scholarly journals Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2481
Author(s):  
Ngoc-Bao-Van Le ◽  
Jun-Ho Huh
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Evaluation Model ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Product Reviews ◽  
Analysis Model ◽  
Data Set ◽  
Research Results ◽  
Customer Reviews

Product reviews become more important in the buying decision-making process of customers. Exploiting and analyzing customer product reviews in sentiments also become an advantage for businesses and researchers in e-commerce platforms. This study proposes a sentiment evaluation model of customer reviews by extracting objects, emotional words for emotional level analysis, using machine learning algorithms. The research object is the Vietnamese language, which has special semantic structures and characteristics. In this research model, emotional dictionaries and sets of extract rules are combined to build a data training data set based on the semantic dependency relationship between words in sentences of the given Vietnamese context. The recurrent neural network model (RNN) solves the emotional analysis issue, specifically, the long short-term memory neural network (LSTMs). This analysis model combines the vector representations of words with a continuous bag-of-words (CBOW) architecture. Our system is designed to crawl realistic data in an e-commerce website and automatically aggregate them. These data will be stored in MongoDB before processing and input into our model on the server. Then, the system can exploit the features in products reviews and classify customer reviews. These features extracted from different feedback on each shopping step and depending on the kinds of products. Finally, there is a web-app to connect to a server and visualize all the research results. Based on the research results, enterprises can follow up their customers in real-time and receive recommendations to understand their customers. From there, they can improve their services and provide sustainable consumer service.

scholarly journals ANALYSIS OF THE LOCATION OF NANNING LARGE-SCALE MALL BASED ON BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 975-979
Author(s):  
H. Huang ◽  
L. L. Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Selection ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Location Analysis ◽  
Analysis Model ◽  
Reference Information ◽  
Neural Network Algorithm

Abstract. Site selection is a key first step in the operation of large-scale shopping malls, and most of the existing site selection methods lack practicality and efficiency. Therefore, it is necessary to carry out a scientific modeling of the site selection problem and provide effective reference information for site selection. With the development of machine learning algorithms, the modeling of such problems becomes more and more simple. In this paper, using matlab software as a tool, based on BP neural network algorithm, Nanning urban area is selected as the research object. After analyzing the influencing factors of location problem, the large-scale mall location analysis modeling is carried out. After repeated training and testing of the training data and the test data, the data for testing the usability is input into the model and applied for analysis. It turns out that the large-scale mall location analysis model is usable and can meet the site selection needs of the mall.

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

scholarly journals Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data

2019 ◽  
Vol 8 (2) ◽  
pp. 5073-5081
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Student Performance ◽  
Activation Function ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fine Tuning ◽  
Academic Excellence ◽  
Data Set

Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.

scholarly journals High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2324 ◽  
Author(s):  
Haiqi Zhang ◽  
Jiahe Cui ◽  
Lihui Feng ◽  
Aiying Yang ◽  
Huichao Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
High Precision ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Set ◽  
Positioning Accuracy ◽  
Localization Accuracy

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

scholarly journals Emotion Analysis Model of MOOC Course Review Based on BiLSTM

2021 ◽  
Vol 16 (08) ◽  
pp. 93
Author(s):  
Shen Ji ◽  
Tan Fangbi
Keyword(s):  
Short Term Memory ◽  
Training Data ◽  
Computer Application ◽  
Online Course ◽  
Analysis Model ◽  
Short Term ◽  
Data Set ◽  
Emotional Evaluation ◽  
Memory Network ◽  
Long Short Term Memory

Online course review can objectively reflect the emotional tendency of learners towards the learning effect. This paper proposes a deep neural network based sentiment analysis model for MOOC course reviews. The model uses Bidirectional Long Short-Term Memory Network (BiLSTM) to analyze Chinese semantic. In order to deal with the imbalance of training data set, this paper introduces two methods to balance it and adds dropout mechanism to prevent the over fitting of the model. The model is then applied to the emotional evaluation of MOOC course of “Fundamentals of College Computer Application”. The application results show that the model has achieved good accuracy and can well realize the emotional orientation analysis of online course reviews so as to provide valuable reference for Course Builders.

