Learning ◽

Convolutional Neural Network ◽

Climate Index ◽

Climate Indices ◽

High Temporal Resolution ◽

Synoptic Scale ◽

Time Step ◽

Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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