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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory
In: Environmental science and pollution research: ESPR, Band 30, Heft 12, S. 33780-33794
ISSN: 1614-7499
LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter
The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vaccination from the community. This attituded of pros and cons, which is also called sentiment, can influence people to accept or refuse to be vaccinated. Todays, people express their sentiment in social media in comments, post, or status. One of the methods used to detect sentiment on social media, whether positive or negative, is through a categorisation of text approach. This research provides a deep learning technique for sentiment classification on Twitter that uses Long Short Term Memory (LSTM), for positive, neutral and negative classes. The word2vec word embeddings was used as input, using the pretrained Bahasa Indonesia model from Wikipedia corpus. On the other hand, the topic-based word2vec model was also trained from the Covid-19 vaccination sentiment dataset which collected from Twitter. The data used after balanced is 2564 training data, 778 data validation data, and 400 test data with 1802 neutral data, 1066 negative data, and 566 positive data. The best results from various parameter processes give an F1-Score value of 54% on the test data, with an accuracy of 66%. The result of this research is a model that can classify sentiments with new sentences.
BASE
Prediction of Atmospheric Air Temperature Using Long Short-Term Memory (LSTM) Recurrent Neural Network
In: JASTP-D-20-00144
SSRN
Working paper
Defective egg detection based on deep features and Bidirectional Long-Short-Term-Memory
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 185, S. 106152
Effects of age, gender, and stimulus presentation period on visual short-term memory
In: Journal of women & aging: the multidisciplinary quarterly of psychosocial practice, theory, and research, Band 28, Heft 1, S. 24-33
ISSN: 1540-7322
Ethanol facilitation of short-term memory in adult rats with a disturbed circadian cycle
In: Alcohol and alcoholism: the international journal of the Medical Council on Alcoholism (MCA) and the journal of the European Society for Biomedical Research on Alcoholism (ESBRA), Band 36, Heft 4, S. 292-297
ISSN: 1464-3502
EFFECT OF NALTREXONE ADMINISTRATION ON SHORT-TERM MEMORY IN CHRONICALLY ETHANOL-TREATED OUTBRED RATS
In: Alcohol and alcoholism: the international journal of the Medical Council on Alcoholism (MCA) and the journal of the European Society for Biomedical Research on Alcoholism (ESBRA), Band 39, Heft 1, S. 14-19
ISSN: 1464-3502
Age-Related Differences in Learning and Short-Term-Memory from Childhood to the Senium
In: Human development, Band 11, Heft 1, S. 42-52
ISSN: 1423-0054
Deep Learning with Long Short-Term Memory Networks for Diagnosing Faults in Smart Grids
In: European journal for security research, Band 6, Heft 2, S. 151-169
ISSN: 2365-1695
Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India
In: Air quality, atmosphere and health: an international journal, Band 12, Heft 8, S. 899-908
ISSN: 1873-9326
Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks
The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people's comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.
BASE
An Optimal Control Strategy for Execution of Large Stock Orders Using Long Short-Term Memory Networks
In: Journal of Computational Finance, Band 26, Heft 4
SSRN
Memetic Crow Search Algorithm and Long Short-Term Memory Network Forecasting System for Stock Prices
In: HELIYON-D-22-18315
SSRN
Multimedia vs. Analogue Text: Learning Outcome and the Importance of Short-Term Memory Capacity
In: Arts and Social Sciences Journal: ASSJ, Band 7, Heft 5
ISSN: 2151-6200