Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction

Nurhaida, Ida; Noprisson, Handrie; Ayumi, Vina; Wei, Hong; Putra, Erwin Dwika; Utami, Marissa; Setiawan, Hadiguna

doi:10.3991/ijim.v14i18.16867

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…LSTM network is a special version of the recurrent neural network. It has been designed to overcome the problem of vanishing /exploding gradient [35] that occurs in RNN [50], and it has the ability to learn better long-term dependencies [36].…”

Section: Long Short Term Memory Network (Lstm)mentioning

confidence: 99%

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Al-Khazaleh

Alian

Biltawi³

et al. 2023

Int. J. Onl. Eng.

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The emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%.

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Section: Long Short Term Memory Network (Lstm)mentioning

confidence: 99%

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Al-Khazaleh

Alian

Biltawi³

et al. 2023

Int. J. Onl. Eng.

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“…The developed model's effectiveness was measured by the four effectiveness evaluation methods: the precision, the recall, the F-measure, and the accuracy [14][15][16] [17]. Here are the equations that represented the tests of the model's effectiveness.…”

Section: Effectiveness Evaluation Of the Modelmentioning

confidence: 99%

Feature Selection for Analyzing Data Errors Toward Development of Household Big Data at the Sub-District Level Using Multi-Layer Perceptron Neural Network

Nuanmeesri

2022

Int. J. Interact. Mob. Technol.

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This research aims to analyze the patterns of data errors in order to fulfill the data required for household big data development at the sub-district level in Thailand. Feature Selection and Multi-Layer Perceptron Neural Network were applied, while the data imbalance was solved by the SMOTE method and the comparison between the CFS feature selection method and Information Gain (IG) feature selection method. Afterward, the datasets were classified the data errors by the Multi-Layer Perceptron Neural Network. Each model’s effectiveness was measured by the 10-fold cross-validation method. The research results revealed that the suitable data size after being adjusted data imbalanced was 400%. Once the data had been processed for developing the model, it was found that after being adjusted data size towards the application of the SMOTE, CFS feature selection technique, and classified data errors by the Multi-Layer Perceptron Neural Network, the model provided the highest level of effectiveness in data errors classification with an accuracy of 98.29 %. Moreover, the application could effectively classify data errors and display the household big data at the highest level. The application evaluation results given by the experts and the users had an average mean of 4.69 and higher, a standard deviation of 0.47 and lower, which has the level of effectiveness of 93.78% and higher, while interquartile range values not over 1, a quartile deviation of no more than 0.5.

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“…In supervised machine learning [12][13][14] [15], Naïve Bayes is a widely used model of classification due to its simplicity and efficiency. Naïve Bayes computes the posteriori probability for a class by observing the churn problem, i.e., the churn and non-churn observation probabilities.…”

Section: A Naïve Bayesmentioning

confidence: 99%

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

Priambodo¹,

Jumaryadi²,

Rahayu³

et al. 2022

IJACSA

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Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naïve Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

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Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction

Cited by 10 publications

References 23 publications

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Feature Selection for Analyzing Data Errors Toward Development of Household Big Data at the Sub-District Level Using Multi-Layer Perceptron Neural Network

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

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