scholarly journals Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mohammad Subhi Al-batah ◽  
Mutasem Sh. Alkhasawneh ◽  
Lea Tien Tay ◽  
Umi Kalthum Ngah ◽  
Habibah Hj Lateh ◽  
...  
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Multilayer Perceptron ◽  
Gradient Descent ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Operating Characteristics ◽  
Data Set ◽  
Levenberg Marquardt

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

scholarly journals Airfoil buffet aerodynamics at plunge and pitch excitation based on long short-term memory neural network prediction

2021 ◽  
Author(s):  
R. Zahn ◽  
C. Breitsamter
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Training Data ◽  
Good Prediction ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Identification Approach ◽  
Long Short Term Memory ◽  
Transonic Buffet

AbstractIn the present study, a nonlinear system identification approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. The identification approach is applied as a reduced-order modeling (ROM) technique for an efficient computation of time-varying integral quantities such as aerodynamic force and moment coefficients. Therefore, the nonlinear identification procedure as well as the generalization of the ROM are presented. The training data set for the LSTM–ROM is provided by performing forced-motion unsteady Reynolds-averaged Navier–Stokes simulations. Subsequent to the training process, the ROM is applied for the computation of the aerodynamic integral quantities associated with transonic buffet. The performance of the trained ROM is demonstrated by computing the aerodynamic loads of the NACA0012 airfoil investigated at transonic freestream conditions. In contrast to previous studies considering only a pitching excitation, both the pitch and plunge degrees of freedom of the airfoil are individually and simultaneously excited by means of an user-defined training signal. Therefore, strong nonlinear effects are considered for the training of the ROM. By comparing the results with a full-order computational fluid dynamics solution, a good prediction capability of the presented ROM method is indicated. However, compared to the results of previous studies including only the airfoil pitching excitation, a slightly reduced prediction performance is shown.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods

2021 ◽  
Vol 62 (4) ◽  
pp. 393-406
Author(s):  
Yanxiang Yu ◽  
◽  
Chicheng Xu ◽  
Siddharth Misra ◽  
Weichang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Short Term Memory ◽  
Rock Physics ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Blind Test ◽  
Data Set ◽  
Benchmark Model ◽  
Sonic Log

Compressional and shear sonic traveltime logs (DTC and DTS, respectively) are crucial for subsurface characterization and seismic-well tie. However, these two logs are often missing or incomplete in many oil and gas wells. Therefore, many petrophysical and geophysical workflows include sonic log synthetization or pseudo-log generation based on multivariate regression or rock physics relations. Started on March 1, 2020, and concluded on May 7, 2020, the SPWLA PDDA SIG hosted a contest aiming to predict the DTC and DTS logs from seven “easy-to-acquire” conventional logs using machine-learning methods (GitHub, 2020). In the contest, a total number of 20,525 data points with half-foot resolution from three wells was collected to train regression models using machine-learning techniques. Each data point had seven features, consisting of the conventional “easy-to-acquire” logs: caliper, neutron porosity, gamma ray (GR), deep resistivity, medium resistivity, photoelectric factor, and bulk density, respectively, as well as two sonic logs (DTC and DTS) as the target. The separate data set of 11,089 samples from a fourth well was then used as the blind test data set. The prediction performance of the model was evaluated using root mean square error (RMSE) as the metric, shown in the equation below: RMSE=sqrt(1/2*1/m* [∑_(i=1)^m▒〖(〖DTC〗_pred^i-〖DTC〗_true^i)〗^2 + 〖(〖DTS〗_pred^i-〖DTS〗_true^i)〗^2 ] In the benchmark model, (Yu et al., 2020), we used a Random Forest regressor and conducted minimal preprocessing to the training data set; an RMSE score of 17.93 was achieved on the test data set. The top five models from the contest, on average, beat the performance of our benchmark model by 27% in the RMSE score. In the paper, we will review these five solutions, including preprocess techniques and different machine-learning models, including neural network, long short-term memory (LSTM), and ensemble trees. We found that data cleaning and clustering were critical for improving the performance in all models.

Crack-Depth Determination by a Neural Network with a Synthetic Training Data Set

1993 ◽  
pp. 803-810 ◽  
Author(s):  
M. Takadoya ◽  
M. Notake ◽  
M. Kitahara ◽  
J. D. Achenbach ◽  
Q. C. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Depth ◽  
Training Data ◽  
Data Set ◽  
Depth Determination ◽  
Synthetic Training Data
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